in our case the configurations are the dags generated by the grammar i.e. f l g
now we must not only choose values for weights we must also choose the features that weights are to be associated with
the old field models these probabilities exactly correctly so making the distinction does not permit us to improve on the old field
intuitively we choose the weight such that the expectation of f under the resulting new field is equal to its empirical expectation
the context sensitive constraints introduced by the n gram model are reflected in re entrancies in the structure of statistical dependencies as in figure NUM
random fields are a particular class of multidimensional random processes that is processes corresponding to probability distributions over an arbitrary graph
where f x y if x is the total number of features of dag x
an advantage of scfgs that random fields lack is the transparent relationship between an scfg defining a distribution q and a sampler for q
this method is compared empirically with the x NUM method in some detail in section NUM below
to link the english word forms in the input table to the thesaurus in order to extract a partial thesaurus we used the japanese translations for the english word forms
qjp analyzes a japanese sentence into segmented morphemes words with tags and a syntactic bunsetsu kakari uke structure based on the two strategies a morphological analysis based on character types and functional words and b syntactic analysis by simple treatment of structural ambiguities and ignoring semantic information
NUM texts contained in the evaluation data ranged in length from NUM to NUM sentences
brown et al use a probabilistic measure for estimating word similarity of two languages in their statistical approach of language translation brown NUM
table shows the numbers of distinct content words those of two or more occurrences and the numbers of word sequences of two or more occurrences
kupiec uses np recognizer for both english and french and proposed a method to calculate the probabilities of correspondences using an iterative algorithm like the em algorithm
one such example is that p type silicon frequently collocates with n type silicon making the correspondence uncertain
this paper proposes a method to construct a translation dictionary that consists of not only word pairs but pairs of arbitrary length word sequences of the two languages
it would be natural to set a maximum length for the candidate word sequences which we really have it be between NUM and NUM in the experiments
such translation pairs are taken away from the co occurrence data then only the remaining word sequences need be taken into consideration in the subsequent iterative steps
then word sequences of length two that are headed by a previously extracted word are extracted provided they appear at least twice in the corpus
car designers do n t reinvent the wheel each time they plan a new model but software engineers often find themselves repetitively producing roughly the same piece of software in slightly ditfenmt r rm
our view is that succesful algorithmic reuse in nle will require the provision of support software for nle in the form of a general architecture and development environment which is specifically designed for text processing systems
all communication between the components of an le system goes through gdm insulating parts fi om each other and providing a uniform api applications programmer interface for manipulating the data produced by the system
gate is also intended to benefit the le system developer which may be the le researcher with a different hat on or industrialists implementing systems for sale or for their own text processing needs
in some respects these probleins are insoluble without general changes in the way nle research is done researchers will always be reluctant to use poorly documented or unreliable systems as part of their work for exmnple
this set is called vie a vanilla ie system
this method appears to have produced a dramatic improvement in the performance of two different statistical search engines that we tested cornell s smart and nist s prise boosting the average precision by at least NUM
our early experiments with multi stream indexing using smart suggested that the most effective weighting of this stream is lnc ltc which yields the best average preci table NUM shows relative performance of each stream tested for this evaluation
the statistical weighting formulas based on terms distribution within the database such as tf idf are far from optimal and the assumptions of term independence which are routinely made are false in most cases
several researchers have noticed in the past that different systems may have similar performance but retrieve different documents thus suggesting that they may NUM smart version NUM is freely available unlike the more advanced version NUM
to make this process efficient we first perform a search with the original unexpanded queries short queries and then use top n i NUM NUM returned documents for query expansion
an example trec NUM query is show below note that the description field is what one may reasonably expect to be an initial search query while narrative provides some further explanation of what relevant material may look like
NUM NUM NUM compiled queries are the forms of the detection criteria generated by the detection capability from the selection statement and used for document selection
the architecture shall support template object and slot level language and code set identification as necessary either in the template schema NUM NUM NUM or in individual filled templates
the user information request area accepts for tipster processes inputs from the user interface and the user information outputs that sends tipster results to the user interface for display
the interface control document icd shall unambiguously define the architecture component and module interfaces so that these parts of the architecture may work together to produce the intended results
when templates objects are retrieved the fill rules associated with each slot shall also be retrieved to assist in understanding existing templates constructing new templates or modifying the fill criteria
the user shall be able to modify this component version query directly and shall be able to create new detection criteria in this component s format if he learns the format
NUM NUM NUM a type of annotation is required to allow the user to make notes about a document other annotations or the detection and extraction processing
application is a group of components and modules both internal and external to the tipster architecture that operates on documents to answer a user s request for information from documents
note NUM a key concept of the architecture is that any implementation of a tipster architecture requirement shall be fully compatible with the tipster requirements that have not been implemented
note the use of the term component in the architecture design document is in a different context namely part of the structure of a generic object
an ltag along with the operations of substitution and adjnnction also has tile implicit operation of lexical insertion represented as the diamond mark in fig NUM
one of the trees is always the tree obtained by specializing the schema in fig NUM for a particular category NUM
the coordination of o cats lcb NUM l rcb with a copy of itself and the subsequent derivation tree is depicted in fig NUM
the derived structures in figs NUM and NUM are diff cult to reconcile with traditional notions of phrase structure 1deg
application of the operation on the np node at address NUM NUM gives us a tree with the contraction set lcb NUM NUM rcb
the process of contraction ensures that the anchor is placed into a pair of ltag tree templates with a single lexical insertion
eats cookies drinks beer dgl ivation itrtlt tufc derivezl slitlcttlre figure NUM derivation for chapman eats cookies
however the derivation structure gives us all the information about dependency degwe shall use the general notation derivation structure to refer to both derivation trees and derivation graphs
a history of these operations on elementary trees in the form of a derivation tree can be used to reconstruct the derivation of a string recognized by a ltag
specific mutual information is a good measure of independence which it was designed to measure but good measures of independence are not necessarily good measures of similarity
generally the broader the classification granularity we chose the more confident we can be about the distribution of classes at that level but the less information this distribution offers us about next word prediction
in estimating the probability of the ith word in a word stream the model considers all previous word contexts to be identical if and only if they share the same final n NUM words
the original sentence is not the only speech utterance that could give rise to the observed phoneme string for example the meaningless and ungrammatical sentence the buoy seat this and which is
for example if the system is searching for the frequency of a particular noun in an attempt to find the most likely next word then alternative words should already be nearby in the n gram database
neither the hughes system nor the finch system are ever applied to language models also the details of the brown language model are insufficient for us to rebuild it and run our sentences through it
where there is just one degree of freedom yates correction is applied
due to the references all morphological and syntactic semantic information as well as the canonical forms of the english equivalents of the found word are achieved
the translation algorithm searches for the constituent verb of a clause consults the information extracted from the dictionary and first checks for the constructions admissible for the verb
this approach enables the analysis of the polish input expression the choice of the appropriate english verb equivalent and the synthesis of the correct english output
storing the dictionary as an sgml type document aims at comfortable browsing of its contents as well as facilitating its use in an application other than the poleng translation algorithm
whenever a finite state is reached references to the table of morphological features and the table of canonical forms are obtained
however the sequence of words between the subject and the predicate in a sentence is subject to a number of constraints and is therefore amenable to deterministic parsing
the inflected forms are generated on the basis of polish inflection codes attached to all entries in the dictionary of canonical forms
each entry in the dictionary is supplied with inflection codes of its polish and english parts as well as other syntactic semantic information
since most of the nlp components use linguistic representation that may widely differ a single internal representation is used e.g. for encoding part of speech morphological features etc
this expetience with other languages has provided significant insights for the development of a versatile gbmt engine and for the use of off theshelf components for building a complete machine translation system
source documents and their translations are managed using the tipster document manager developed at crl which is also used as the architectural basis for integrating the system s components
finally the resulting fully instantiated tree structure is processed to produce a target tipster document which contains altemative translations tagging and morphological information and constituent information stored as tipster annotations
the core components of the glossary based engine are the bilingual dictionaries and the bilingual glossaries which can easily include entries based on translators own notes using a mulfilingual lexical database editor
the project has produced a working multillngual translator s workstation prototype with complete machine translation functions for spanish arabic and japanese to english and sc ne russian morphological analysis
it has also resulted in the development of a language and tool integration methodology that facilitates the process of developing a new machine translation system and integrating it in a translator s working envkonment
figure NUM the result after first pass
if the head noun is not derived from say a verb the single genitive ill either position is interpreted as a possessor
the merit of a decision tree method is that it provides a generic framework in which to combine knowledge from multiple sources a property necessary for automatic abstracting where information from a single source alone often fails to determine which sentence to extract
si measures the proportion of pairwise agreements among the raters on i NUM category assignments for a particular object i si gives a measurement of agreement among raters on decisions regarding which category a given object i is to be assigned to
despite some fluctuations of the iigures the results exhibit clear patterns figure NUM the kappa coefllcient is strongly correlated with performance for texts of editorial type and of news report type but correlation for column type texts is only marginal
for s me r st ricd ns on t lmir lisl rilmlion wiljml mrman tml
c structure proper jes of a la nguage g discon inuous cons ituenl s
in a decision tree approach the task of extracting summary sentences is treated as a two way classification task where a sentence is assigned to either yes or no category depending on its likelihood of being a summary sentence
since for now we do not have a good third string algorithm to call when both trigrams and bayes fall by the wayside we content ourselves with the guess made by bayes in such situations
in this section we calibrate this overall performance by comparing tribayes with microsoft word version NUM NUM a widely used word processing system whose grammar checker represents the state of the art in commercial context sensitive spelling correction
the results of word and tribayes for the NUM confusion sets appear in table NUM six of the confusion sets marked with asterisks in the table are not handled by word word s scores in these cases are NUM for the correct condition and NUM for the corrupted condition which are the scores one gets by never making a suggestion
the test sets for the incorrect condition were generated by corrupting the correct test sets in particular each correct occurrence of a word in the confusion set was replaced in turn with each other word in the confusion set yielding n NUM incorrect occurrences for each correct occurrence where n is the size of the confusion set
their there they re than then its it s your you re begin being passed past quiet quite weather whether accept except lead led cite sight site principal principle raise rise affect effect peace piece country county amount number among between
this is somewhat of a handicap in the comparison as word can achieve higher scores in the correct condition by suppressing its weaker suggestions albeit at the cost of lowering its scores in the corrupted condition
the previous sections demonstrated the complementarity between trigrams and bayes trigrams works best when the words in the confusion set do not all have the same part of speech while bayes works best when they do
NUM we calculate p w using the tag sequence of w as an intermediate quantity and summing over all possible tag sequences the probability of the sentence with that tagging that is
in the different tags condition trigrams generally does well outscoring bayes for all but NUM confusion sets and in each of these cases making no more than NUM errors more than bayes
because this method will get quickly out of hand as additional biases are included or parameters tested future work should investigate less costly alternatives to linguistic bias selection
NUM then incorporate biases that modify feature weights by adding the weight vectors proposed by each bias e.g. recency weighting subject accessibility bias
unfortunately this means that most of the features in a normalized case will be one of these missing features
the right to left labeling appears to provide a representation of the local context of the relative pronoun that is critical for finding antecedents
next the sentence analyzer processes the training sentences and creates a training case every time an instance of the ambiguity occurs
whenever an edge e is inserted into the chart if e is active then for all passive a such that a from NUM e to and combined seore e a beam value insert e a combined score e a into the actual agenda
lex key e from va e to lcb rcb and for x acoustic e inside x e outside x lcb i score rcb for x prosody and bigram e inside x e outside x o for x grammar e inside x e outside x
edges correspond to word or phrase hypotheses being partial in the case of ac null tive edges
the two operations combine and seek down are similar to the well known earley algorithm operations completer arm predictor
the ct notation for l asic operations are given in simplicity
the grammar scores have only indirect influence on the string their main function is picking the right tree
the prosody module developed at the university of bonn classifies time intervals according to these classes
however bod then neglects analysis of this e term assuming that it is constant
by modifying the algorithm slightly to record the actual split used at each node we can recover the best parse
however while the inside outside algorithm is a grammar re estimation algorithm the algorithm presented here is just a parsing algorithm
this paper contains the first published replication of the full dop model i.e. using a parser which sums over derivations
let a represent the number of subtrees headed by nodes with non terminal a that is a j aj
a linear increase in ambiguity will lead to an exponential decrease in probability of the most probable parse
whereas bod reported NUM exact match we got only NUM using the less restrictive zero crossing brackets criterion
the diff files sum to NUM bytes versus NUM NUM bytes for the original files or less than NUM NUM
the entry maxc NUM n contains the expected number of correct constituents given the model
in addition we only need to develop a lexicon limited to the words and senses in the original text as long as we use the same words in the same context as the original text
norman rosenblatt dean of the college of criminal justice at northeastern university agrees
while doing the centering analysis of my sample texts i noticed that it is the segment boundaries the shifts that are important for summarization in the discourse anal2there are other cues to discourse segmentation not yet included in this study such as tense and aspect continuity and the use of cue words such as and
those on early release must check in with corrections officials fifty times a week according to ash who says about half the contacts for a select group will now be made by the computerized phone calls
we need to look for shared elements agents propositions etc in the semantic representation of the original text in order to aggregate similar elements as well as to recognize important elements that the author restates many times
aa next week some inmates it released early from the hampton county jail in springfield will be wearing a wristband that t hooks up with a special jack on their home phones
the first and third sentences are the first sentences in the segments about the most frequent cb the inmates the second sentence as well as part of the first sentence is given by recognizing restatements in the text
a the description conjures up images of big brother watching a but jay ash deputy superintendent of the hampton county jail in springfield says the surveillance system is not that sinister
a springfield jail deputy superintendent ash says although it will allow some prisoners to be released a few months before their sentences are up concerns that may raise about public safety are not well founded
our NUM bit structural tag representation allows us to build an interpolated bigram model containing NUM levels of bigram class information
e maih lcb j mcmahon fj smith qub ac uk NUM association for computational linguistics computational linguistics volume NUM number NUM in a test set by considering the n NUM most recent words wi n l wi n wi l or i NUM wi l in a more compact notation
a second weakness of word based language models is their unnecessary fragmentation of contexts the familiar sparse data problem
classifications which when incorporated into the models lower the test set perplexity are judged to be useful
the same holds for the reverse procedure when a pair of graphemes is pronounced as either a single phoneme or a pair of phonemes
the rules are incorporated in the ptgc system using an automated procedure as a separate input function that parses the input strings into states
to implement the above algorithm in ptgc some decisions had to be made about the states observation symbols and transition probabilities
for the name corpus table NUM n1 shows experiments using a corpus of surnames and od experiments using a corpus of street names
the second order hmm can be translated into a first order hmm with an extended state space in which state pairs are used as single states
figure NUM illustrates the difference between the performance of exp NUM and exp NUM order of model in the first output position
the probabilities are first calculated using the greatest available floating point representation which is NUM bits for a long double floating point number
it can easily be seen that if a b then loga p logb p
in figures NUM to NUM a summary for each type of experiment is shown in order to compare the performance between the languages
an ontology a world model containing ilfformation about types of things events mid properties in the world is a necessary prerequisite for a tmr language
instead the meaning representation of good introduces an attitude on the part of the speaker with regard to the modified noun
NUM i former roommate ii early riser iii occasional visitor iv eventual compromise
NUM modified modifiers verb adverb noun prepositional phrase noun adjective prepositional phra adjective adverb prepositional phrase
our microtheory associates its meaning with a region on a scale which is defined as the range of ml ontological property cf
this method has been tested in the mikrokosmos semantic analyzer based on the iexical entries for NUM NUM spanish mid NUM NUM english adjectives
the purpose and result of the mikrokosmos mmlysis process is the derivation of an interlingnal representation for natm al language inlmts
meaning of a typical adjective a simple l rototypical case of adjectival modification is a scalar adjective which modifies a noun both syntactically and semantically
at runtime strings being analyzed are simply matched along paths on the bottom side of the lexical transducer and the solution strings are read off of the matching top side
other languages t ecause the ambiguity of the surface words forces many dead end analysis paths to be explored and because more valid solutions have to be found and returned
another transducer is also composed on top of the lexicon fst to map various rule triggering features no longer needed into epsilon and to enforce various long distance morphotactic restrictions
the arabic system runs in exactly the same way using the same runtime code a the lcxical transducers for other languages like english french and spanish
as the time necessary to move a rule transduccr to a new state is usually independent of its size moving NUM transducers at runtimc cat be NUM times slower than moving a single intersected transducer
all intermediate levels disappear in the compositions and one is left with a single two level lexical transducer that contains surface strings on tim bottom and lexical strings including roots and tags on the top
the result is an arabic finite state lexical transducer that is applied with the same runtime code used for english french german spanish portuguese dutch and italian lexical tran ducers
the other chal null lenges of arabic morphological w riation and orthography including varying amounts of diacritical marking all succmnbed to rather complex but conq letely traditional two level rules
the arabic rules have now been modilied to work in two steps lirst to generate the fully voweled form and then to generate the various partially roweled forms and the unvoweled form
both theories agree that a discourse is structured into a hierarchy of non overlapping constituents segments in g s and spans in rst
to simplify our discussion however we assume the core of a segment is an utterance not embedded in any subsegment
it should be clear that the theory independent notion of ils as it was characterized above is exactly the linguistic structure in g s
in rst because the underlying intentions are not analyzed explicitly the distinction between necessary and artifactual order is not available
by synthesizing rst and g s work done using both approaches can be applied to accomplishing these tasks during interpretation and generation
there are distinctions among the rst intentional relations that in g s would be subtypes of the dominance relation among intentions
this span is then the nucleus of a higher span in which the satellite is an additional g s subsegment from the same segment
alternatively the g s core and an adjacent subsegment may be analyzed as an rst nucleus and satellite forming an rst span
in these cases of multiple subsegments the rst structure will depend on whether the rst relations are the same or different
an embedded segment in g s will be analyzed as a satellite in rst and the segment core will be the nucleus
this means that the proofread translation result has the same character string as the translation result with substituted nouns or adjectives for the variables
to implement this technique a three stage approach is adopted to the gradual refinement module preference based pruning syntactic based pruning and semantic based pruning
les enfants the children adjectival adverbial and prepositional phrases are treated in a similar fashion in both grammars
outside x optimistic estimates for the portion fi om vertex NUM to the beginning of an edge
furthermore there are two operations to insert new word hypotheses insert and inherit
the ug used in our experiments consists of NUM lexical entries and NUM rules
beam value is calculated as a fixed offset from the maximum combined score on an agenda
due to the high optimization level of the sequential parser load balancing is famy poor
we used a variant of inside outside training to estimate a model of ug derivations
the grammar is a probabilistic typed ug with separate rules for pauses and other spontanous speech phelnonmua
for effective parallelization it is crucial to keep communication between processors to a minimum
acoustic i i score score to e outside acoustic
the average size of ambiguous collision sets is about NUM in both corpora
it gives a measure of the global trend of the statistical decision model
we do not believe that experimenting over different domains would give different results
swhereas for more independent phenomena NUM should emphasize the right attachments
the algorithm will try to obtain as many paradise esl s i.e.
where n is the total number of collision sets found in the corpus
the experiments also stress the importance of class based models of lexical learning
decisions are deferred until enough evidence has been gained of a noisy phenomenon
the problem is clearly due to the highly repetitive ambiguities
learning phase to filter out the syntactically odd esl s i.e.
trigger backward find all the words in the dictionary that use the trigger word in their definition and join their respective temporary graphs to the cckg
note that constraining the number of semantically significant words is important in limiting the exploration process tbr constructing the concept cluster as we shall soon see
only NUM of the words occur more than NUM times NUM occur more than NUM times but over NUM occur less than NUM times
the concept hierarchy can be useful in many cases but it is generated from the dictionary and might not be complete enough to find all similar concepts
the resulting cluster is lcb letter message address mail post office stamp send package card note rcb
the concept chlstcr in itself is interesting for tasks such as word disambiguation but the c k will give more to that cluster
our source of knowledge is the americ m iteritage first i ictionary t which contains NUM entries aml is designed for children of age six to eight
maximal common subgraph the maximal common subgraph between two graphs consists of finding a subgraph of tile first graph that is isomorphic to a subgraph of the seeond graph
we set this threshold empirically the maximal common subgraph between the cckg and the new temporary graph must contain at least three concepts connected through two relations
the structure of tile np and that of the vp in the cgf differ from those in the anlt grammar
although this word length model is very simple it plays a key role in making the word segmentation algorithm robust
it is formally defined as the joint probability of the character sequence cl ck if wi is the unknown word
second especially in sublanguages syntactic noise seems to be a systematic phenomenon because many ambiguities occur within identical phrases
in the top line an identifying label is providecl l aa 7etx lm o o where NUM short for object specifies the syntactic relation of the nom relatiw to the verb
whcu l he sysleiil is otll roiil ed wii h tll kliowu coliil ina l iol s o words or well wii l illll llovcll words i
NUM give l ll l lllill le l l is i ypi ally oc tlrs wil ll verl s ililcicrgoiltg he a usa l ivo in hoa l
information can be associated with nouns and or w rbs as shown in NUM NUM li 2g l NUM hc lli3i o o i ea book o inherent feettules telzttionm featmes
all three will be important for nlp based call
for a rule to be learned or even to be useful in explanation it should be congruent with learners cognition and expressed in a way that is meaningful to them
on the contrary i strongly believe that valuable itss will be built and that they will use nlp or i would not spend so much of my time on one
in general a slight amotmt of precision can apparently be expended to gain a substantial increase in applicability
class base roles obviously have much less parameters are easier to acquire and can be applied more broadly
in this paper we propose a word alignment algorithm based on classes derived from sense related categories in existing thesauri
the smt approach can be understood as a word by word model consisting of two submodels a language model for generating a source text segment st and a translation model for translating st to a target text segment tt
our algorithm fbr word aligmnent is a decision procedure tbr selecting the preferred connection fiom a list of candidates
the thesauri provide classification that can be utilized to generalize the empirical knowledge gleaned fi om corpora sensealign achieves a degree of generality since a word pair can be accurately aligned even when they occur rarely or only once ill the corpus
the outside test are NUM sentence pairs fiom a book on english sentence patterns containing a comprehensive fifty five sets of typical sentence patterns l lowever the words in this outside test is somewhat more common and thereby easier to align
model NUM assumes that pr st tt depends only on lexical translation probability t s i t i.e. the probability of the i th word in st producing the j th word t in tt as its translation
the comparison will be described in the full paper and the results will be incorporated into a submission for the trec NUM routing systems NUM
we have developed a technique that categorizes document images based on their content
in section NUM we outline the automated categorization system which we developed
it would be of use for other information retrieval applications such as word spotting
as for the ocr based approach read word shape token as word
finally we identify the presence of a deep eastward southward concavity
this mffavorable result was mainly caused by the lack of character segmentation function
this means one word shape token mapped to NUM NUM words on average
in section NUM we discuss the experimental results and future work
we use the relative frequency between NUM and NUM as the weight
stage NUM the system removes word shape tokens corresponding to stop words
all the real work of analyzing texts and maybe producing summaries of them or translations or sql statements etc in a gate based le system is done by creole modules
alternatively module sets can be assembled to make e.g.
the integration of detection and extraction a range of experiments were performed here to integrate extraction capabilities into the smart retrieval system
the cable abstracting and indexing system program is aimed at providing assistance to government analysts in indexing and abstracting their cable traffic
model merging vs bigrams the first experiment compares model merging with a standard bigram model
the derived models assign lower perplexities to test data than the standard bigram model derived from the same training corpus
table NUM features generated from h4 for tagging well heeled from table NUM
note that the perplexity changes very slowly for the largest part and then changes drastically during the last merges
modules and systems can be evaluated using e.g.
t NUM mountain avenue murray hill nj NUM usa
figure NUM shows an example of overlapping word hypotheses and possible word segmentations for the string n t ig f all prefectures in the nation
we can solve this by adding new symbols representing the product of the set of relevant categories np p pp and the set of positions NUM NUM NUM after the verb in which they occur
a final limitation which is perhaps more theoretically defensible is that we are forced to be absolutely and strictly compositional in assembling the semantics of verb phrases grouped under the same subcategorization treatment
we have been dealing with type hierarchies that have the property of being bounded complete partial orders except that we have added a btm element to ensure that every pair of types has a glb
since everything is a subtype of itself and btm is a subtype of everything there is a NUM in each of the diagonal cells and in the cell for btm on each row
the effect of the first statement would be to ensure that at compile time the feature person will be instantiated to NUM if it does not already have a value of any kind
for example a naive compilation of disjunction into many alternative rules and lexical entries combined with an equally naive parsing algorithm may produce worse behavior than an implementation that interprets the disjunctions directly
the easiest and most obvious way is to turn the iteration into recursion with the necessary flat structure being built up as the value of a feature on the highest instance of the recursive expansion
we can conceive much nlore types of dictionaries for example lexicons for syntactic attd semantic analyses and dictionaries tha t are to be created or extracted from existing ones upon users or developers nee is
during a run of the program nodes become activated when perceived to be relevant and decay when no longer perceived to be relevant
in the initial stage when the program is presented with a sentence the default codelets initialized in the coderack are affix and affinity codelets
if there is a unique word boundary before x and after y we refer to the ambiguity existing in xy as a combination ambiguity
it is well known however that they perform poorly when presented with ambiguous fragments that have alternate word boundaries in different sentential contexts
various types of knowledge from statistics to linguistics are seamlessly integrated for the tasks of word boundary disambiguation as well as sentential analysis
we describe our model in section NUM showing a sample run of our program in section NUM to illustrate the behavior of the model
the complete model needs a new matrix of conditional probabilities that contains the probability of state pairs in time t lcb NUM NUM rcb p lcb pij i l n j l n rcb pij p ql silq2 sj NUM so the complete model NUM consists of lcb a b 7r p rcb
with this statement the ptgc problem can be restated as follows given the observation symbol sequence o t phonemes and the hmm a find the hidden state sequence q t graphemes that maximizes the probability p o i q
to prove this only the following assumptions are required qx t is a state sequence that ends at time t at state si qx t c qe and qx t is part of one of the e globally best hidden state sequences
in equations NUM NUM qt is the hidden state of the system at time t si is the th possible hidden state of the system ot is the observation symbol at time t and vm is the m th possible observable symbol
the basic theory the pilot implementation and the proposed final system are presented in section NUM the evaluation procedure and the error measure methodology are described in section NUM in section NUM the experimental results of the system are presented and the nature of the errors is discussed
we decided to divide each phoneme duration in a fixed ratio NUM and NUM
thus diphone sets for new synthetic voices are easier to produce
a special user friendly intert ace package was developed in order to facilitate these operations
unfavourable positions like inside stressed syllables or in over a ticulated contexts were excluded
the isadora system is a tool used for modelling of one dimensional patterns like speech
to a large extent these issues are part of a necessary tradeoff between intelligibility and naturalness
nevertheless none of them succeeded in building a complete system providing high quality synthetic speech
plosives are exception to this rule they are divided just in front of the opening burst
next word pronunciation is derived based on a user extensible pronunciation dictionary and letterto sound rules
a text pre processor converts further special formats like numbers or dates into standard graphemic strings
further the syntactic and semantic components of these nodes are considered independently
the combined system produced an error rate of NUM NUM
the probability p ms t is then
statistical decision trees are constructed in a two phase process
to address both of these issues we made pairwise comparisons of consensus labeled phrase groups using measures of relative change in acoustic prosodic parameters over a local window of two consecutive phrases
in the instructions subjects were essentially asked to analyze the linguistic and intentional structures by segmenting the discourse identifying the dsps and specifying the hierarchical relationships among segments
second while scont and sf appear to share prominence features in table NUM table NUM reveals differences between scont and sf in amount of f0 and db change
features for group t included significantly lower f0 maximum and average and lower rms maximum and average for read speech but only lower f0 maximum for the spontaneous condition
as for the segmentation analyses we compared intonational correlates of segment boundary types not only for group s versus group t but also for spontaneous versus read speech
recent studies have focused on the question of whether discourse structure itself can be empirically determined in a reliable manner a prerequisite to investigating linguistic cues to its existence
passonneau and litman to appear analyzed correlations of pause as well as cue phrases and referential relations with discourse structure their segmenters were asked to identify speakers communicative actions
shinagawa ku tokyo NUM japan ami c csl sony
we only require dlat there i e some set of feal ul es we use for similarity judgcmettts
we propose an implementation method for obtaining features based on abstracted triples extracted fi om a large text eorpus utilizing taxonomical knowledge
the filtering process reduces the number of abstracted triples using heuristics based on statistical data attached to the abstracted triples
the first and the second number in NUM shows the class frequency and the depth of synset respectively
govut illi thlt g tllg i tla division colllq ollhtry co lllcil collf l eltc
if we assume a relatively uniform distribution of features the total number of features increases with depth in the hierarchy
u this section we compare ifsm s similarity judgements to those generated by other tneth is
the purpose of the following phases is to extract featm e sets for each synset in an abstracted form
one of the simplest methods is to make groups by cutting the hierarchy structure at some depth from the root
leads to a considerable loss of information and concomitant decrease in prediction accuracy
then the second most precise filter cascade was selected
the remaining uncertainties are marked with dashed lines
this question was easy to answer using bible
each filter combination resulted in a different model
the evaluation is uniform over the whole lexicon
whether a pair of words is considered a cognate pair depends on the ratio of the length of their longest not necessarily contiguous common subsequence to the length of the longer word
for each source word s target words were ranked by their dependence with s the top n target words in the rank ordering for s formed the entry for s in the n best lexicon
minimized by aligning d with g rather than with c
1punctuation numbers etc also count as words
by computing logarithm on both sides of equation NUM we will get the probability score core p lt
the base method of word level aligtnncnt is extend d with NUM hrase level alignntettt that ow xcomes the dhdrence of matching unit and provides more opportunity for the extraction of richer lit guistic information such as l hrasal lewq bilingual dictionary
q he structural similarity in word order and units between english and l rench tiiiist e lie of the l ajot factor to the succ ss of th tuethods
i or tim exl e l led tnethod of phrase alignment the itase model is an intcrntediatc stage for the estima l iou of word to w rd f rol abilith s
when a typical alignm at is denoted by a the l rol ability off given can l e written as the sum over all l ossibh alignments brown et d
the alignntent process of gen rating korean phrases and selecting their matching i hrases in l nglish can l e formul tted around i he l rincipl o tlyna mi
despite the wide agreement that discourse structure and linguistic form are mutually constraining there is little agreement on how to determine the structure of any particular discourse
locations were relatively independent of one another as shown by the the fact that segments varied in size from NUM to NUM phrases in length avg
table NUM shows the average human performance for both the training and test sets of narratives for both boundaries identified by at least three and four subjects
that is if a cue word occurs at the beginning of the prosodic phrase after the potential boundary site the usage is assumed to be discourse
figure NUM shows boundaries assigned by the pause algorithm pause for the boundary slot codings from figure NUM repeated at the top of the figure
for both pause and cue the recall is relatively high but the precision is very low and the fallout and error rate are both poor
our definitions may contain up to three general types of information as shown in the examples in description this contains genus differentia information
for this reason we establish a graph matching threshold to decide whether we will join a new graph to the cckg being built
the figures in table NUM are similar due to the small number of articles and lack of degradation in human performance over the short period of time it took for each of us to tag average NUM minutes per article
running the transducer backwards to parse a surface form into possible underlying forms however remains nondeterministic in subsequential transducers
therefore inaccuracies in predicting output strings represent real errors in the transducer rather than manifestations of other phonological phenomena
each pair consisted of an underlying pronunciation of an individual word of english and a machine generated surface pronunciation
the difference lies in the order in which state mergers are attempted and can have significant effects in the results
first rules applying to larger classes of phones will lead to an even greater explosion in the number of states
this transducer will flap a t after any odd number of stressed vowels rather than simply after any stressed vowel
using variables can increase the size of the output alphabet but none of the complexity calculations depend on this size
purely nativist approaches such as the principles and parameters model build parameterized linguistic generalizations directly into the learning system
section NUM then describes each of the augmentations to ostia based on the faithfulness community and context principles
in the international section transliterated personal names are more than the other two
if one defines key as the part of speech functional distinctions expressed by means of tone rather than mood alone one can integrate the mood and key systems into the grammar by positioning key systems as dependent on the various mooo systems s grammar for the declarative and interrogative sentence mood
NUM threading and set valued features the threading technique can also be used to implement some of the effect of set valued features
alternatively as is done in many wide coverage systems for efficiency reasons syntactic and semantic analysis can be separated into consecutive stages
so using selectors and boolean combinations of feature values together we have developed an analysis that completely eliminates disjunction and hence non determinism
the intuitive motivation behind this move is to regard the completion of a subcat requirement as being signaled by a special finish category
the grammarian needs to add a declaration describing the set of types and the partial order expressed as immediate dominance on them
in this case each vector will have nine elements and adjacent positions will be linked if the corresponding column element is NUM
the feature le t encodes what can precede and computational linguistics volume NUM number NUM right what may follow a category
associated with ca b and the other positions in these two features are linked by shared variables x1 x as indicated
first of all consider how to represent the various subcategorization possibilities of a verb like send using boolean combinations of atoms
it would be nice to f lcb nd some way of combining this single schema approach with a single subcategorization entry subsuming multiple possibilities
collocations are known to be opaque that is their meaning often derives from the combination of the words and not from the meaning of the individual words themselves
a collocation gives the context in which a given word was used whicl will help retrieve documents using the word with the same sense and thus improve precision
running the collocation set c2 over the database db1 produced our worst results and this can be attributed to the low frequency in db1 of many collocations in c2
it is directly applicable to machine translation systems that use a transfer approach since such systems rely on correspondences between words and phrases of the source and target languages
we can then find values of the n s that cause the algorithm to miss a valid translation as long as the corpus contains a modest number of sentences
each word in the original corpus is associated with a set of pointers to all the sentences containing it and to the positions of the word in each of these sentences
the alternative we currently support is to allow the user to replace the default thresholds during the execution of champollion with values that are more appropriate for the corpus at hand
for instance consider what happens when the coders randomly place units into categories instead of using an established coding scheme
NUM the module must not make the decision but produce all alternatives in parallel to be winnowed out by later processing
NUM the sp must modularize sentence planning tasks as far as possible to facilitate design clarity development and maintenance
the architecture of the healthdoc sentence planner is shown in figure i solid arrows indicate data flow dashed arrows indicate control flow
NUM they are followed by the content delimitation module and finally by the exophoric and endophoric choice modules also in parallel
the sentence planner embodies a design that we hope has some general applicability in bridging the gap between text planners and sentence generators
the aggregation module eliminates redundancy in the pre spl by grouping entities that are arguments of the same relation process etc together
depending on the contextual and linguistic constraints roles that are listed in the input might be suppressed and additional ones might be introduced
special thanks to john wilkinson who was one of the key sentence planners during the first phase of our work
the endophoric lexical choice module chooses lexical units for entities that have already been introduced in the discourse either verbatim or by related entities
in addition certain morpho syntactic analyses are performed to handle structures that are specific only to one of the two languages involved
to exploit this thesaury t effect in translation we include similarity between target and dictionary translation as one of the factors
the significant majority of words in bilingual sentences have diverging translation those translations are not often tbund in a bilingual dictionaly
this generally corresponds to the results from a recent work on a variety of tasks such as terminology extraction and structural disambiguation
in NUM of the correct connections the target of the connection and dictionary translation have at least one chinese character in common
this contrasts with approaches based on word specific statistic where strongly associated word pairs selected may not have a strong presence in the data
it does not employ word by word translation probabilities nor does it use a lengthy iterative em algorithm for converging to such probabilities
the algorithm works from left to right in st using a dynamic programming procedure to maximize pr st itt
results obtained fiom the algorithms demonstrate that classification based on existing thesauri is very effective in broadening coverage while maintaining high precision
most county jail inmates did not commit violent crimes
l he proposed alignment is done first or l hras pairs and then word pairs that eventually induces the bilingual dictionary
however choosing the right subset is not easy
the repetitive ai plicatioil of tim alignmeut m d reesl imation h ads to a convergent stationary state where the tra ining stops
although the proposed algorithm can not cover all possible alignment cases the proposed algorithm produces resonably accurate alignment results efliciently as is demonstrated in the following section
as another method to relax the problem of decision dependency on the previous matches preemptive scheme to find max matching of phrase ki is adopted
the building of a data base is beyond the scope of the architecture
the likelihood of all alignable cases within bilingual phrase is defined as in equation NUM where e l
an accurate iimm designed by the authors for korean sentences taking into account the fact that a korean sentence is a sequence of word phrases is used shin et al
whenever a computer randomly calls them from
revised corrected documents may be processed as any other form NUM document
embedded subsegments are shown with brackets
the cbs are shown in bold
in our corpus only NUM of sentences are written by using one tagged words
although we have encouraging initial results there are a number of questions to be answered for example the minimmn seed segmented corpus size required the minimum initial word list required the effect of reestimation for a large unsegmented corpus with various out of vocabulary rates
in order to compute the parameters in figure NUM we need the counts involving unknown words such as c ti NUM ti NUM unk c unk and c wi tl a unk
NUM c duke of hanover as a new word while this word is divided into hanover and duke in corpus segmentation
where is a normalization constant lcb tt NUM ak rcb are the positive model parameters and lcb fl fk rcb are known as features where fj h t e lcb o NUM rcb
although raw training only collects a subset of the data collected by comprehensive training it still gives kankei some flexibility when disambiguating phrases
a feature given h t may activate on any word or tag in the history h and must encode any information that might help predict t such as the spelling of the current word or the identity of the previous two tags
the running time of the parameter estimation algorithm is o nta where n is the training set size t is the number of allowable tags and a is the average number of features that are active for a given event h t
which occurs less than NUM times in the data is unreliable and ignores features whose counts are less than NUM NUM while there are many smoothing algorithms which use techniques more rigorous than a simple count cutoff they have not yet been investigated in conjunction with this tagger
if the above feature exists in the feature set of the model its corresponding model parameter will contribute towards the joint probability p hi ti when wi ends with ng and when ti vbg NUM
as seen in figure NUM about is usually annotated with tag l which denotes in preposition or tag NUM which denotes rb adverb and the observed probability of either choice depends heavily on the current article
numbers are stored under these keys to record how often such a pattern was seen in a not necessarily ambiguous vp or np attachment
or above for both read and spontaneous speech values of at least NUM
where the average column total is t the statistic is given by
consider the interpretation of the referent of the boxed pronoun he in segment z
the types of discourse units being coded and the relations among them vary
however there is no analysis of the statistical significance of these correlations
we use machine learning to automatically construct segmentation algorithms from large feature sets
the potential boundary sites are between the text lines corresponding to prosodic phrases
no statistical analysis of the significance of the differences was presented however
note that human performance is basically the same for both sets of narratives
the left column shows the prosodic phrase numbers which are explained later
figure NUM shows a snapshot of the generalized forward algorithm
moreover sequence of characters may constitute a different word
multilingual technical dictionary the objective is to set up mappings between technical terms of korean and other langnage s in both directions
it also stores the sounds of single syllables diphones numerics high frequency words gazetteers flmctional words and consecutive word sequences
since the notion of the one word per character sociological word is not a good working hypothesis for linguistic words and since there is no fixed length for words a crucial issue is whether the notion of linguistic words can be directly used as the standard for segmentation unit
we calculated lexical accommodation rates for client and agent in the same language human human experiment setting for client and japanese to english interpreter in the human interpreted experiment setting and for client and japanese4o english wizard interpreter in the machine interpreted experiment setting
specifically then if we base our predictions on these standard accounts in the literature we should expect the following results concerning both level and direction of accommodation in human human interaction we should find significant lexical accommodation
there is a growing body of work which explores and quantifies the differences between corpora
we show that corpus similarity can only be interpreted in the light of corpus homogeneity
a method for evaluating the accuracy of the measure is introduced and some results of using the measure are presented
the most interesting measures are those for the difficult proper name cases
table NUM comparative contributions of three scoring measures after NUM learning epochs
the repertory of these test loci is given in fable NUM
next to last word of phrase test each word of phrase in succession
we applied our approach m all three
most rules also test the label of thc candidate phrase NUM
this measure did not improve predsion or recall in the learned sequences
a similar strategy is used for number expressions using numeric tags
none donald f pescenza none analyst with org nomura securities inc org
all results in this section are on the ibm training and test data with the exception of the two average human results
coordination in tree adjoining grammars formalization and implementation
this valency stnlcture is then converted into the equivalent english sentence structure
dependency analysis determines the dependency structure to indicate the association between words
the first determines whether lhc input sentence has double subject construction or not
an example of the japanese sentence analysis
a simple sentence usually has only one subjective case in most languages
the second determines which of tim four wtri mls file sentcnce is
figure NUM derivation for keats steals and chap
if the score of every eastern asian string does not exceed the threshold then the loop returns nil which indicates that no eastern asian characters are involved in the code string
these methods however can not handle east asian languages because they presuppose that input texts are easily segmented into words which does not hold true in these languages
if the text is judged to be written not in the language the person can read then it is written in another language or decoded with an incorrect coding system
step NUM this step divides the given code string into east asian part i.e. sub strings consisting of east asian characters and the rest i.e. european part
let text be the set of words in a text then the likelihood of text with regard to language i is given as tile following p NUM ext l
NUM selecting possible languages for the given coding system the coding system or the character set s of a text is loosely related to the language of the text
if the give code string contains escape code sequences defined in iso NUM or its variants east asian character strings are easily extracted because east asian characters are explicitly marked by escape sequences in the string
table NUM likelihood scores of characters of asian
this part is decoded with each coding system
the gui is unique in that it provides methods for input edit and display of text in multiple languages
first sentence boundaries are found in both texts using finite state transducers
this paper classifies lhe japtulese double subject constntction inlo four types mid describes problems arising when analyzing fllcse lypes using orditmry japmlese construction approaches
if our main interest were linguistic or cognitive scientific we would be even more concerned about the way our system can not handle multimodal word behavior and about the resulting misclassifications and fracturing of the classes
furthermore it is possible to organize an n gram frequency database so that close structural tags are stored near to each other this could be exploited to reduce the search space explored in speech recognition systems
the sex of keywords is used in order to retrieve similar artmes a c ording to the folh wing formulas
NUM association for computational linguistics computational linguistics volume NUM number NUM ory and observation arises in word frequency distributions and in this light evaluate applications of the good turing results
frequent words from a corpus1 however we have developed a second algorithm to be used after the first to allow vocabulary coverage in the range of tens of thousands of word types
we appreciate the students who agreed to provide thei essays for the present project
multiple parts of speech are handled by putting all the assertions regarding grammatical parts of speech before those regarding ungrammatical ones
lcb park mpalmer rcb c linc
in constructing a categorial lexicon for the grammar we used an existing grammar independent lexicon that had been made available as part of the xtag project at the university of pennsylvania
as an initial attempt the present implementation focuses on certain dominant types of syntactic mistakes as identified from the available set of students essays
we present a prototype grammar checker for english as a second language esl students utilizing combinatory categorial grammar ccg written in sicstus prolog
the first permutation is performed for the first auxiliary verb next to the main verb and the locus moves on from the main verb to the first auxiliary verb
if all the left term condition for matching the case markers meet the permutation frame is valid and the number of subcategorization frames for a predicated is sometimes increases
information on the mapping between the surface case frame and the deep case frame and yet is free of potential combinatorial explosion due to an exhaustive empirical research and development of japanese verbs and auxiliary verbs
two cases of multiple deep cases in a single slot there are two kinds of description by which multiple deep cases are described in a deep case slot of a subcategorization frame fig l
one of the reasons NUM is that other postpositions that can be mapped into a thematic role are supposed to fall into either of the seven slot and take the position as the alternative case markers
if it fails the analyzer looks for other slots the other subcategorization frame of the same verb and then the frames of other verbs that appear in the different place of the sentence
thus we decided to make one by ourselves by a bootstrapping method to make the initial list of the classification and to make it to grow by developing a working lexicon for mt systems
we propose a new type of verb subcategorization frame code set that combines the verb s surface case set and the deep case set as a solution to the difficulties of empirical researches on japanese
the architecture that combines the verb surface case frame and deep case frame is described in section NUM followed by extended mechanisms lot applying what we generalized from voice conversion phenomena triggered by auxiliary verbs
in other words word ordering is almost free for the major syntactic elements in a japanese simple sentence except tor the predicate itself which is to be placed at the end of the sentence
NUM the process is exponential in the worst case because if a sentence contains a word with k modifiers then a version it will be generated with each of the NUM k subsets of those modifiers all but one of them being rejected when it is finally discovered that their semantics does not subsume the entire input
in these labels b and c are variables representing the first or distinguished indices associated with b and c by analogy with parsing charts an inactive edge labeled b b can be thought of as incident from vertex b which means simply that it is efficiently accessible through the index b
string positions provide a natural way to index the strings input to the parsing process for the simple reason that there are as many of them as there are words but for there to be any possibility of interaction between a pair of edges they must come together at just one index
nothing turns on these details which will differ with differing ontologies logics and views of semantic structure
it consists of a distinguished index r and a list of predicates whose relative order is immaterial
consider the expression NUM NUM r run r past r fast r argl r j name j john
in other words if the final result has phrases with m and n modifiers respectively then NUM n versions of the first and NUM m of the second will be created but only one of each set will be incorporated into larger phrases and no factor of NUM n m will be introduced into the cost of the process
nie powiedziano mi object o tyro the english translation i subject have not been told about that
NUM sam teaches in london and lucy in boston
it is possible to distinguish two main approaches to ellipsis resolution
comp dtrs n adj dtrs phon beautifullyl
NUM john sent the flowers to mary before he did the chocolates
NUM john gives mary flowers and chocolates too
NUM john completed his paper before he expected to
NUM construct the list l of adjunct phrases in s
we have proposed a generalized reconstruction algorithm for ellipsis resolution
the curves in figure NUM show that system a has a much higher precision at the low recall end of the graph and therefore is more accurate
no passage retrieval was done in this run the second cornell run crnlla used their local global weighting schemes with no topic expansion
in general they are constructed to reflect an actual operational environment and to allow as fair as possible separation among the diverse query construction approaches
in general more emphasis has been placed on a per topic analysis in an effort to get beyond the problems of averaging across topics
since topic expansion was necessary to produce top scores the superiority of the manual expansion over no expansion in the berkeley runs should not be surprising
this improvement was unexpected as the removal of the concepts section seemed likely to cause a considerable performance drop up to NUM was predicted
inquery and cornell use overlapped passages of fixed length NUM words as compared to city s non overlapped passages of NUM to NUM paragraphs in length
this system used far fewer top documents for expansion the top NUM as opposed to the top NUM and this may have hurt performance
a generator for our purposes is the inverse
because of the significant differences in formal and textual representational schemes successfully bridging the gap between them is one of the major challenges faced by an explanation system
by specifying a reference process the second argument of the participants accessor the external agent can request a view of the process from the perspective of the reference process
hence it now begins to traverse the children of this topic node which are the as kind of process description process participants and location description content specification nodes
similarly knight instantiates the content specification expressions of process participants description and location description which also cause kb accessors to be invoked these also return views
when the views in the resulting explanation plan figure NUM are translated to text by the realization system knight produces the explanation shown in figure NUM
the explanation of pollen tube growth was produced by applying the explain process edp and the explanations of spore and root system were produced by applying the explain object edp
the task of an explanation generator is three fold to extract information from a knowledge base to organize this information and to translate it to natural language
these explanations are expository in contrast to causal explanations produced by automated reasoning systems expository explanations describe domain phenomena such as anatomical structures and physiological processes
form NUM is the original document and forms NUM and NUM are intermediate forms of the document
on the other hand like advanced mt systems it uses reliable morphological processors and taggers components which are relatively inexpensive require little or no maintenance and greatly enhance output quality
the result of applying tag to each word is shown in figure NUM
figure NUM an application of build in which join vp is the action
figure NUM the most recently proposed constituent shown under
for each model the corresponding conditional probability is defined as usual
the author acknowledges the support of ai tpa grant n66001 94c NUM
chunk tag omitted if n NUM
t arg max score t tgtrees s
where bi is the context in which ai was decided
the internet a natural channel for language learning
the electronic dictionary of japanese developed at monash university is a good examplej the dictionary and reference tool are distributed as freeware ftp l tp cc monash edu au pub nihongo
the advantages of networks for sharing resources are obvious
the net as facility for resource sharing and development
for example all language learners are concerned with lexicons
having fabulous browsing tools computers have a great advantage over traditional dictionaries
intersc tsukuba ac jp conjugate html
they are a wonderful tool for using a foreign language
the final maifltenance was under the control of jim breen
this reinforces our intuition that the use of function words typically the most common words in the two parts of the sentences are quite different NUM
the expressions have to allow the reader to easily identify the referred objects avoiding ambiguities and unwanted implications
in k1 ne knowledge representation languages generic entities are represented as concepts in the t box
consider lcb br example the following text the applicant has to provide all the requested information
in this situation the specific person who is reading the form instun tares the generic applicant
apart from style differences there do exist also differences in realization that depend on the output language
about NUM such relations have t een identified by mann and thompson t987 e.g.
to cope effectively with pronominalization we propose to augment the centering model with mechanisms exploiting the discourse structure
in the tagging of corpus lines then we will also indicate the status of missing elements to the extent that we can tell what that is
however in many cases the feg potential of a verb can be expressed in one or more simplifying formulas by for example recognizing some fes as optional
we explored the use of three different types of scoring metrics for use in selecting the best of the competing rules to add to the sequence
he rule patterns thcmselves are simple and the fact that they arc sequenced localizes their effccts mid reduccs the scope of their interactions
the next two clauses are further antecedents that look to the left of the phrase for contextual patterns of form non none of
this can lead to the selection of lowprecision rules and while small numbers of precision errors may be patched wholesale precision problems make subsequent improvement more difficult
take one of our smallcr training sets in which there arc 9i sentences consisting of NUM NUM iz word tokens with z o77 unique word types
the rule search space the language of phrase rules supports a large number of possible rules that the phrase rule learner might need to consider at any one time
following this the learning procedurc needs to consider every rule that can possibly apply at this juncture which itself is a function of the rule schema laaaguage
for example say pi tests for the presence of a lexeme to the left of a phrase and e tends the phrase s lxaundaries to include v
the analysis in this paper attempts to show that the effects described by mogns and steedman an be achieved without any meaning changing operations or unwanted ambiguities
if however their report of tiffs state invokes the progressive aspect then they do become committed to knowing something about the start and end dates
here we have a homogeneous state where there is no result to be achieved no interesting state of affairs that arises as a consequence of reaching the end point
unless i spell out what this label commits me to there is no way for me to defend it or for you to attack it
aspect aktionsart and temporal modeifiers then provide information which can be used to determine the cardinality of this set and to draw other conclusions about its temporal characteristics
this NUM a is all very well as far as it goes but unless the consequences of saying that something is an to the meanings of the parts
the difficult part is ensuring that all your analyses work at the same time and without introducing large nuinbers of spurious readings
postulates mps to specify tile conne dons between the terlns that will appear ill our interpre tations
of course in general we know that most states do have start and end points but in many cases that is all we know about them
has a past habitual reading which is pen to contimmtion in a way that the habitual reading of the simple past can not be
the fourth subject indicated on his questionnaire that he believed he could solve the problem more quickly using the keyboard however that subject had solved the two tasks using speech input NUM faster than the two tasks he solved using keyboard input
since speech recognition may substitute important words one for the other it s important to keep in mind that speech acts that have no firm illocutionary force due to grammatical problems may have little to do with what the speaker actually said
since the utterance adds no new constraints but there are the cities that were just mentioned as having delays it presumes the user is attempting to avoid them and invokes the domain reasoner to plan a new route avoiding the congested cities
first we used varying amounts of training data exclusively for building models for the speechpp this scenario would be most relevant if the speech recognizer were a black box and we did not know how to train its model s
briefly the model consists of two parts a channel model which accounts for errors made by the sr and the language model which accounts for the likelihood of a sequence of words being uttered in the first place
a fundamental principle in the design of trains NUM was a decision that when faced with ambiguity it is better to choose a specific interpretation and run the risk of making a mistake as opposed to generating a clarification subdialogue
each element of the stack captures NUM the domain or discourse goal motivating the segment NUM the object focus and history list for the segment NUM information on the status of problem solving activity e.g. has the goal been achieved yet or not
since the accuracy of evaluation can vary wildly depending on ways in which the data is divided into training and test sets the re sampling method of cross validation is used here which gives the average over possible partitions of the data into training and test sets
ianother possibility is to represent the data as an jv x m matrix of height n the number of sentences in the text and width m NUM yes no representing a binary judgement about whether a given sentence is relevant for summarizing
in order to minimize evaluator dependence relations between sentences are expressed in terms of the eonjuncts that can connect them rather than through explicit categories
we found however that an online computer interface that would allow the user to specify the target of a relation to this extent would become prohibitively complicated
question with NUM answers and all of these led to a screen with conjuncts to choose fl om never more than NUM on a screen
in conclusion we feel justified in hoping that the goals of evalnatt r indei endence and languageimtependence are reachable through judicious tunlug of the
subjects were assigned an interface given a 9sentence text and asked to connect the sentences without however performing topic comment extraction
designing the dialog we believe that this is a trial and error process which will have to bc guided by the outcome of experiments more about this will follow below
assisting topic comment extraction frankly we have been unable to find a foolproof method and have settled for user requested online help cued on linguistic aspects of the sentence
the first of our assumptions is that it is always possible to make explicit the relationship of a sentence to what has come before using a conjunct
at this point it is useful to look back at the design considerations outlined above and to clarify exactly what assumptions on sentences and relations underlie them
we surmised that this was due to differences in work methods or thinking methods and that therefore these needed to be equalised a little more
other related issues of interest are the following what can current nlp technology contribute to computer assisted language learning
computational linguists do n t show much interest for call and call experts ignore the work done by computational linguists
but what are we waiting lot
survey in h shrobe american association for artificial intelligence eds exploring artificial intelligence survey talks for the national conferences on artificial intelligence los altos ca
technology from the research laboratories to the real world
in doing so we tend to forget the obvious natural languages are used by people
for a slightly outdated bibliography on call see NUM
obviously nlp technology may be relevant for all these systems but in different ways
what lessons have been or can be learned by looking at the available call systems
more complicated cases require more elaborated frames
the results for the trains corpora are encouraging
experiments on the effect of training and testing kankei on the same set of dialogs used cross validation several trials were run with a different part of the corpus being held out each time
future work needs to further test this hypothesis
i i need the oranges in elmira
if the full pattern of an ambiguity has not been seen kankei can test whether a partial pattern of this ambiguous attachment occurred as an unambiguous attachment in the training corpus
the NUM dialogs predicted attachments in the NUM test data with a success rate of NUM NUM which suggests that kankei is capable of making generalizations that are independent of the corpus from which they were drawn
the results of training on the NUM data and testing on the NUM data are not shown because the best results were no better than always attaching to the vp
using parsed corpora for structural disambiguation in the trains domain
thanks also to james allen for his helpful comments
compared with the ideniification of chinese personal names the identification of transliterated personal names has the following difficulties a no specific clue like surnames in chinese personal names to trigger the identification system
q his paper reports ou ongoing work on a cai i system to facilitate foreign lain guage learning gi ssei lcb i lcb ug
NUM NUM effect of number of features on accuracy
NUM NUM experimental results two attachments sites
the jury was not informed of the automatic generation project
the texts used for tile assessliicnt wcfc business reply letters
rhythm and flow human letters NUM NUM better
la marchandise a did enrcgistr sous le no NUM NUM
some discrepancy is caused by poor quality of translation dictionary
in the current implementation the translation dictionary is empty at the beginning
yes figure NUM the flow of finding the correspondences of word sequences
several attempts have been made for similar purposes but with different settings
sim wj we log2 fje fj2 fe
tables NUM NUM and NUM shows the statistics obtained from the experiments
table NUM shows the recall ratio based on the results of the experiments
table NUM results of science journal threshold num of i correct pairs
the accuracy exceeds NUM in most of the stages
the model performed well with german
efficient multilingual phoneme to grapheme conversion based on hmm
the first improvement was accomplished using a second order hmm
this means that there are at least e more paths
every hidden state should produce one observation symbol
NUM NUM comments on the performance of the proposed system
the matrix density is a way of measuring the saturation of the model that is whether the model is sufficiently objective or is too dependent on the nature of the training material
the system is not limited by any dictionary
NUM NUM the first order hidden markov model
in this scenario the system simply performs the template merging dictated by the most probable coreference configuration for a given coreference set
text we are encouraged by the results of these experiments especially considering the limited amount of training data that was available
the resulting data had the same coreference sets as the training data for the acteristic of context paired with the result of coreference
as one might expect a negative value was learned for the case in which template t was created from an indefinite expression
the texts gave rise to NUM coreference sets and produced characteristics of context for NUM potential coreference relationships between pairs of templates
however it would be reasonable to expect that we have enough data to estimate distributions for coreference sets with only two members
this requires determining when two or more templates describe the same entity as templates created from coreferring phrases need to be merged
as part of determining this state of affairs the ie system must create templates describing the relevant entities that are reported on
and itai NUM dagan et al NUM kennedy and boguraev 1996a kennedy and boguraev 1996b
the discrepancies between the two sets of tagged data were discussed and resolved
each of our original manually tagged versions was also scored against the keys
nonetheless the organization and location subtypes were the most prone to error
table NUM distribution of tag types
participant systems against both the dry run and test keys
multilingual entity task met japanese results
pat is a republican and proud of it
such an approach is attractive for two reasons
qyees can be rewritten using snbstitntion and adjunction
they were however barred from coordinating
NUM john loves mary and bill too
NUM john ate bananas and bill strawberries
previous uses of conjoin applied to two distinct trees
under this view the and heavy np shift
table NUM shows that this term tends to be substantially higher than NUM NUM and increases with word frequency
we simply used the n most frequent words in the union of the two corpora to be compared
the paper presents a measure based on the xx NUM statistic for measuring both corpus similarity and corpus homogeneity
within the literature on statistical language mode llng there is much discussion of related questions
c er wird seiner tochter ein miirchen erz hlen miissen
NUM yore this stem the mort hology eomt onent pro null duces the finite form shown in NUM
NUM a head is combined with its schema NUM verb cluster schema i i o pjj head cluster structure NUM
in their paper hinrichs and nakazawa treat verbal complements as ordinary complements that are included in the comps list of their heads
erziihlen miissen is a constituent in the non fronted position in NUM and the same holds if the verbal complex is fronted
the loc value of the verbal complement is put into slasii and the argmnents of the verbal complement are attracted by the matrix verb
this loc value is licensed by another verbal projection that meets the local requirements of the matrix verb but may be positioned in the vorfeld
in sect ion NUM NUM i will discuss a problem that arises for all accounts of partial verb phrase fronting underspecified comps lists
by the means of a new daughter licensing daughter in a schema for the introduction of nonloeal dependencies this problem will be solved
these tests show that the levels of completeness found during the trec NUM and trec NUM testing are quite acceptable for this type of evaluation
this sentence is the 15th in an article on memory chip production
the following is an example of the actual outtmt of the system
the actual conditions will be presented later
the method for retrieving similar articles may also need to be modified
this approach can be applit abh to our systeln
figure NUM n and word error
figure NUM structure of the system
as presented in figure NUM the tig parser is optimized for clarity rather than speed
the algorithm in figure NUM requires space o igin NUM in the worst case
rule NUM predicts a subtree if and only if the previous siblings have already been recognized
computation proceeds with the introduction of more and more states until no more inferences are possible
a tree is lexicalized if at least one frontier node is labeled with a terminal symbol
the simultaneous adjunction of two left and two right auxiliary trees leads to six derived trees
the tig formalism specifies that every tree is produced that is consistent with the specified order
by means of simultaneous adjunction there can be several trees created by a single derivation
first random sampling is notorious for being slow and it remains to be shown whether the approach proposed here will be practicable
the score for feature f is the reduction it permits in d pl qold where qold is the old field
let us begin by examining stochastic context free grammars scfgs and asking why the natural extension of scfg parameter estimation to attribute value grammars fails
the erf method instructs us to choose the weight fli for rule i proportional to its empirical expectation f
the evolution of a markov chain describes a line in which each stochastic choice depends only on the state at the immediately preceding time point
requirements NUM NUM suggest that the modifiers in the noun phrase should not introduce unnecessary information that can hamper the text fluency and yield false implications
in point of fact we can easily see that the erf weights in table NUM are not the best weights for our example grammar
the divergence between and q at point x is the log of the ratio of x to q x
in other words our method for abstracting a single set of boundaries from the responses of multiple subjects should be reproducible
n NUM NUM NUM and n NUM NUM NUM forming a rapidly descending curve
words or o NUM NUM class words
table NUM ambiguously tagged texts simple verbs
the rule tagger notes this and applies the rule early thus incorrectly changing many p sui c nj pairs to subc nj and reducing the accuracy of t he tagging
in tim case of siml le verb tag set tense person and numl er information is discarded leaving only a v tag and the lower four tags in the table
the scores witlr the siml le verb tag set fur different sizes of training sets are found in tabh NUM and those with the complex verb tag set in l a ble NUM
this behavior is most likely due to the fact that some verb tense person number combinations e mnot easily be distinguished from context so the learner was unable to find a rtfle that would disambiguate them
the results can be found in table NUM
null the problems described above did not occur in brill s experiments because he derived the lexicon fi om a pos tagged corpus and used the untagged version of the same corpus for training and testing
since the test set was a differeat set of texts the ordering of the rules was not as applicable to them as to the training texts and so the tagging performance suffered
the awwage i s amhiguity per word is NUM NUM NUM NUM including t unctuation tags arr i NUM NUM NUM NUM excluding l unctuat ion tags
subjects were presented with transcripts of the narratives formatted so that each non indented new line was the beginning of a new prosodic phrase
NUM NUM accessing focusstate and rt if e is
there are twelve parse trees of four distinct types
because the algorithm assigns boundaries between ficus reducing the number of ficus in a narrative can reduce the number of proposed boundaries
here we summarize computation of a simplified formula for used for comparing two data sets with a single dichotomous variable
identification and classification of proper nouns in chinese texts
the threshold is different from the original one
thresholds are trained from chinese personal name corpus
the words surrounding a word candidate are checked
in summary there are three major errors
the similar problem occurs in the economic section
equation NUM defines the original formula
input text has to be segmented roughly beforehand
we will discuss methods that apply at knowledge acquisition time to produce a single static knowledge source to be used by a single complete semantic tagger as well as methods for dynamically combining outputs of a set of independent possibly incomplete semantic taggers
in order to constrain the discussion we will make the following assumptions senses for each word have been pre enumerated compare for example pustejovsky or nunberg and the references cited in these works which point out difficulties in enumerating senses
possible sources of evidence that could be considered for dynamic combination include domain tags e.g. ldoce box codes collocational and corpus co occurrence approaches frequency domain specific or domain independent selectional restrictions decision trees part of speech and subcategorization lesk et al dictionary approaches semantic distance approaches over ontologles spreading activation marker passing over semantic nets scripts mops word experts
what are the implications of a sequential combination of evidence vs a paralel approach for the dynamic scenario
processing tasks shall operate interactively with progress status shown to the user or in the background with progress status available upon user request
what preprocessing do we assume as input to the taggers in the dynamic scenario
the main issue for discussion will be the advantages of various methods of combining evidence
the german joint research project verbmobil vm aims at the deveh pment of a speech to speech translation system
the unpacker which has exponential complexity selects only the n best scored packed edges where n is a parameter
among the different nlp projects making a limited use of semantic annotations we are aiming at common annotation methodologies beyond particular approaches
in ore experience he change to interactive col trol will tremendously increase the complexity of the resulting ode
search is guided by a weighted linear combination of acoustic score bigram score prosody score grammar derivation seore and grammatical parsability
l he operator ct performs an addition o a nun her to every element of a set
although the results so far are yet not as encouraging as we expected our efforts make for interesting lessons in software engineering
to handle liner grained control issues in any rood ule would take us back to memory and or eomm mication system contention
prosody hypotheses are consumed by the parser in every cycle and represented as attributes of vertices which fall inside a prosodic time interval
namely the very fast type check used to circumvent most unifications causes large disparities in the granularity of agenda tasks
h crementality can lead to the elrlalld for a eolni ete redesign of a lnodule
template is a form or structure which identifies one or more objects that are associated together as the result of an extraction process
on the one hand like word based glossers it puts the user in control by allowing all core linguistic components used by the glossary based engine to be accessed modified and developed by the translator
of course ssa introduces additional noise due to its shallow nature see referred papers for an evaluation of performances4 but as far as our experiment is concerned measuring the complexity of collision sets ssa still provides a good testbed
one of the purposes of this paper was to show that despite the good results recently obtained in the field of corpus driven lexical learning we must still demonstrate that nlp techniques after the advent of lexical statistics are industrially competitive
this result could be biased by the esl s occurring just once in the collision sets hence we repeated the computation for the pair of esfs occurring at a frequency higher than the average NUM in both domains
the definition of collision set cs is the following def collision set a collision set cs is the set of syntactic groups derived from a given sentence that share the same modifier mod o
during the learning phase we wish to eliminate as many hell esl s as possible because the more noise has been eliminated from the source syntactic data the more reliable is the application of the later inductive operators i.e.
in fact some esl can be missed in a collision set or some spurious attachment can be detected but in the average these phenomena are sufficiently rare and in any case they tend to be equally probable
determining the conceptual structure of language leads directly to its comparison with that present in other cognitive systems such as perception reasoning affect attention memory and cultural structure
thus the across schema as in i swam across the lake applies if my path extends between opposite points of the shore of a round lake thus roughly bisecting the lake
for example although many languages inflect nouns to indicate the number of the object referred to by the noun no known language inflects the noun to indicate the color of its referent
thus some of its concepts or conceptual categories have closed class representation in all languages e.g. the grammatical distinction between nouns and verbs with their prototype reference to objects and processes
principles determining which concepts have a structuring function
one may attempt a functional explanation that all and only the structuring principles found in language serve requirements made necessary by other factors such as the nature of communication or of perception
content vs structure in the conceptual inventory of language
one of these is their representation of configurational structure
i term this the overlapping systems model of cognitive organization
comparably the preposition through is shape neutral
for each prepared category profile the system computes the similarity to assign the test document to the most similar category NUM
presidential race athletics mlb giants mlb pga golf tokyo subway attack and food recipe
third we printed them using a NUM dpi laser printer and made nth generation photo copies from them to degrade images by quality
the accuracy drop observed with the fifth generation photo copies was not due to the mapping ambiguity but was caused by recognition errors
more importantly most of them NUM NUM of NUM NUM word shape tokens NUM NUM mapped to a single word
resultant groups are lcb a u rcb lcb e rcb lcb n rcb
finally we scanned the hard copy documents of the first the third and the fifth generation with a NUM dpi scanner
although only text files are nmchinereadable and convenient from the viewpoint of informarion retrieval many documents are available as images alone
the model is based on a clear distinction of the various knowledge sources that come into play in the referring process and provides an implementation tbr martin s theoretical invesl igatious
an extension of the centering theory has been proposed to deal with pronomiualization effectively exploiting the information provided by the discourse structure on how the reader s flow of attention progresses
rules NUM for english and german and rule NUM for italian arc then used to decide wllethcr a pronoun an be used or not
architecture shall allow the user to order document lists to support document viewing by any document attribute s result s annotation s template s slots or combination thereof related to document detection or extraction processing
the architecture shall support the processing of selection statements and the natural language portions of the user defined detection criteria by the extraction component i.e. the desired information to be extracted may be specified through the use of natural language and the specifics identified by an extraction component
the near term requirement shall be met through the use of the core tag sets and the base tag sets as defined in reference a tei or optionally and less desirable by document structure definitions not restricted to the tei methodology verification method demonstration and inspection
document structure definitions are not restricted to the tei methodology however whatever method is used the document structure definition shall be stored in the library so as to facilitate use by new applications NUM NUM NUM NUM NUM basic near term requirement a basic library defining commonly used message types and standard document divisions such as communication header and text shall be available for applications implemented in the near term
b make the reading easier labels in italics have been introduced to identify the steps of the algorithm corresponding to tile main choice points in figure NUM
of course more training of the model would improve performance
word conversion success rate for the first position percentage
the name directory corpora were obtained from the lre NUM onomastica project
these words are responsible for most of the errors encountered
in the appendix the algorithm we implemented is presented
word conversion success rate accounting for all the referenced positions percentage
symbol conversion success rate for the first position percentage
this is a model that contains conditional probabilities of the form
now consider b concerning the transition probability matrix
it gives only one or very few transcriptions per word
NUM rcb lcb j between bunsetsu phrases where a buckets a dependency structure corresponds to a set of kakari uke bunsetsu pairs
first we focused on the unique sets of character types in written japanese and constructed a very small dictionary using mainly functional words in hiragana chm acter
treating of wage compound words another characteristic of old japanese origin verbs wage verb is that they often continue with other words or morphemes to become verbs or nouns
2in this analysis a inllected word is treated a s two or more morphemes a stem part and one or more inflection part
here are some examples of the allocation rules for NUM NUM kanji character sequence NUM NUM kanfi character sequence and NUM katakana character sequence
it is important to understand however that their example shows that the discourse structure determined by informational relations as defined in rst can be incompatible with the one determined by intentional relations
in addition to their claim that intentional and informational analyses must co exist moore and pollack presented an example in which the intentional and informational relations can impose a different structure on the discourse
that is not only is there a functional distinction between nucleus and satellites there is also a classification of satellites according to how they help achieve the hearer s adoption of the nucleus
we argue that the key to reconciling ils in the two theories lies in the correspondence between the dominance relation between intentions in g s and the nucleus satellite relation between text spans in rst
here we simply assume that an utterance conveys either a belief or an action p and thereby makes manifest the speaker s intention that the hearer adopt belief in or an intention to perform p
an evidence relation occurs when a speaker intends the satellite to increase the bearer s belief in the nucleus computational linguistics volume NUM number NUM utterances that express the discourse segment purpose
among researchers interested in computational models of discourse there has been a long standing debate between proponents of approaches based on domain independent rhetorical relations and those who subscribe to approaches based on intentionality
to be an acceptable rst analysis there must be one schema application under which the entire text is subsumed and which accounts for all minimal units usually clauses of the text
the results of experiments that use each of the recency representations separately and in a combined form are shown in table NUM
next we present the linguistic bias approach to feature set selection and applies the technique to the relative pronoun disambiguation task
each case is a set of features or attribute value pairs that encode the context in which the ambiguity was encountered
direct changes to the representation are made by adding or deleting features indirect changes modify a weight associated with each feature
writing a letter thcrcfore involves typing the code that corresponds to the desired pm agraph and inserting the relevant elcnlents
for example for the following criteria rhythm and flow NUM NUM precision of terminology NUM absence of rel ctitions
j lcb tep NUM i i further improvements could also be obtained using a more refined discriminator than mcp1 but there is no free lunch
absence of repelition NUM NUM out of NUM better than tile semi automatic system
this delay and are prel ared to wait fin your deliveo
j esp re que vous nous pardonnerez celte attente et que vous voudrez bien patienter
je suis ddsolde que vous n ayez pas re 2u les chaussurcs de sport blanches
je r onds h w trc demande concernant la marchandise diffdrde suiwmte cardigan NUM lai le NUM
each member o1 the jury wrote a report on cad1 letter with assessment values according to quality criteria
cher monsieur j ai bien regal w tre lettre qui a lelenu toule rl rl attention
the auctioneer auctioned off everything obviously from the estate of an old dying out family in short order
in these cases the phrases involved usually stand in direct semantic opposition e.g. her right hand her left hand
all sentences containing co occurrences of the target adjective and each of its antonyms were extracted from the aphb corpus yielding NUM sentential co occurrences
the specific association of man with the aged sense of old is reflected in the use of this noun in antonymic constructions
the approach is illustrated by an experiment discriminating among the senses of adjectives which have been relatively neglected in work on sense disambiguation
this corpus of NUM NUM NUM words was obtained from the american printing house for the blind and archived at ibm s t j watson research center
the table is arranged from least generous to most generous in the upper left hand corner is a technique bod might reasonably have used in that case the probability of getting the test set he described is lessthan one in a million
for this paper we conducted two sets of experiments one using a minimally cleaned set of data NUM making our results comparable to previous results the other using the atis data prepared by bod which contained much more significant revisions
lower right corner we give bod the absolute maximum benefit of the doubt we assume he used a parser capable of parsing unary branching productions that he used a very overgenerating grammar and that he used a loose definition of exact match
we call a pcfg derivation isomorphic to a stsg derivation if for every substitution in the stsg there is a corresponding subderivation in the pcfg figure NUM contains an example of isomorphic derivations using two subtrees in the stsg and four productions in the pcfg
with this criterion and the example grammar of figure NUM the best parse tree would be the probability that the s constituent is correct is NUM NUM while the probability that the a constituent is correct is and the probability that the b constituent is correct is rcb
the model can be summarized as a special kind of stochastic tree substitution grammar stsg given a bracketed labeled training corpus let every subtree of that corpus be an elementary tree with a probability proportional to the number of occurrences of that subtree in the training corpus
in spontaneous speech we could not achieve the same effects
we all that store agenda in cycle i
actual rcb for x acoustic prosody and l igraln
agenda pop will remove the best triple from an actual agenda and return it
for every vertex we keep a best first store of scored edge pairs
the search prodecure is a beam search implemented as an agenda access mechanism
file former is implementation related the latter algorithm arid theory related
to this end we developed a parallel version of the intarc parser
the chart vertices correspond to the frames of the signal representation
after this replacement there were NUM distinct word bigrams
that is open data were tested in the experiment
we discuss the reason for this in the next section
we tested three variations of the new word extraction method
this result does not necessarily mean that reestimation is useless
we wish to give the word a formal interpretation only to the extent that it helps us in our research and provides a container for the features and entities we describe
the remainder of this paper is structured as follows
this paper examines the source of this overestimation bias
to the eye the overestimation seems fairly small
does this imply that the urn model is wrong
the observed vocabulary sizes are represented by large dots
sion to occur more often as the novel progresses
coherent discourse requires local topic continuity
we conjecture that there is in general an exact correspondence between the output of the ltig procedure and the gnf procedure
as with tags adjoining constraints forbidding the adjunction of specific auxiliary trees on specific nodes can be required in the resulting ltig
the path set of a grammar is the set of all paths from root to frontier in the trees generated by the grammar
the reason for this is that a wrapping auxiliary tree has nonempty structure on both the left and the right of its spine
if all the frontier nodes other than the foot of an auxiliary tree are empty the tree is referred to as empty
any context free grammar cfg can be converted into a lexicalized tree insertion grammar ltig that generates the same trees
however if sharing is being used then one chart state can correspond to a number of different positions in different trees
further since it can not be the case that t u there is no ambiguity in the mapping defined above
this could be a complicated process however since the rosenkrantz algorithm alters the trees more radically than the gnf procedure
this is due to loss of information when the same rule is derived in more than one way by the gnf procedure
these ajo straet patterns called core patterlls conl a in the a memnt o redmmant intbrmation conveyed by the attested evidence and are automatica lly
llr wor co o l l roll ie l oriis
we suppose that the reason for this phenomenon is that a comma adds modality to the words and lengthens the pauses as described in the previous section
anmyzing speech data we confirmed a correlation between the level of a function word and the length of a pause inserted ai er that word
this method controls pause location and length in speech synthesis with the conjunction level in ldg using only lexical information with no need fi r syntactic analysis
in future work we will collect such data to proove this hypothesis in so doing will refine our method to improveits ability to analyze long japanese sentences
null these syntactic characteristics of the japanese language make it difficult to determine the dependency modification structure of hmg sentences
the part discourse structure analysis in fig NUM then presumes the sentence structure before syntactic and semantic analysis
the set of features under consideration is vast but may be expressed in abbreviated form in table NUM
to guide us in developing p we collect a large sample of instances of the expert s decisions
in the following pages we present a unified approach to these two tasks based on the maximum entropy philosophy
the maximum entropy method answers both of these questions as we will demonstrate in the next few pages
if we impose no constraints depicted in a then all probability models are allowable
we would like to include in the model only a subset of the full set of candidate features jr
in section NUM NUM we describe maximum entropy models that predict differences between french word order and english word order
NUM phrase normalization we identify head modifier pairs in order to normalize across syntactic variants such as weapon proliferation proliferation of weapons proliferate weapons etc into weapon proliferate
some of these techniques are aimed primarily at identifying multi word terms that have come to function like ordinary words for example white collar or electric car and capturing other co occurrence idiosyncrasies associated with certain types of texts
however for these queries where the improvement did occur it was very substantial indeed the average gain was NUM in NUM pt precision while the average loss for the queries that lost precision was only NUM
one way to approximate the manual text selection process we reasoned was to focus on those text passages that refer to some key concepts identified in the query for example alien smuggling for query NUM below
since each unique term can be thought to add a new dimensionality to the representation it is equally critical to weigh them properly against one another so that the document is placed at the correct position in the n dimensional term space
the key concepts for now limited to simple noun groups were identified by either their pivotal location within the query in the title field or by their repeated occurrences within the query description and narrative fields
our nlir system employs a suite of advanced natural language processing techniques in order to assist the sta null tistical retrieval engine in selecting appropriate indexing terms for documents in the database and to assign them semantically validated weights
a serious problem with this content term expansion is its limited ability to capture and represent many important aspects of what makes some documents relevant to the query including particular term co occurrence patterns and other hardto measure text features such as discourse structure or stylistics
for example if phrases stream tends to pack relevant documents into top NUM NUM retrieved documents but not so much into NUM we would give premium weights to the documents found in this region of phrase based ranking etc table relationships etc
we discarded characters that appeared only once in the training texts NUM character types remained
obviously a robust word segmenter is the essential first step
word segmentation accuracy is expressed in ternrs of recall and precision
in japanese a lot of words consist of one character
automatic extraction of new words from japanese texts using generalized forward backward search
figure NUM modified segmentation models with consideration to unknown words
the word model assigns a reasonable probability to the unknown word
as for reestimation table NUM shows no significant improvements in the new word extraction accuracy
figure NUM excerpts of correctly extracted new words matched incorrectly extracted word hypotheses
because the phoenix approach ignores small fmlction words in the mt ut its translation results are by design bound to be less accurate
the ill lcb parser has lifliculties in parsing long utterances that are highly dislluent or that significantly deviate from the grammar
the system is composed of three main components a speech recognizer a machine translation mt module and a speech synthesis module
this allows the ungrammaticalities that often occur between phrases to be ignored and reflects tile fact that syntactically incorrect spontaneous speech is often semantically well formed
for the phoenix parser we have implemented a simple method that looks for small islands of parsed words among non parsed words and rejects them
in order to assess the overall eflhctiveness of the two translation contponents we developed a detailed end to end evaluation procedure gates el hi
file parser matches as much of the inl ut utterance as it can to the patterns specified by the i tns
this article describes a new method of similarity nmtehing inherited feature based similarity matching ifsm which integrates these two approaches
grouping to examine the appropriate number of abstracted synsets we calculated three levels of abstracted synset sets using the fiat probability grouping method
there are three types of statistical data available i.e. estimated frequency estimated occurrences of abstracted triples and lists of surface words
synset ng con esponding to cat is an abstraction of synset ns corresponding to kitty
fig NUM shows the overall process used to obtain a set of abstracted triples which are sources of feature and weighting sets for synsets
currently there are no explicit methods to determine sets of distinctive features and their weightings of each object word or concept
all the sul iccl s in the top five al slract triples of level NUM are organizal iotf
l he other criteria shows the correctness of the mappings of die surface t riple i atlerns to abstracted tril les
snplmrt s of orgmdzation this is measured i y counting the eon ect surface supports of each absl racted
second the system determines the fitness value of the rules used in translation using these correct and erroneous translation frequencies
fourth in the feedback process the system determines the fitness value and performs the selection process of translation rules
englisit japanese yunfi speaks english NUM wa totemo joouzu ni eigo very well
in this process new translation examples are alttomatically produced by crossover and mutation
the system selects the translation rules which can be applied to the source sentence
the method of the selection process was described in the section on feedback process
all of these translation examples were processed by the method outlined in figure NUM
there are two kinds of translation rules those for sentences and those for words
NUM the translation result has the same structure as the proofread translation result
in this process the system produces several candidates of translation results for a source sentence using extracted translation rules
we shall suggest an alternative to the familiar maximum likelihood bigram estimate which estimates the probability as p wi i wi NUM f wi wi wheref w is just the frequency of occurrence of w in some f wi training corpus
this sort of advantage becomes even more apparent with number words for example if we were trying to predict the likelihood of seconds given six even though the bigram six seconds does not occur in our training text we find that three seconds four seconds and five seconds occur as do six years six months six weeks and six days
with an NUM NUM run to build a NUM NUM advantage at the intermission
set NUM contained articles about arms control from all over the world
discard the word if the previous steps have removed all of it
for routing the routing score is calculated from the class scores
for this report three different methods were implemented and experimented with
at this point the distinguishing terms for each class can be chosen
the first is to choose all of the words in each fist
the distinguishing terms are then chosen by one of three methods
common annotation types shall be defined and these definitions shall be maintained as part of the architecture configuration policy
user commands from the user interface component pass through the user information request area to initiate specific tipster operations for example show a document create a private collection show a document list etc user information requests shall be presented in the language of the document except for two cases noted below
the architecture shall provide for the use of a common user annotation library that provides a repository for pre defined user annotations in the form of partially or fully completed annotations for pre defined document locations or associated with particular attributes that a user may use for comments about the document or the annotations created by the application
tig is o n NUM parsable and strongly lexicalizes cfg
the speedup can be achieved by altering the parser in two ways
the set i t3 a is referred to as the elementary trees
the foot must be labeled with the same label as the root
the scanning and substitution rules recognize terminal symbols and substitutions of trees
the root of at least one elementary initial tree must be labeled s
activated nodes in the conceptual network spread activation to their neighbors and thus concepts closely related to relevant concepts also become relevant
the list of structures built are active relations an affinity relation between the characters b n and
for example when the program worked on sentence NUM it produced sentence NUM once as the answer
for the NUM fragments that appear in globally ambiguous sentences the mutual information approach gave only one interpretation of the word boundaries
if many good linguistic structures have been built the temperature will be low and the system will make decisions less randomly
an affinity codelet works on any two adjacent character objects to evaluate whether an affinity relation should be built between these two characters
indeed in this case the new affinity relation between the characters ps r n and shgng has won
in short the immediate constituents of a word object are character objects and those of a chunk object are word objects
by checking the structural relationships among words in a sentence rule based approaches aim to overcome limitations faced by pattern matching and statistical approaches
note that this figure is lower than our previously mentioned average of NUM since we were unable to exactly replicate the atis model from cmu
each point indicates the performance of the speechpp using a set of models trained on the behavior of sphinx ii for the corresponding point from the second experiment
an analysis of the experiment results shows that the plans generated when speech input was used are of similar quality to those generated when keyboard input was used
the results of the first experiment are shown by the bottom curve of figure NUM which indicates the performance of the speechpp with the baseline sphinx ii
the curve clearly indicates that the speechpp does a reasonable job of boosting our word recognition rates over baseline sphinx ii and performance improves with additional training data
third we combined the methods using the training data both to extend the language models for sphinx ii and to then train speechpp on the newly trained sr
every constituent in each grammar rule specifies both a syntactic category and a semantic category plus other features to encode cooccurance restrictions as found in many grammars
for prob s we train a word bigram back offf language model katz NUM from hand transcribed dialogues previously collected with the trains NUM system
for comparison we created a loglinear model with the same four features the results for this model are labeled NUM loglinear features
previous work oll automatic pp attachment disambiguation has only considered the pattern of a verb phrase containing an object and a final pp
categorical data analysis is the area of statistics that addresses categorical statistical variable variables whose values are one of a set of categories
each word of the training data was then turned into a feature vector and the feature vectors were crossclassified in a contingency table
an exampie of such a linguistic variable is part of speech whose possible values might include nou n verb determiner preposition etc
since the tagger displays considerable variance in its accuracy in assigning pos to unknown words in context we use boxplots to display the results
then the a ssociation strengths were categorized into eight levels a h depending on percentile in the ranked mutual information values
it differs from previous error tolerant finite state recognition algorithms in that it uses a given finite state machine and is more suitable for applications where the number of patterns or the finite state machine is large and the string to be matched is small
the number of confident word correspondences of words is not enough for complete alignment
experimental results show our system outperforms conventional methods for various kinds of japanese english texts
in such a case the method constructing initial asm needs to be modified
we obtained the data from paper version of the magazine by using ocr
original forms of english words are determined by NUM rules using the pos information
we would like to thank all people concerned for providing us with the tools
the other is the word correspondences that are statistically acquired in the alignment process
this paper describes an accurate and robust text alignment system for structurally different languages
we would like to thank pascale fung and takehito utsuro for helpful comments and discussions
unlike conventional sentencechunk based evaluations our result is measured on the sentence sentence basis
this paper describes the sentence planner sp in the healthdoc project which is concerned with the production of customized patient education material from a source encoded in terms of plans
head i ie head hli f t ead fi med rel nil mocl ret nil modrol dl alter the searches the input is re analyzed using newly found words
u d rel keeps tile observed relationships captured by the pattenl matchers between the two lexical heads of child nodes this value is not actually used in the fi llowing experiments accum cost c i records the accumulated cost of the subtree which has np i as its root
the category which the morphological analyzer assigns to a word is one of the following sn stem of a sino verb n noun pn proper noun num number adj stem of an adjective or an adjectival verb prfx nominal prefix sfix nominal suffix num prfx numerical prefix and numsfix numerical suffix
for three noun words the following rule is applied if only the dependency between hj and h NUM was observed then NUM a is chosen else if only the dependency between h l and h NUM was observed then NUM b is chosen else if only the dependency between h NUM and h NUM was observed then NUM b is chosen
bergler s approach also has a meta lexical layer which maps from syntactic patterns to semantic interpretation that does not affect the lexicon itself
we must address the issue of whether or not a computer based method would be efficient with regard to time and cost of scoring
as mentioned earlier in the paper previous systems did not score responses accurately due to an inability to reliably capture response paraphrases
in situations where lexico syntactic patterning is deficient a lexicon with specified metonymic relations can be developed to yield accurate scoring of response content
these results are encouraging however with regard to using a lexical semantics approach for automatic content identification on small data sets
the architecture shall recognize on going work of reference a in the area of code sets
t is integrated in wsh NUM at the first if clause
an application may contain a detection component extraction component or clustering component or any combination thereof
selection statement is a high level textual description of the needed information as specified by the user
the persistent knowledge repository is a common place storage device where information may be retained
in this case the number of arcs in the traversal graph is given by
we used NUM cases for attributes s and some of these appear in figure NUM
presently this process is labor intensive and requires a co operative effort between users and application developers
foreign language documents in the list may be annotated with english glosses of selected words and phrases
thus chinese string searching algorithms have to deal with a mixture of single and multi byte characters
the kmp algorithm has better worst case time complexity where as the bm algorithm has better average case time complexity
however the searching accuracy depends on the segmentation algorithm which is usually implemented as a dictionary look up procedure
such repetition to form words is used in making emphasis as well as an essential part of yes no questions
ascii clmracter alphabet and the transtbrmed l byte character alphabet representing the different two byte characters in p respectively
the failure transitions are computed from NUM to i NUM becausefl j j
the implementation of a part of speech filter for a given pair of languages depends on the availability of part of speech taggers for both languages where the two taggers have a small common tag set
computing hit rates for each word separately and then taking an unweighted average ensures that a correct translation of a common source word does not contribute more to the score than correct translations of rare words
input NUM translation lexicon with up to n translations for each word NUM aligned test bitext cumulative hit rates are averaged over all the source words in the lexicon counting words by type
in one evaluation a training corpus of NUM sentence pairs processed with these knowledge sources achieved a precision of NUM NUM while a training corpus of NUM NUM training pairs alone achieved a precision of only NUM NUM
first the most precise filter cascade was selected by looking at figure NUM translations were found for all words in the test source text that had entries in the lexicon induced using that cascade
while the true size of the source vocabulary is usually unknown recall can be estimated using a representative text sample by computing the fraction of words in the text that also appear in the lexicon
then i if the translation pair s t occurs in this oracle list it is reasonable to filter out all other translation pairs involving s or t in the same sentence pair
i baseline no filters i i i i cognate filter by itself achieves the best precision for the best of n translations when n NUM
from the resulting aligned corpus this study used only sentence pairs that were aligned one to one and then only when they were less than NUM words long and aligned with high confidence
NUM iffercnt instantiations of this general strategy for initial phrase labeling naturally arise for different phrase finding tasks
the interpreter first attempts to match the test label of r to the label of the candidate phrase
this is an organization is the leftmost lexeme in the phrase on a list of country words
the final two clauses incorporate the left context wholesale into the triggering phrase yielding org golkswagen of america inc org
l espitc rcsuhs that comparc favorably to those of more mature systems this work is still in its infancy
further we only acquired rules for the hardest cases namely the person organization and location phrases
NUM cspitc thcse impediments wc cmnc dose to reproducing our results with thc english machinclcarned named entidcs rule sequcncc
the rule that most reduces the residual error in the training data is selected as the next rule in the sequence
rule sequences as part of our work in information extraction we have been extensively exploring the use of rule sequences
all other strings of numbers including those which had commas or decimal points were replaced with the token num
in our discussion of results we show how problems for the lower score can be alleviated by increasing the size of the database corpus
furthermore suppose that the two groups appear twice in a pair of aligned sentences and each word group also appears three times by itself
they are embodied in selection statements other detection criteria queries routing queryand templates
a new persistent document may be created by applying corrections and assigning the appropriate revision number
the figures attached to each node in figure NUM are the example penalty scores given by formula NUM under the assmnption that the t and the original thesaurus are tile same and a NUM NUM
the architecture shall provide appropriate information to support tipster features that are included in specific applications
detection component is a component that selects documents from a collection based upon the detection criteria
component is equivalent to a computer system component csc in the conventional life cycle definition
a csu is an element specified in the design of a csc that is separately testable
the erel project is partially funded by the conseil gdndral des bouches du rhsne
why is illico relevant to the development of a language rehabilitation software
all these operations are made by the contextual module
put the white circle in the square a5
NUM example an exercise proposed by
besides for each activity the system proposes a set of functionalities responding to different requirements and competence levels in accordance with the work that is expected to be done by the user
in what follows we first describe the functionality of our erel system then we detail one of the activities proposed and describe some specific elements of nlp required for its development
figure NUM outline of translation process in alt j e
qlm last processes the sentence according io its type
note the consistently large improvements in retrieval precision attributed to the expanded queries
finally the expanded queries were run to produce the final result
NUM rankings from all streams are merged into the final result
sabir used our long queries to obtain long query run
out of the fifty queries we tested NUM has undergone the expansion
NUM top NUM documentss retrieved by each query are retained for expansion
there are a number of ways to obtain phrases from text
the queries used with this stream were the usual stem queries
syntactic phrases extracted from tip parse trees are head modifier pairs
although they are correctly identified as personal names they are assigned wrong features
f esides these types of nouns boundary errors affect the precision 1oo
for each sentence we scan it from left to right to find keywords
of course titles and punctuation used in last section can be adopted too
however only large scale transliterated personal name corpus can give reliable statistical data
the objective is to allow the end user or developer to establish the criteria for extracting information from documents
in our model the variation of characters is learned from ntu balanced corpus
patterns in NUM NUM collect the evidence of a modifier modifiee relationship between a sino verb and a noun the sino verb which appears at the tail of a noun modifier phrase and the noun which is modified by the phrase
this subsection describes the heuristic that is employed when the evidence can not cover any of the entire trees
this means that proper nouns are a major cause of the errors as pointed out in previous research
according to r a asq and are neglected
patterns in NUM NUM collect phrases such as a about b md b about a
table NUM shows the results of the baseline and indicates the number of samples for which the correct the result of baseline comparing the two tables reveals that the proposed method is more accurate than the baseline
in the following part of this subsection we will illustrate the search procedure using the initial value of we lcb k d sn adj n rcb n j sn rcb
in language modeling for speech recognition the goal is to constrain the search of the speech recognizer by providing a model which can given a context indicate what the next most likely word will be
the interruption point ip markes the end of the reparandum and it is followed by an optional interregnum im which includes editing phases such as a filled pause or editing terms
e s speakerb2 sym okay uh e s you prp re vbp asking vbg what wp my prp opinion nn about in whether in it prp s bes possible jj to to have vb honesty nn in in government nn e s
our experiments indicated the following mismatch in segmentation hurts language model performance both in terms of perplexity as well as in terms of recognition word error rate
if no segment boundaries are known during testing it is better to hypothesize segment boundaries using a model trained on linguistic segments than one based on acoustic segments
in some cases the speaker stops a sentence and starts over in contrast with restarts where just a few words are repeated as described in ss2 NUM
interjections are rare and are considered only when the corresponding sequence of words interrupt the fluent flow of the sentence and the sentence later picks up from where it left
in order to simplify the notation the restart notation we developed marks only the boundaries of the entire restart rm to rr with square brackets and the interruption point with a
NUM unfortunately the two test sets did not match completely in terms of the number of words since the lattice test set had been hand corrected after the initial transcription to account for some transcription errors
the results are shown in figures NUM NUM
keyword stated criteria may include statements of document attributes such as author source date of composition date of receipt country of origin etc NUM NUM NUM NUM the user shall be able to view a detection component s interpretation of the submitted detection criteria
hence the treebank provides the resource of multifarious correct instances of word and sentence si litting
spelling errors and where essential other typographical lapses are scrupulously recorded and then corrected
word splitting edward s edward s and sentence spli ting e.g.
the tagged text is then extracted into a file for parsing via gwbtool see NUM NUM NUM
note that the treebanker need not specify any labels in the partial bracketing only constituent boundaries
fable NUM nine typical docnments from a ft lcb i an aster treebank
this material is organized via a menu system and updated at least weekly
figure NUM the gwbtool treebanker s workstation parse window display showing the parse forest for
all the other available lexicons were cascaded this way in the order of their apparent precision down to the baseline lexicon
a machine translation system should not only translate with high precision but it should also have good coverage of the source language
in particular the user shall be able to prioritize references to personalities events objects times or locations as welt as identifying the priority of detection criteria in a submission of multiple criteria statements
the transitions from and to the old states are redirected to the new state the transition probabilities are adjusted to maximize the likelihood of the corpus the outputs are joined and their probabilities are also adjusted to maximize the likelihood
a sequence of outputs can be emitted by more than one sequence of states thus we have to sum over all sequences of states with the given length to get the probability that a model emits a given sequence of outputs
e.g. if we use the unigram constraint and merge states until no further merge is possible under this constraint the resulting model is a standard bigram model regardless of the order in which the merges were performed
if we want to reduce the model from size l NUM NUM the trivial modeli which consists of one state for each token plus initial and final states to some fixed size we need o l steps of merging
similarly the log perplexity lp is defined log pm o lpm r log ppm a k here the log probability is normalized by dividing by the length of the sequence
we applied our iterative algorithm to that corpus
the input of the following sentence will therefore contain no information allowing a resolution of the pronoun
to create this representation we NUM relabel the attributes using the right to left labeling NUM incorporate the subject and recency weighting representations by adding the weight vectors proposed by each bias NUM apply the restricted memory bias by keeping only the n features with the highest weights where n is the memory limit and choosing randomly in case of ties
mance of the recency weighting representation on the other hand may be caused by NUM its lack of such a representation of local context and NUM its bias against antecedents that are distant from the relative pronoun e.g. to help especially those people living in the patagonia region of argentina who are being treated inhumanely
in other words if trained with cleanly segmented data a trigram model is more likely to produce a better segmentation since it tends to preserve the nature of training data
like the recency bias however the baseline representation already encodes the subject accessibility bias by explicitly recognizing the subject as a major constituent of the sentence i.e. s rather than by labeling it merely as a low level noun phrase i.e. np
finally we argue that the linguistic bias approach to feature set selection offers new possibilities for case based learning of natural language it provides a natural mechanism for combining the frequency information available from corpus based nlp techniques with linguistic bias information employed in traditional linguistic and knowledge based approaches to language processing
prey and ol refer to the preceding and following lexical items gen sera and spec sera refer to general and specific semantic class values cn refers to concept case frame activation morphol refers to the morphology of the word to be tagged s do v and last constit refer to the subject direct object verb and last low level constituent i.e. noun phrase verb prepositional phrase respectively
more specifically each tagging decision is initially described in the case representation in terms of NUM features NUM local context features encode syntactic and semantic information for the words within a five word window centered on the current word NUM global context features encode information for any major syntactic constituents that have been recognized e.g. semantic class and concept activation information for the subject direct object verb
for ex null ample the right to left labeling assigns the same antecedent value i.e. ppp to both of the following sentences it was a message from the hardliners in congress who it was from the hardliners in congress who the baseline left to right representation on the other hand labels the antecedents with distinct attributes do ppl and v ppl respectively
this figure has the additional advantage that it can be easily incorporated into existing best first parsers using a figure of merit based on inside probability
at this stage we are able to parse a paragraph and to get a syntactical analyze of this structure
a NUM examples picked randomly from about NUM unseen words are shown in table NUM NUM of them are reasonably good words and are listed with their translated meanings
each sub grammar encapsulates the relevant part of the grammar to access when recursively unifying an input sub constituent of the corresponding category
the task of mapping domainspecific thematic relations to the syntactic slots in an np is therefore left to the client program
we are specifically working on NUM integrating a more systematic implementation of levin s mternations within the grammar
1the research presented in this paper started out while the authors were doing their phd
these constraints require the system to apply specific processes to the context in order to know which objects can be designated by a definite description and which can not if there is no such objects in the context then no definite description can be produced
at higher levels of difficulty the use of relative clauses is allowed and an object can be designated by its position the circle which is in the square a3 plurals the circles and the generic word pawn are also allowed
figure NUM precision recall of a perfect reranking scheme for the top n parses of section NUM of the wsj
the parser uses four procedures tag chunk build and check that incrementally build parse trees with their actions
the first pass takes an input sentence shown in figure NUM and uses tag to assign each word a pos tag
in order to compute lower levels of grammars some rules of this grammar can be dynamically switched off according to the value of a global variable coding the level chosen by the user
the product is called denjikai for windows v2 NUM which retrieves the word information from various dictionaries including edr electronic dictionary
even in the simple example of section NUM we did not explicitly state how we selected those particular constraints
the log likelihood l p of the empirical distribution NUM as predicted by a model p is defined by NUM
somme d argent sum of money pays d origin country of origin question de privilege question of privilege conflit d intdrot conflict of interest
for other phrases however the best translation is obtained by interchanging the two nouns and dropping the de
the general feature selection process is too slow in practice and we presented several techniques for making the algorithm feasible
we began by introducing the building blocks of maximum entropy modeling realvalued features and constraints built from these features
as we pointed out in section NUM this is not how successful translation works
for this application we employ features that are indicator functions of simply described sets
in simple cases we can find the solution to computational linguistics volume NUM number NUM this problem analytically
in c two consistent constraints c1 and c2 define a single model p c ci a c2
also the cotr will have access to information about any tipster compliant components which have already been developed and which may be appropriate for use
apply to a wide range of software and hardware environments be scaleable to a large number of document archives and high document flow rate
a document part will be defined as a portion of a document which requires a particular type of processing in order to exploit its information
also if the same components have been used in different applications it is likely that they have been exercised more extensively than non shared components
in may NUM when the initial architecture design is complete an interface control document will be provided specifying the form and content of all inputs and outputs to the tipster modules
by defining common components and interfaces it will facilitate the development of both operational and research applications as well as the rapid transfer of new text processing technology into the field
to the extent possible and in the government s best interest existing code and capability to be incorporated into the tipster application will be re engineered in accordance with the tipster architecture
in case 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
obviously if an aircraft tail number parser were to be linked to tipster modules its input and output must conform to the output and input specifications of the tipster modules surrounding it
it is expected that any newly implemented functionality which is of potential use to other applications will be submitted to the tipster configuration control board ccb as an extension to the architecture
there are two major ways to deal with the effects of nonrandomness in word usage on the accuracy of statistical estimates
hence it is useful to consider in some detail how their accuracy is affected by inter textual and intra textual cohesion
NUM the present method of finding underdispersed words appears to be fairly robust with respect to the number of text slices k
consider again the distribution of the word ahab in figure NUM in text slice NUM ahab occurs only once
this indicates that the bias should not be attributed to syntactic and semantic constraints on word usage operating within the sentence
by symmetry this probability is identical to the probability that the very last token sampled will represent an unseen type
apparently it is the sequential order in which sentences actually appear that crucially determines the bias of our theoretical estimates
this finding seriously questions the appropriateness of using the growth curve of the vocabulary for deriving a measure of lexical specialization
we did this by augmenting ostia to use phonological feature knowledge to generalize the arcs of the transducer producing transducers that are slightly more general than the ones ostia produced in our previous experiments
only future research will determine whether phonological constraints are innate or merely learned extremely early and whether empiricist algorithms like ellison s will be able to induce a full phonological ontology without them
this tree describes the behavior of state NUM of the transducer in figure NUM in the output string indicates the arc s input symbol with no features changed
the outcomes at the leaves of the decision tree specify the output of the next transition to be taken in terms of the input segment as well as as the transition s destination state
as long as the alphabet is of finite size any machine using variables can be translated into a potentially much larger machine with separate states for each possible value the variables can take
in such a system for two rules to apply correctly the output must lie in the intersection of the outputs accepted by the transducers for each rule on the input in question
if the machine takes the wrong transition the subsequent transitions will leave the transducer in a non accepting state or a state will be reached with no transition on the current input symbol
the expression on the right hand side is NUM n times the cross entropy of q with respect to hence maximizing log likelihood is equivalent to minimizing cross entropy
the answer is of course that ql and NUM are probability distributions over l g1 but not all of l g1 appears in the corpus
dd l show that there is a unique weight fl that maximizes the score for a new feature f provided that the score for f is not constant for all weights
features are rather like geographic features of dags a feature is some larger or smaller piece of structure that occurs possibly at more than one place in a dag
we wish to compute increments NUM NUM to determine a new field with weights 61fll NUM ft
the idea is actually rather simple to estimate how often the feature appears in the average dag we generate a representative mini corpus from the distribution qold and count
effective way of doing this is by newton s method
the next few constraints selected by the algorithm are shown in table NUM
example of textual clue representations type metaphor analogy name b NUM NUM NUM comment comparison involving the meaning of a marker adjective attribute of the object object before the verb notations gn and gv stand for nominal or verbal groups adj and adv for adjectives and adverbs and prep for prepositions
most of the previous works in natural language understanding nlu looked for regularities only on the semantic side of this figure as shown in a brief overview in section NUM this resulted in complex semantic processings not based on any previous robust detection or requiring large and exhaustive knowledge bases
a corpus of NUM explanatory texts in french of about NUM words each has been collected under a shared research project between psychologists and computer scientists in order to study metaphors and analogies in teaching
they can be characterized by syntactic regularities e.g. the comparative is used in structures such as less than more than the identification is made through attributes or appositions
overview the classical nlu points of view of metaphor have pointed out the multiple kinds of relations between what is called the source and the target of the metaphor but rarely discuss the problem of detecting the figure that bears the metaphor
we propose in 1this work takes part in a research project sponsored by the aupelf uref francophone agency for education and research section NUM an object oriented model for representing these clues and their properties in order to integrate them in a nlu system
NUM wa yakyuu o shile imasu
in this paper we describe a method of machine translation using inductive learning with genetic algorithms and show through the results of evaluation experiments that genetic algorithms are effective for the example based machine translation
in figure NUM likes is tile common part in the two english sentences and wa and ga suki desu are the comlnon parts in the two japanese sentences
the effective translation results are grouped into two categories NUM the translation result has the same character string as the proofread translation result
however the translation examples which are produced have the same character strings as tile source sentences and therefore these translatio examples are not inputted into tile dictionary
machine translation method using inductive learning with genetic algorithms
to estimate the average number of candidate translations examined we make the simplifying assumption that the decisions to reject each candidate translation with i words are made independently with constant probability ri
separate the n sentences into eight categories depending on whether each of the source collocation x and the partial translations i.e. a and b appear in it
these evaluations indicate that champollion has a high rate of accuracy in the best case NUM of the french translations of valid english collocations were judged to be good
by applying champollion to a corpus in a new domain translations for the domain specific collocations can be automatically compiled and inaccurate results filtered by a native speaker of the target language
while aligned bilingual corpora will become computational linguistics volume NUM number NUM more available in the future it would be helpful if we could relax the constraint for aligned data
although the theoretical analysis and simulation experiments of section NUM NUM show that such cases of missing the correct translation are rare more work needs to be done in quantifying this phenomenon
for example a tagger for french would allow us to run xtract on the french part of the corpus and thus to translate from either french or english as input
the most obvious are machine translation and machine assisted human translation but other multilingual applications including information retrieval summarization and computational lexicography also require access to bilingual lexicons
for the first corpus db1 we ran xtract and obtained a set of approximately NUM NUM collocations from which we randomly selected a subset of NUM for manual evaluation purposes
as a possible value for the feature constr the alep formalism being type based every feature with its range of possible values has to be declared in the declaration component
in a second section i will very briefly present a semantic framework which introduces the idea of information passing in order to cope with cross sentential anaphora the dynamic predicate logic dpl
an outline of the evaluation scheme is shown in figure NUM
the new language model successfully identifies the most likely utterance
figure NUM shows the normalized probability results of these experiments
however these systems can also have more immediate uses
the interpolation parameters are estimated by using a held out corpus
with a bottom up approach the reverse may be the case
the tree representation also imposes its own constraints mentioned later
the temple translator s workstation provides the mt developer with tools to semi antomatically build glossaries
it is this very database that is accessed at run time by the machine translation system
partial glossary entries and is then loaded in the lexical database
the temple translator s workstation has been developed in c within a two year project at crl
morphological analyzers bilingual dictionaries and bilingual glossaries for spanish arabic
a tipster document manager to support access and processing of user s documents
i.e. the probability of occurrence of state sk when the two previous states are si and sj at t NUM and t NUM respectively
the novelty lies in modeling the natural language intraword features using the theory of hidden markov models hmm and performing the conversion using the viterbi algorithm
considering the effort and cost required to create such a dictionary this is a serious limitation especially for inflectionally rich languages such as greek and german
this algorithm is the only language specific part of the ptgc system and its formulation requires only familiarity with the spelling of the language and not sophisticated linguistic knowledge
appendix implementation notes the fact that only multiplications are involved in the processing of the conversion algorithm led us to convert the algorithm to use only additions
we will then show the algorithm with examples for pruning the erroneous word chains prior to parsing
applications are able to call xmat library functions on the created widgets as well
these guis were constructed using crl s tipster user interface toolkit tuit
ted takes advantage of the computing research laboratory s x multi attributed text xmat widget
the tuit application programming interface api and software library supports document editing and browsing
another tuit api function creates window dialogs and menus for managing tipster collections and documents
the tuit api supports the creation of windows menus and dialogs
ted is being used in several government sponsored projects at crl and is
users can also create their own text annotations to be stored with documents
there are also interfaces for grouping annotations by type or attribute values and for hiding or showing these annotations groups
annotated text can be displayed with color highlighting or with different font styles
the location le jardin the garden is the final location of the motion
such verbs do not behave all homogeneously
this final information was contained neither in the verb or in the preposition
we call these verbs inertial change of position icops verbs
for example we can say courir sur place to run in place
others denote only possible change of position courir to run
we call these verbs change of position cops verbs
they denote a change of the relations between the parts of an entity
pending further funding we believe that the prototype grammar checker can be brought up to an industry strength
the first NUM segments correspond to the first NUM episodes
rhetorical relations do not play a role in their model
however no quantitative evaluation of the results were reported
for tj NUM the average pause duration is NUM
features are present in the ficu that ends in NUM NUM
our algorithm thus performs with recall higher than human performance
the second is the inherently fuzzy nature of boundary location
c4 NUM minimizes the error rate by always predicting nonboundary
then boundary elseif after sentence final contour
using textual clues to improve metaphor processing
they also involve lexical markers e.g.
the main remaining problem however is to choose an adequate processing when confronted with a metaphor and thus to detect the metaphors before trying to build their meaning representation
in conclusion we will discuss how the model can help chosing the adequate semantic analysis to process at the sentence level or disambiguating multiple meaning representations providing probabilities for non literal meanings
for each class attributes give information for spoting the clues and when possible the source and the target of the metaphor using the results of a syntactic parsing
we propose to represent this relevance for probabilistic purposes
it is mready used to evaluate the clues relevance
our aim is to provide nlu systems with a set of heuristics for choosing the most adequate semantic processing as well as to give some probabilistic clues for disambiguating the possibly multiple meaning representations
this can be partially solved using textual clues
thus the string the natural history museum and the board of education is split at and because each of its substrings contains a strong scope np head as we define it with modifiers within its scope
but robert jordan a partner at steptoe johnson who took the lead in drafting the new district bar code said the aba s rules were viewed as too restrictive by lawyers here
the location of an organization for instance could be part of its name city university of new york or an attached modifier the museum of modern art in new york city
the difference between an ordinary common noun and an ordinary common noun turned name is that the unique reference of the name has been institutionalized as is made overt in writing by initial capital letter
as with pp attachment of common noun phrases the ambiguity is not always resolved even in human sentence parsing cf the famous example i saw the girl in the park with the telescope
however some names do take determiners as in the new york times in this case they are perfectly regular in taking the definite article since they are basically prernodified count nouns
it is a fairly standard convention in an edited document for one of the first references to an entity excluding a reference in the title to include a relatively full form of its name
the precision and recall of nominator operating without a database of pre existing proper names is in the NUM s while the processing rate is over 40mg of text per hour on a risc NUM machine
other untyped names such as star bellied sneetches or george melloan s business world are neither people places organizations nor any of the other legal or financial entities we categorize into
if they are sentenceinitial nominator accepts as names both new sears and new coke it also accepts sentence initial five reagan as a variant of president reagan if the two co occur in a document
black box methodology was used for the assessmcm which was era tied out by an independent jury of NUM people who were representative of end users in a blind test context
this asscssillol l used a black b rcb x motlmdology with ain independetlt blindtested jury that gave difforent quality levels in relation to a sot o1 criteria
the overall averages of the entire jury for all the quality criteria including application oriented criteria and for all the letters were as follows
the second conclusion is that the weak points of the semi automatic systems are the strong points of the automatic hybrid systems in the same order
senti automatic system file principal weak points of the semi automatic system are as follows in decreasing order of variation in relation to the human averages
semi automatic system i NUM out of NUM automatic hybrid system NUM NUM out of NUM human written letters NUM NUM out of NUM
we can thus conclude that for the automatic letters the results are representative the semi automatic letters were produced hy ittnlan writers in a real situalion
for this last point all the difl rcnces mc considerable but that between the automatic and semi automatic letters is very great NUM NUM out of NUM i
NUM it has a limited capability for adding new fea null tures when the existing ones are inadequate for the learning task
we conclude with a discussion of the general implications of the linguistic bias approach to feature set selection for case based learning of natural language
NUM let the retrieved cases vote on the predicted class solution value and use that value to resolve the ambiguity for x
unfortunately deciding which features are important for a particular learning task is difficult especially when interactions among potentially relevant features are unpredictable
system developers can safely include features for all available knowledge sources in the baseline instance representation the irrelevant ones will be discarded automatically
the structure of a problem case is identical to that of a training case except that the solution part of the case is missing
as shown in the last column of table NUM four out of five rm recency variations posted higher accuracies than the combined recency representation
intuitively however it seems that very different subsets of the feature set may be useful for part of speech prediction and semantic class prediction
note that our algorithm has true runtime o tn3 as shown previously
we also note that bod s algorithm will probably be particularly inefficient on longer sentences
it also contains algorithms implementing the model with significantly fewer resources than previously needed
thus every stsg tree would be produced by the pcfg with equal probability
if every subtree in the training corpus occurred exactly once this would be trivial to prove
but in many cases one of these operations is unnecessary because only one candidate constituent may be included in the best parse tree
e.g. we can divide the states into classes generating the same outputs
otherwise we use word association model to determine the left boundary
the standard n gram approaches are special cases of using model merging and constraints
the models are tested on ntest NUM NUM words of the same corpus
a high quality translation dictionary is indispensable for machine translation systems with good performance especially for domains of expertise
figure NUM log perplexity of test part during merging
constraints same output until NUM NUM none after NUM NUM
there is a constant phase between NUM and NUM NUM merges
note that japanese word is translated into different english words according to their occurrences with distinct word
we want this probability to stay as high as possible
japanese compound nouns and unknown words are detected by the morphological analysis stage and are determined before the later processes
kay rbscheisen used the following dice coefficient for calculating the similarity between english word we and french word w i
however the target noun phrases and unknown words are decided in the preprocessing stage
number of other interesting and meaningful expression that should be translated in a specific way
deciding the similarity measure in this way reduces the computational overhead in the later processes
the step numbering of the following procedure corresponds to the numbers appearing in figure NUM
the cawg will be able to evaluate the proposed changes and make suggestions to the se cm or the architecture committee
if the reviewer is not the cawg the se cm will keep the cawg informed of the issue under discussion
a software architecture engineer will assist the architecture committee chair by performing certain cm tasks
tipster configuration management procedure cmp i configuration identification tbd prc inc
if a member is unable to attend a meeting he she must designate a representative to attend in his her place
when the problem is fixed the erb will determine the implementation schedule of the fix into the affected baselines
erb decisions are recorded on change directives a copy of which is sent to the ccb for information only
it is expected that submissions will be made by application reviewed by the se cm and approved disapproved by the architecture committee
NUM any requests for change rfc to the tipster architecture to cover the discrepancy or deviations
the ccb chair will conduct the meeting and render the final decision to the course of action to be taken
comprehension grammars for a sample of ten languages english dutch german french spanish catalan russian chinese korean and japanese were derived by machine learning from corpora of about NUM sentences
in conclusion both theoretical arguments and experimental results support the choice of the dice coefficient over average or specific mutual information for our in champollion
we selected a set of NUM collocations with mid range frequency identified by xtract and we ran champollion on them using sample training corpora databases
the table has a format similar to that of table NUM x represents an english collocation credit card or affirmative action and y
more precisely for a given source collocation champollion initially identifies a set s of k words that are highly correlated with the source collocation
the algorithm we use is based on statistical methods and produces p word translations of n word collocations in which n and p need not be the same
this can be shown by replacing p x NUM y NUM on the right side of equation NUM NUM
in order to produce a fluent translation of a full sentence it is necessary to know the specific translation for each of the source collocations
when moving to a new domain and sublanguage translations that are appropriate can be acquired by running champollion on a new corpus from that domain
note that this information is added during postprocessing after the translation has been selected and takes very little time to compute because of the indexing
what is important is that the selected measure satisfy the conditions of asymmetry insensitivity to marginal word probabilities and convenience in testing for correlation
the primary building blocks for the cckg are the temporary graphs built from the dictionary definitions of those words using our transformation process mentioned in the previous section
by means of adjunction complete derivations can be extended to bigger complete derivations
subscripts are used to indicate the label on a node e.g. x
however this still does not take full advantage of the context freeness of tig
relabel the foot of t with e turning t into an initial tree
by convention substitutability is indicated in diagrams by using a down arrow d
this could be valuable when processing a language with a fundamentally left recursive structure
furthermore the trees created by the ltig procedure have an extremely repetitive structure
ellipsis structures are taken to be sequences of lexically realized arguments and or adjuncts of an empty verbal head
b the students said that john sent invitations to the professors yesterday and to each other today
ellipsis structures pose an important problem for nlp systems designed to provide text understanding or to handle dialogue
r x y bill wrote x for the journal during yl
NUM indicates that it is possible to obtain an unbounded number of bare adverbial adjuncts in an ellipsis site
but if the system is trying to deduce the implicit rules the user is responding to to make the fills then the system is automatically constructing an information extraction system as well
once the variables subj head obj prep and pobj are defined by the user they are plugged here all the arguments are optional
fastspec has made it immensely easier for us to specify grammars and recently it has become one of the principal influences on the tipster effort to develop a community wide common pattern specification language
the false positives all talked about retaliation against terrorists but it was embedded in negative or modal contexts such as the following will not retaliate against the terrorist attack
this can be accomplished by having a two window editor the text being annotated or analyzed is in one window the template in the other
you ca n t tell from the fact that an entity is a person whether he is going into or out of a position at an organization
during the preparation for muc NUM it took us only about one day to implement the necessary clause level domain patterns because of the compile time transformations
the semantics in these rules sets the features of active aspect tense and negative appropriately and sets head to point to the input object providing the past participle
in the example of section NUM we learn from clause level event recognition that garrick will become president and co0 and we learn that he will succeed costello
ing methods namely lower f0 maximum and average lower rms maximum and average faster speaking rate shorter preceding pauses and longer subsequent pauses
we examine the effects of speaking style read versus spontaneous and of discourse segmentation method text alone versus text and speech on the nature of this relationship
but it is not always true
splat also contains a sentence bank a user extensible collection of sentence plans annotated in various ways
table i shows how splat would annotate a sample sentence
for authoring NUM NUM the input to penman sentence plan language
splat uses an example based approach in the form of sentence plan
templates to aid the user in creating and maintaining sentence plans
previously constructed spl plans provide models that may be modified or incorporated into the new plan
currently a graphical upper model browsing tool has been implemented and other resources are being developed
splat stores the pre built spl plans for a set of sample sentences in a sentence bank
an important feature of splat is that it provides a new view of the upper model
only the allowed merges are tested for each step of merging
thus each step of merging is o NUM
only states belonging to the same class are allowed to merge
null the second method is a variation of the first method
these measures are used to determine the quality of markov models
one step of merging can be seen in figure NUM b
this method does not need a pre annotated corpus for parameter estimation
figure NUM log perplexity of training and test parts when starting with a bigram model
the bigram model yields a markov model wit h NUM NUM states
in classic the same as restriction is limited so that either both attributes must be filled already with the same instance or the concept must already be known as a legal linking
a linking is legal when at least one of the events associated with the verb can be linked in the indicated way and all the required arguments are filled
for example the following three rules are inconsistent since feature1 of np and feature1 of vp would not unify in rule NUM given the values assigned in NUM and NUM
however the lack of negative examples still poses a problem and would require the project linguist to create appropriate negative examples or manually adjust the class definitions for further differentiation
problems with consistency and completeness can arise when writing a wide coverage grammar or analyzing lexical data since both tasks involve working with large amounts of data
third the classifier can identify the need for new verb classes by flagging verbs that are not members of any existing defined verb classes
first the process of representing the system in a taxonomic logic can serve as a check on the rigor and precision of the original account
while the solution of several knowledge acquisition issues would result in a friendlier tool for a linguistics researcher the tool still performs a useful function
i have shown how a terminological language such as classic can be used to manage lexical semantics data during analysis with two minor extensions
this method extracts all possible kakari uke pairs and then rather than generate not m1 or some possible sets of pairs only one best set of pairs is generated while still retaining all other possible tairs
most words in hiragana are functional words NUM such as postpositional particles auxiliary verbs and inflective suffix NUM of verbs and others table NUM
qjp is small fast and robust because NUM dictionary size less than1 100kb and required memory size 260kb are very small NUM analysis speed is fast more than NUM words see on NUM pc and NUM even a NUM word long sentence containing unknown words is easily processed
in addition there are several NUM kanji chaxacter stems of kami l dan verbs a tj sahen verbs goj and adjcctives f l which axe stored in the dictionalsr because they rc so few in nmnber
analysis strategies are the followings the morphological analysis is achieved by expanding an earlier methods ill2 for bunsetsu or word segmentation using character types thus mlowing the use of a very small dictionary
sharing of morphemes by dictionary and rules our strategy is that all functional words wmch are few in nmnber are stored in the dictionary and most content words or their stems in kanji or katakana are to be extracted and given their i ar ofo speech candidates based on character types
last it selects the best uke bunsctsu modifice marked by in c from possible ones for each bunsetsu which is a kakari bunsctsu modifier except tim last one because every bunsetsu modifies one of the following bunsctsus so the last one has no uke bunsetsu
when presented with a french sentence f candide s task is to find the english sentence e which is most likely given f
we can simplify this task by holding a a0 constant and performing a line search over the possible values of the new parameter a
in section NUM we describe the mathematical structure of maximum entropy models and give an efficient algorithm for estimating the parameters of such models
the maximum entropy principle presents us with a problem in constrained optimization find the p e c that maximizes h p
for all but the most simple problems the that maximize can not be found analytically
figure NUM illustrates the change in log likelihood of training data l r p and withheld data l h p
if there are ivei total english words and ivy i total french words there are vfl template NUM features and ivei
lexico grammatical decisions can be made by reference to this information tailoring the language to the speaker s and hearer s descriptions
participants who is uttering the speech act the speaker and who is it addressed to the hearer
since the information to be expressed is already present in the kb why does it need to be re expressed in the semantic specification
this approach is taken because penman was designed with monologic text in mind so the need for varied speech acts is not well integrated
figure NUM shows a sample speech act specification from which the generator would produce i d like information on some body repairers
wag s input specification also allows a wider range of specification of the speech act type than used in penman and other sentencegeneration systems
in particular a move responding to a wh question usually only needs to provide the wh element in their reply
sentences themselves serve an important part in interaction they form the basic units the moves of which interactions are composed
as stated above this approach does not require the sentence specification to include any ideational specification except for a pointer into the kb
however wag supports a second mode of generation allowing a higher degree of integration between the text planner and the sentence realiser
we a lso l otlltd s w q NUM NUM mistakes ht late olli s dclinil i ns e.g. severed adverbs ending ltt llt wcr classified a ij cl ives
the plots on the first row of figure NUM suggest that underdispersed types and tokens are also used more intensively in the last text slices
the residuals d k du k do not reveal any significant trends f NUM for both newspapers
note however that even for the corpus data we again find that the expectation of v n is consistently too high
first by randomly sampling individual sentences instead of sequences of sentences the effects of intra textual and inter textual cohesion will be largely eliminated
table NUM shows that m similarly leads to overestimation for alice in wonderland moby dick and max havelaar
both trends are significant according to least squares regressions represented by dotted lines f NUM NUM NUM NUM p NUM
lexical specialization informally defined as topic linked concentrated word usage and formalized in terms of underdispersion provides us with the required tool
consider again the potential sources for violation of the randomness assumption underlying the derivation of e v n
segmentation in chinese seems more difficult than in japanese
in addition we annotated some additional data
the algorithms rectangles in the figure were used in the two languages the only component difference was the new mexico state university segmenter used to find the word boundaries in chinese
the fact that the government released the revised training data very late in the cycle of met did not pose a problem since the system could be retrained so quickly with the updated training data
we also thank chao huang chang reviewers for the NUM acl conference and four anonymous reviewers for computational linguistics for useful comments
the second issue is that rare family names can be responsible for overgeneration especially if these names are otherwise common as single hanzi words
for each pair of judges consider one judge as the standard computing the precision of the other s judgments relative to this standard
previous reports on chinese segmentation have invariably cited performance either in terms of a single percent correct score or else a single precision recall pair
we can better predict the probability of an unseen hanzi occurring in a name by computing a within class good turing estimate for each radical class
as we have noted in section NUM the general semantic class to which a hanzi belongs is often predictable from its semantic radical
input lattice top and two segmentations bottom of the sentence how do you say octopus in japanese
two sets of examples from gan are given in NUM and NUM gan s appendix b exx
we thank united informatics for providing us with our corpus of chinese text and bdc for the behavior chinese english electronic dictionary
although the wu iations are slight the best value for the r lagfl c edom l arameter seems to be at an ambiguity level of NUM it seellls that the strategy of reducing the ambiguity as quickly as possible best rule first is better than following the ordering of the rules by the l earner
a proof for NUM is presented in the appendix
the effects of lexical specialization on the growth curve of the vocabulary
recall that the word ahab is unevenly distributed in moby dick
clearly syntax imposes severe constraints on the occurrence of words
figure NUM shows that intra textual cohesion within paragraphs is sufficient to give
we have also pointed out the importance of incorporating the notions of inheritance and other substructuring conventions in tagsets to reduce the size and complexity of the descriptions and to capture generalizations over natural classes
we generate a representative mini corpus and estimate expectations by counting in the mini corpus
gibbs sampling does not work for the application to av grammars however
discarding failed derivations and renormalizing yields the initial distribution po x
figure NUM the atomic features arising in dags generated by g2 figure NUM
thus the hapax based mle yields an estimate that is uncontaminated by the lexical properties of individual high frequency forms
turning to proper names we see that the hapax based mle is much larger than the overall mle
the question then is which of these mles provides a better estimate for low frequency types
this result has potential importance for various kinds of applications requiring lexical disambiguation including in particular stochastic taggers
also systematic ambiguity exists among cases of noun verb conversion for examplefluiten is either a noun meaning flutes or a verb meaning to play the flute spelden means either pins or to pin and ploegen means either ploughs or to plough
we argue that the best estimator is provided by computing the relative frequencies of the various functions among the hapax legomena the forms that occur exactly once in a corpus in particular a hapax based estimator is better than one based on the proportion of the various functions among words of all frequency ranges
syncretism and related morphological ambiguities present a problem for many nl applications where lexical disambiguation is important cases where the orthographic form is identical but the pronunciations of the various functions differ are particularly important for speech applications such as text to speech since appropriate word pronunciations must be computed from orthographic forms that underspecify the necessary information
basically a feature is typed binary relation extracted from an abstracted triple
original synset ids are maintained in this processing for feature extraction process
this model represents an integration of traditional methods i e
inherited feature based similarity measure based on large semantic hierarchy and large text corpus
the frequency is also preserved it is as an occurrence count
we call this strategy the brute force approach like resnik
these abstracted triples are sorted and merged
this is the same approach as that of the distribdted semantic models
a wider context gives a precision to the contents of the features
we find that the overall quality of the extracted word hypotheses is satisfactory al null though the values of recall and precision are not so high
we compared the results with a segmented japanese corpus and reported NUM NUM recall and NUM NUM precision for NUM sentences whose out of vocabulary rate is NUM NUM
it was collected to build a japanese electronic dictionary and contains a variety of japanese sentences taken from newspapers magazines dictionaries encyclopedias textbooks etc
table NUM shows for the small test set NUM sentences the segmentation accuracy of the various combinations of the segmentation models and the word models
tile reported recall and precision values were NUM NUM and NUM NUM for two character words and NUM NUM and NUM NUM for three character words respectively
electronic networks can be useful in many ways for language learners
since as noted above we would expect reliability to be much higher if there were seven subjects we believe that values above NUM for n NUM subjects indicate reproducibility
the boxes in the figure show the subjects responses at each potential boundary site if no box is shown none of the seven subjects place a boundary at the site
we have shown that an atheoretical notion of speaker intention is understood sufficiently uniformly by naive subjects to yield highly significant agreement across subjects on segment boundaries in a corpus of spoken narratives
we have already discussed how variable the subjects responses are both in number and placement of segment boundaries so we know that our subjects are not replicating the same behavior
the segmentation task reported here is not properly a classification task in that we do not presume that there is a given set of segment boundaries that subjects are likely to identify
when we look at correlation of segment boundaries with linguistic features we use both thresholds tj NUM and tj NUM to select a set of empirically justified boundaries
in section NUM NUM we test an initial set of algorithms for computing segment boundaries from a particular type of linguistic feature either referential noun phrases cue phrases or pauses
the nature of any hypothesized interaction between discourse structure and linguistic devices depends both on the model of discourse that is adopted and on the types of linguistic devices that are investigated
the final versions of the lexical entries will encompass full semantic syntactic valence descriptions where the elements of each feg associated with a verb sense will be linked to a specification of sortal features
aml iguous sul jectobject assignmenl s in i t a lia n
i rolu i he ollins li a a n li nglisil
in the presence of sets of mutually left recursive rules involving more than one nonterminal allowing increased ambiguity can yield significant reduction in the number of elementary trees
an overview of thai morphological analysis with a gradual refinement module
rcb lcb think verb rcb lcb so much modifier rcb
both word boundary and tag ambiguity also create complexity in syntactic analysis
at this stage a temporary dictionary is created for the remaining steps
we tested the system on pc NUM dx2 by using two hundred sentences corpus
this causes a lot of alternative chains of words where some are meaningless
it consists of wordboundary preference syntactic coarse rules and semantic strength measurement
thai word can have more than one part of speech
an overview of the gradual refinement module will be given
the linguistic expressions that refer in the text
this low value shows that the initial model is very specialized in the training part
two different ways of learning class based rules
a a greedy algorithm for aligning words
NUM NUM preliminary details sensealign is a class based
model merging finds a model with NUM states which assigns a log perplexity of NUM NUM to the test part
consequently incomplete or incorrect alignments occur
we refer to this algorithm as sensealign
it is effective for specific linguistic reasons
this method is commonly used in speech recognition and known as word bigram or word trigram model
the rules that provide the most instances of plausible connection is selected
NUM NUM acquisition of alignment rules class based
these merges do not change the corpus probability and thus are the first merges anyway
yet the question when to discard a constraint to achieve best results is unsolved
overall lockheed martin has demonstrated improvements in the technology to process texts and to do extraction the ability to move the research into a realistic setting and the ability to develop systems conforming to the architecture
the top errors of the model over the training set are shown in table NUM clearly the model has trouble with the words that and about among others
the running time of the search procedure on a sentence of length n is o ntab where t a are defined above and b is the beam size
the behavior of a feature that occurs very sparsely in the training set is often difficult to predict since its statistics may not be reliable
given hi as the current history a feature always asks some yes no question about hi and furthermore constrains ti to be a certain tag
all experiments use a beam size of n NUM further increasing the beam size does not significantly increase performance on the development set but adversely affects the speed of the tagger
a maximum entropy model is well suited for such experiments since it cornbines diverse forms of contextual information in a principled manner and does not impose any distributional assumptions on the training data
a model trained and tested on data from a single annotator performs at NUM higher accuracy than the baseline model and should produce more consistent input for applications that require tagged text
the performances of the baseline model on the development set both with and without the tag dictionary are shown in table NUM
figure NUM a simple i1 grammar with no lp constraints the right hand sides of id rules that will later have to be linearized by collecting them in a partially ordered list
figure NUM gives a learning cycle starting rein the sibling list ele nent det nun adj n nu
though no contradictions may u isc with acquired rules they may come from lp s declared by the user it the case when the system is started with some such lp s
at tile outset the prograrn is supplied with the specific h grallhl lar whose l p rules are to be acquired and the user provided bias of the
our instance space will consist of all strings generable by the given id grammar the size of this instance space for any non toy grammar will be very large
we also nee i to emphasize that the program selectively rather than randomly wries the potentially relevant parameters munber and precedence in this particular case ttempting to converge the
null it is known that the version space method misbehaves on encountering noisy data an instance mistakenly classed as negative e.g. may lead to premature pruning of a search branch where the solution may actually lie
the NUM rocess results in the three l p rules det num adj adj num and det n n
a program which learns from examples usually reasons from very specific low level instances positive or both positive and negative to more general high level rules that adequately describe those instances
in the fall government language teams retrieved training and test texts with multilingual software for the fast data finder fdf refined the muc NUM guidelines and manually tagged NUM training texts using the sra named entity tool
this process of alignment is called mapping and relies on the text being overlapping at least in part and in cases where more than one mapping possibility exists the software optimizes over the f measure for that piece of information
recall a measure of completeness is the number that the system got correct out of all of those that it could possibly have gotten correct and precision a measure of accuracy is the number of those that it got correct out of the number that it provided answers for
figure NUM an application of check in which no is the action indicating that the proposed constituent in figure NUM is not complete
each feature fj corresponds to a parameter aj which can be viewed as a weight that reflects the importance of the feature
however we would not want to build a term with n i arguments where n is the number of verbs in english
these values will be satisfied if and only if all the members of the set have been encountered in any order
examples of this might be on some analyses the german middle field and some types of coordination
NUM we assume a category kleene with three category valued features finish kcat kleene category and next
nevertheless for many purposes where efficiency of processing is at a premium it could be worth living with this limitation
it pops selectors off the list each time it applies so that the correct positional encoding is available for the next application
it can also be combined with the preceding treatment of linear precedence to enforce a partial ordering on members of the set
carpenter s lattices are upside down and so for him unification is least upper bound and so on
NUM NUM NUM verification method demonstration and inspection
the architecture shall address document detection and information extraction
the architecture shall provide for interchangeability
the architecture shall provide for modularity
next consider the english past tense versus past participle ambiguity
this proportion is also highlighted by the dashed horizontal line
baayen and sproat lexical priors for low frequency forms the verb
NUM NUM dutch words in en a more general problem
figure NUM shows that this prediction is not borne out
finally consider the dutch verb forms en that we started with
again it is the hapax based mle that proves to be superior
hence for the low frequency ranges the data is weighted towards nouns
this process begins at the leaves of the tree and works its way to the root
the client accommodated to the agent using the words in common more frequently in subsequent conversation than did the agent
we would like to investigate the possibility of increasing the level of accommodation in the machine mediated setting
the mergers are performed in a nested loop over the states of the initial tree transducer
as our initial results show it is not simply a coincidental byproduct of conversing about common topics
so concern for social standing and communicative efficiency combined to generate a high rate of mutual accommodation
it is not possible to say that one or the other interlocutor was responsible for the accommodation found
there was no significant difference between client and agent in the hnman interpreted setting or in the machine interpreted setting
we discuss experiment results from three conversational settings human human monolingual human interpreted bilingual and machine interpreted bilingual
we found significant accommodation in all three conversational settings with the highest rate in the human interpreted setting
these advantages may be lost if humans are encouraged to treat a machine interface as if it were human
lexical accommodation is only one instance of a diverse range of convergence behaviors that humans display in conversation
however they list two sets one consisting of NUM fragments and the other of NUM fragments in which they had NUM recall and precision
in many cases these failures in recall would be fixed by having better estimates of the actual probabilities of single hanzi words since our estimates are often inflated
however as we have noted nothing inherent in the approach precludes incorporating higher order constraints provided they can be effectively modeled within a finite state framework
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 major problem for all segmentation systems remains the coverage afforded by the dictionary and the lexical rules used to augment the dictionary to deal with unseen words
approaches differ in the algorithms used for scoring and selecting the best path as well as in the amount of contextual information used in the scoring process
finally we model the probability of a new transliterated name as the product of ptn and ptn hanzii for each hanzii in the putative name
the transitive closure of the dictionary in a is composed with id input b to form the wfst c
given that part of speech labels are properties of words rather than morphemes it follows that one can not do part of speech assignment without having access to word boundary information
first a set of key terms ranging from single word terms to four word terms are automatically generated and organized in a hierarchical structure out of a text dataset which represents a specific topic
the design of the gui component relies on a number of well understood elements which include a suggestive graphic design and a direct manipulation metaphor to achieve an easy to learn user interface
as the amount of on line text grows at an exponential rate developing useful text analysis techniques and tools to access information content from various electronic sources is becoming increasingly important
the ultimate goal of this prototype system is to offer an automated toolkit which allows the domain expert or the user to visualize and examine key terms in a large information collection
the retrieval of the key terms is treated as an iterative process in which the user may select single world terms from the term hierarchy and navigate to multiple word terms accordingly
for system training approximately half an hour of continuous speech recorded from a single speaker is required along with its orthographic transcription
the first stage is the phoneme segmentation of logatoms yielding a start point transition center and end point for each phone
in order to guarantee optimal synthesis quality a neutral phonetic context in which the diphones needed to be located was speeitied
acoustic differences between stored and requested segments its well as acoustic discontinuities at the boundaries belween adjacent segments have to be minimized
likewise coarticulalions are strongly subject to speaker s lluency so that imposing a slow speaking rate results in more intelligible units
however it can be used as a segmentation outline the retinement of which has to be performed by a human expert
in order to avoid the tedious time consuming manual segmentation of logatoms an automatic procedure based on hmm models is considered
a concatenative td psola diphone synthesis technique was used allowing high quality pitch and duration transformations directly on the waveform moulines9
speech signals were recorded by a close talking nilcrophone using a sampling rate of NUM khz and NUM bit linear aid conversion
the acoustical analyser delivers every milisecond a set of mel fi equency cepstral coefficients along with their slopes plus the energy of each frame
steps NUM and NUM are performed on each language corpus separately
quantifier scope pronouns antecedents just as the mapping froin f structure to qlf did not attempt to fill in values for these recta variables
while recall is quite comparable to human performance row NUM the precision is low while fallout and error are quite high
boundary NUM NUM there are three little boys NUM NUM up on the road a little bit NUM NUM and they see this little accident
the instructions given to the subjects were designed to have as little bias as possible regarding segment size and total number of segments
our statistical results indicate that despite the freedom of the task naive subjects independently perform surprisingly similar segmentations of the same discourse
for example pauses tended to precede phrases that initiated segments independent of hierarchical structure and to follow phrases that ended segments
testing the new algorithms on a new data set shows that when multiple sources of linguistic knowledge are used concurrently algorithm performance improves
however there is only weak consensus on what the units of discourse structure are or the criteria for recognizing and generating them
for each set of eight subjects we created two randomly selected partitions a and b with four distinct subjects in each
these results as well as the improved performance of the additive algorithms suggest that performance can be improved by considering more features
recall is worse than pause cue and human performance and precision is better than pause and cue but worse than human performance
signal the structure of a discourse
the summed deviation for perfect performance is
inferential link due to implicit argument
all scores are better for ea
as input information about referential nps
no boundaries are assigned by np
then learned decision tree for segmentation
passonneau and litman discourse segmentation table NUM
constructing semantic tagsets NUM goal of the session
are semantic tags necessarily hierarchically organized
can semantic tagsets which are language specific be used in muitilinguai natural language processing to establish correspondences between lexical units of different languages NUM organization of the session in order to fuel the debate i will first make available to participants a list of lexical units of a number of languages at least english and french whose semantic tagging seems to me to raise interesting problems and questions
if so what should tag hierarchies look like
all participants with a message to deliver regarding the above mentioned problems are invited to make a very short presentation of the semantic tagset they are using or developing and how it offers answers to those problems or why it fails to do so
the purpose is not so much to decide who has got the right solution
participants are invited to study this list before the session starts and prepare any contribution they may wish to make to the discussion based on the following questions what would be your own particular solution to the problems posed
what are the main methodologies available for the construction of semantic tagsets
if the session proves fruitful and i am sure it will i will propose to those interested in the enterprise to later work on the writing of a summary of all the questions we raised all solutions we came up with and all conclusions we drew
as hypothesized in the introduction better features on the context surrounding that and about should correct the tagging mistakes for these two words assuming that the tagging errors are due to an impoverished feature set and not inconsistent data
the generation of features for tagging unknown words relies on the hypothesized distinction that rare words NUM in the training set are similar to unknown words in test data with respect to how their spellings help predict their tags
the probability model the probability model is defined over NUM x NUM where 7t is the set of possible word and tag contexts or histories and t is the set of allowable tags
due to the availability of large corpora which have been manually annotated with pos information many taggers use annotated text to learn either probability distributions or rules and use them to automatically assign pos tags to unseen text
thus a model parameter aj effectively serves as a weight for a certain contextual predictor in this case the suffix ing towards the probability of observing a certain tag in this case a vbg
even though use of the tag dictionary gave an apparently insignificant NUM improvement in accuracy it is used in further experiments since it significantly reduces the number of hypotheses and thus speeds up the tagger
we have found it necessary to include all of these elements for our purposes even though some of them are so closely related that they are unlikely to be given separate instantiation in the same clause
our inclination however is to maximize the separation of frame elements at the beginning and to postpone the task of producing a parsimonious and redundancy free description until after we have completed our analysis
however our preliminary research indicates that it would be difficult and undesirable to exclude metaphorical uses if only because the metaphorical uses can often shed light on the structure of the core uses
the length of the output string associated with a transition of a subsequential transducer is unconstrained
weak prediction power a case frame tree with word forms does not have high prediction power on the open data
this table shows an imaginary relationship between an object noun of the verb take and the japanese translation
some linguistically based heuristic for ordering states might produce more consistent results on different types of phonological rules perhaps by reordering the remaining states as the initial states are merged
recall that the failure of the algorithm on r deletion shown in table NUM was not due to the difficulty of deletion per se since our algorithm successfully learns the t deletion rule
one problem with these particular approaches is that since the decision tree for each segment is learned separately the technique has difficulty forming generalizations about the behavior of similar segments
note that the use of alignment information in creating the initial tree transducer dramatically decreases the number of states in the learned transducer as well as the error performance on test data
the t links all such possible nodes with arcs and the traversal node sets can exhaust t
the only difference between our result and the hand drawn transducer in figure NUM is the transition from state NUM upon seeing a stressed vowel this will be discussed in section NUM
1degif the total frequency of the majority translation exceeds NUM of the total translation frequency subtree generation halts
if the grammar size is small and the comparison is between NUM NUM seconds and NUM NUM seconds this might be of no practical importance
if this test succeeds then the interpreter attempts to satisfy the rule s contextual tests in the context of the candidate
where a is a coefficient gw p is the penalty for generality and e p is a i enalty for the induced errors by using p
as was mentioned in section NUM the number of ucc node set in a tree tends to be gigantic and we should obviously avoid an exllaustive search to find the optimum generalization
in both cases the learned case frame trees are expected to have reinforced prediction power on the open data thanks to the semantic classes the replacement in the table generalizes the case frame tree
we will not give a full proof for propositions NUM and NUM correctness of t because of the limited space but give an intuitive explanation of why the two propositions hold
low legibility if we include many different nouns in the training data the data used for learning the obtained tree will have as many branches as the number of nouns
figure NUM case frame tree learned by lasa NUM
the data not used for learning
each of the other four unsuccessful attempts resulted from a common sequence of events after the system proposed an inefficient route word recognition errors caused the system to misinterpret rejection of the proposed route as acceptance
specifically we are adding distances and travel times between cities several new modes of transportation trucks and planes with associated costs and simple cargoes to be transported and possibly transferred between different vehicles
the speech recognition output is passed through the post processor described in section NUM the parser described in section NUM accepts input either from the post processor for speech or the display manager for keyboard and produces a set of speech act interpretations that are passed to the discourse manager described in section NUM
although more sophisticated translation strategies are certainly possible bible percent correct scores for cascaded lexicons suffice to test the utility of data filters for machine translation
bible s objective criterion is quite simple with the drawback that it gives no indication of what kinds of errors exist in the lexicon being evaluated
the word alignment filter removes enough noise to capture high vast giant and extensive all at once
so the product of recall and precision percent correct is a good indication of a lexicon s suitability for use with such a system
for each pair of translations the fl action of times by type that identical words were used in corresponding paragraphs was computed
a bilingual text corpus of canadian parliamentary proceedings hansards was aligned by sentence using the method presented in gal91b
to maximize the filter s effectiveness tag sets must be remapped to a more general common tag set which ignores many of the language specific details
this paper shows how to induce an n best translation lexicon from a bilingual text corpus using statistical properties of the corpus together with four external knowledge sources
this cascaded back off strategy maintained the recall of the baseline lexicon while taking advantage of the higher precision produced by various filter cascades
the word alignment filter is particularly useful when oracle lists are available to identify a large number of translation pairs that can be used to partition sentences
here pi stores the first byte and p2 stores the second byte of two byte characters in p if there are single byte characters in p they are stored in pl and the data in corresponding positions of p2 are undefined
this implies that a matching NUM byte characters is carried out where the data in t i NUM is the second byte of the character line NUM and b i is incremented by NUM instead of NUM because it is counting in terms of bytes line NUM
a search through the chinese law text for p h will require many backing up or committing a false start in the brute force implementation when words or phrases like d k ffs c quot deg fi g cmdegokdav and c c d k are encountered
initially the state of the automaton m is set to NUM the next state is determined by the current character read from the text string t at position i and the current state
these algorithms derived from bm assumes that knowing the positional index i of the text string NUM can access and interpret the data t i as a character
if it is a single byte character the standard kmp algorithm operates for that single byte character t i in line NUM to NUM otherwise i is pointing at a two byte character
the function f maps two byte characters into single byte characters simplifying the generation of values in the array next and the failure links in fl
however with a text string of single and multi byte characters i can point to the first byte or the second byte of a NUM byte character which the computer can not determine in the middle of the text string
hence does this road cross any small towns
deictic reference was implemented similary to eucalyptus the difference being that we chose to implement the conventional notion of current selected object objects clicked by the mouse are highlighted and remain so until deselected
only reports back towns that are currently visible in the display based on the assumption that this is the user s current focus of attention this constraint applies only to quantified nps and not proper names i.e.
the user can also select a set of closely adjacent objects with a double click and then as in eucalyptus use verbal context this town what s the population here to resolve the reference
again we choose rule NUM we are instructed to label the NUM child a but it already has that label so we do not need to do anything
when the word node in the conceptual network becomes activated by activation spreading from the character node more top down word codelets will be posted
in this figure we see that affinity relations are built earlier than words reflecting the system s preference for words of greater lengths
null the initial agenda including active edges and collecting edges by the vertices that they are incident from is given in NUM
one way to think of it therefore is as a parser of structures or logical forms that delivers analyses in the form of strings
the label on this edge matches the first item on the right hand side of rule NUM and the active edge that we show in the second entry is also introduced
the grammar is consulted only for the purpose of creating active edges and all interactions in the chart are between active and inactive pairs of edges incident from the same vertex
john is the name of the entity that stands in the argl relation to the running which took place in the past and which was fast
although utterance level ambiguities must be considered in tile context of whole utterances a sequence like international telephone services is ambiguous in the same way in utterances l and NUM above
then title is considered the increase of the recall shows that title works well but it decreases the precision too
there are many proper nouns in the international section and ahnost all of them are not included m the dictionary
b word s here a word is composed of at least two characters
when the former two types are regarded as a category the performance becomes NUM NUM NUM NUM
c the entertainment section there are many items of news about tv stars programs and so on
the gender information i.e. type NUM is always a female helps us disambiguate the type of personal names
because keyword is a general content word we need other strategies to tell out its exact meaning
NUM incomplete organization names a structure these organization names often omit their keywords
turn is optional and should be inserted to close the list of utterances that is if the next paragraph contains only one utterance and does not begin with parag
figure NUM effect of number of features on accuracy
the ability to test new ideas without having to develop all the components of an application is particularly attractive to the r d researcher
the output of an information extraction component may include a set of document annotations and optionally one or more lists of filled templates
this is a library of templates already defined and used by tipster applications NUM NUM verification method demonstration and inspection
supporting details for those requirements considered to be an internal part of the architecture will be found in the remainder of this document
basic document control includes the maintenance of document collections document lists correction and version records ownership record access control and security
when possible the source documents shown as n indicate the basis for the tipster requirement NUM derived requirement
guidelines for electronic text encoding and interchange tei p3 vol NUM NUM ach acl allc b
the architecture shall allow an application to create a processing log which records each individual process step of an application run and any related errors
such a high levcl language would allow the construction of applications by use of apis corresponding to various modules and components of the architecture
a multi lingual retrieval or routing application may have a separate module for each language it handles but builds an index that is language independent
the architecture shall allow the grouping of documents based upon common occurrences of specific strings nearly identical passages of text or similar template objects
the probability of the tree rooted with s and constructed at step NUM of this reduction must then bep0 NUM NUM i lpl
the probability of the elementary trees of root ck step NUM is NUM and of root ui or ul step NUM is NUM NUM
an exponential algorithm can be comparable to a deterministic polynomial algorithm if the grammar size can be neglected and if the exponential formula is not much worse than the polynomial for realistic sentence lengths
more specifically from our representation of communicative authors appear in alphabetical order
this work was partially funded by copernicus project no NUM speak
other approaches to intonation suggest a different number of tones ranging from four to six
more delicate speech functional distinctions specific to spoken german are realized by means of tone
the added discriminations to the komet grammar impose constraints on the specification of an appropriate intonation contour
an exchange structure consists of moves which are the units for which the speech function network holds
it suffices to generate simple phrases like thanks or ok
it can be proven that the erf weights are the best weights for a given context free grammar in the sense that they define the distribution that is most similar to the empirical distribution
each of these stages is modeled as a statistical process
these estimates are then smoothed to overcome sparse data limitations
dd l show that we can solve equation NUM if we can estimate qold f k for k from NUM to the maximum value off in the training corpus
the mle estimate of this probability would be
a simple average for triples would be defined as
the algorithm is then as follows NUM
for this reason the two methods deserve closer comparison
then choose noun attachment else choose verb attachment
some key results are summarised in the table below
syntactic constraints at the level of the sentence introduce many restrictions on the occurrence of words
not mentioned by name in one text slice only as the hubert labbe model would have
complete or partial interactive disambiguation foliowing a best possible automatic disambiguation is an attractive way to raise quality and reliability
how can a system determine whether it is important or not for the overall communication goal to disambiguate a given ambiguity
we call this an ambiguity kernel as opposed to ambiguity occurrence or ambiguity for short
the end of the scope of each ambiguity occurrence is indicated in the text by a bracketed number which identifies its ambiguity kernel
if there is an ambiguity of segmentation in paragraphs or turns there may be more labeled paragraphs or turns than in the source
for example a i1 b i1 c may give rise to a biic and aiib c and not to a b c and aiibiic
interactive disambiguation technology must be developed in the context of research towards practical interpreting telecommunications systems as well as high quality multitarget text translation systems
it should be clear and simple enough for linguists to do the labeling in a reliable way and in a reasonable amount of time
the status expert system interpreter user expresses the kind of supplementary knowledge needed to reliably solve the considered ambiguity
means the absence of attribute ref NUM or any brmal representation system then we must specify what the necessary information is
however these negative results may have a completely different origin
the response by most linguists and instructional scientists to this state of affairs has been misguided
human language technology can modernize writing and grammar instruction
secondly modern language pedagogy stresses communicative success rather than formal correctness
nlp software holds the potential of alleviating this burden considerably and even of outperforming teachers in the speed and quality of feedback to learners and in the capability of generating well targeted and attractive exercises
more congenial with these priorities were multimedia innovations
language engineers take up the challenge seize the opportunity
reasons for this state of affairs are not hard to find
grammar rules referencing these terms are hard to apply successfully in written composition
however language proficiency includes more than conversational skills alone
radical c root measure pa el
a lexical string maps to a surface string iff NUM they can be partitioned into pairs of lexical surface subsequences where each pair is licenced by a rule and NUM no partition violates an obligatory rule
given a list of lexical strings lex and a list of lexical pointers lexptrs the predicate lexical transitions q lex lexptrs new lex ptrs lexc ats succeeds iff there are transitions on lex from lexptrs it returns newlexptrs and the categories lex cats at the end of morphemes if any
this paper NUM presents the algorithms behind semhe NUM discusses the issues involved in compiling non linear descriptions and NUM proposes extension solutions to make writing non linear rules easier and more elegant
in addition a number of issues which arise when developing non linear grammars are discussed with examples from syriac
two level predicates are converted into an internal representation NUM every left context expression is reversed and appended to an uninstantiated tail NUM every right context expression is appended to an uninstantiated tail and NUM each rule is assigned a NUM bit precedence value where every bit represents one of the six lexical and surface expressions
in analysis surface expressions are assigned the most significant bits while lexical expressions are assigned the least significant ones
there were NUM NUM words in the training data and NUM NUM words in the evaluation data
whenever the sentence analyzer encounters an ambiguity it creates a problem case automatically filling in its context portion based on the state of the natural language system at the point of the ambiguity
the experiments described below employ the following case retrieval algorithm NUM compare the problem case x to each case y in the case base and calculate for each pair
based on the experiments described in this section we can conclude that the overall accuracy of case based learning of linguistic knowledge depends to a large degree on the feature set used in the case representation
using linguistic and cognitive biases for feature set selection we saw in the last section that the performance case based learning algorithms degrades when features irrelevant to the learning task are included in the underlying instance representation
not surprisingly the accuracy of the cbl algorithm increases when task specific subsets of the original feature set are used instead of all of the available features see the last column of table NUM
next the case retrieval algorithm compares the problem case to those stored in the case base finds the most similar training case and then uses the class information to resolve the current ambiguity
correct s indicate significance with respect to the original baseline result shown in boldface p NUM NUM cards all but the n selected features from the case representation
then for each test case the system randomly chooses n features from the normalized feature set sets the weights associated with those features to one and sets the remaining weights to zero
a number of methods were evaluated on this pattern according to the NUM sample scheme described above
the objective of this kind of activity is to incite the user to build simple sentences on a theme and to develop the child s ability for naming categorising or generalising an idea
this mode is only possible in the case where the linguistic coverage is reduced no relative for example and it is very useful for the child to discover the abilities of the system
much of the previous work in this area assumes independence between the linguistic features
the parameters for all three unknown word models were estimated from the training data
and second noun level NUM the loglinear model also includes the variables preposition and pp object tag
we will now turn to the empirical evidence supporting the argument against independence assumptions
an anomalous string such as zt lt g past midnight in which the kanji was used rather than the numeral NUM caused problems for some systems
and although bureaucratic descriptors like indicate japanese ministries often well known ministries such as l j miti are aliased l j without mention of the canonical form
however the reference to u s in j u s dept of state was considered an integral part of the organization name and not therefore segmented and tagged separately
i tag type dry run tag type test money NUM percent NUM percent NUM money NUM date NUM date NUM location NUM time NUM person NUM location NUM time NUM person NUM org NUM org NUM easy tag types numex timex and time were also limited
as with the spanish and chinese groups table NUM japanese systems automatically marked the names of organizations people and places within entity name expressions enamex dates and times within time expressions timex and percents and money within number expressions numex
for example sej could be the name of a particular factory miyata factory or a generic factory located in miyata similarly j i i could be the new hyogo bank the new e.g. rebuilt hyogo bank or a new hyogo bank i.e. one bank in the hyogo bank chain
we discussed the construction of a practical parser for i ag that can handle these ases of coordination
the algorithm relies on a tree traversal that scans the input string from left to right while recognizing the application of the conjoin operation
this has been usually accomplished using statistical methods often coupled with manual encoding that a select terms words phrases and other units from documents that are deemed to best represent their content and b create an inverted index file or files that provide an easy access to documents containing these terms
hence while we refer to the contraction set of an elementary tree it does not have to bc stored along with its representation
while substitution and adjunction take two trees to give a derived tree conjoin takes three trees and composes them to give a derived tree
secondly it treats conj x as a kind of modifier on the left conjunct x
NUM coordinating the vp nodes which are the least nodes dominating tile two contiguous strings
let the second projection of the pair minus the foot nodes be he substitution set
we will occasionally use the first projection of the elementary tree to refer to the ordered pair
iiowever no conditions on the construction of such a structm e was given
for example let o eats be the tree selected by cats
figure NUM derived tree for john gave mary a book and susan a flower
the second projection of this ordered pair is used here for ease of explication
smerging in the graph theoretic definition of contraction involves the identification of two previously distinct nodes
the new tree is denoted by lcb a eookcd lcb u NUM rcb
one would expect measure NUM s results to be high under any circumstances and it is not affected by the density of boundaries
once conversational move boundaries have been marked on a transcript kid argue that naive coders can reliably place moves into one of thirteen exclusive categories
the specific level and scope of operational statistics is application dependent but may include document management detection and extraction statistical items
the architecture shall allow viewing of documents to commence before a query operation is completed if appropriate for the particular detection component
NUM NUM the architecture shall permit complete templates template objects and patterns to be stored in their respective libraries
also it should be possible to obtain all annotations associated with a particular document location through specific begin and end location values
the architecture shall provide for the use of a common template object library that can support various document processing tasks in different applications
he total partial opposition is used to distinguish references to sets of elements from references to portions of sets
but the use of a definite noun phrase to refer back to the envelope would sonnd rather odd to a native speaker
in gist the semantic relations that are relevant in the definition of distinguishing descriptions have been identified through an accurate domain analysis
at every stage of the referring expressions generation process issues raised by multilinguality are considered and dealt with by means of rules customized with respect to the output language
if you are separated b your spouse i should send us this part of the form j properly filled in
according to this theory each text can be seen as a sequence of clauses linked together by semantic relations
when the language is more formal impersonal forms or indirect references are preferred the applicant inps dss
the linguistic form of the expression also varies according to the type of speech act that is to be realized and this justifies the asserting questioning distinction
there are two acoustic scores and four language scores
this is a relatively badly recognized example
the evaluation of the sublanguage method has to be done by comparing the word error rate wer of the system with sublanguage scores to that of the sri system without sublanguage scores
the system structure is shown in figure NUM
step NUM creates the final ltig by lexicalizing the auxiliary trees
the nonterminal symbols on the frontier are marked for substitution
recursive left recursive and centrally recursive respectively
the key force of the restrictions applied to tig in
the scanning rules match fringe nodes against the input string
an earley style recognizer for tig expressed using inference rules
schabes and waters tree insertion grammar parsing left anchored ltigs
each child labeled with a nonterminal is marked for substitution
however other forms might be more advantageous in some situations
this substantial data is added to the lexical information to be used for speech synthesis in cooperation with pronunciation and accent
this word corresponds to either the english conjunction when with neutral reading or if with conjecture modality
in goner m the scores are slightly higher using the siml le verb t g set over the complex verb
thus he used an optimal lexicon which contains all the words with only parts of speech which appeared in the corpus
with very small learning sets the system was unable to tlnd sulticient examples of phenomena to produce reduction rules with good coverage
the lower base accuracy in our exl eriment is probably due to the large number of entries in the collins dictionary
without tuned biases the c erman xerox tagger achieved NUM NUM while the french xerox tagger achieved NUM accuracy
il does not require a larg i s l agged t raining orl us
in the following test we used tile simi le verb tag set rules but varied the r tagfrccdom parameter and the scq parameter
prediction is achieved by determining the value of the response variable given the values of the explanatory variables
the grapher was designed to be extendible for future al plications
figure NUM architecture of a pm t of
allows user prograrns to be integrated at various levels
subject applied to verb phrase yes no
currently they rely on the tcl tk library package provided by sicstus NUM
the architecture is illustrated in figure NUM
e the economic section many items of news about stock market money and so on are recorded
as mentioned in section NUM NUM NUM the frequency of a character to be a part of a personal name is important information
some organization names are composed of proper nouns and content r iliii h j is made up of words
l complete organization names a structure this type of organization names is usually composed of proper nouns and keywords
because a tagger is not involved before identification the part of speech of a word is determined wholly by lexical probability
if all the characters in a string belong to this set i.e. they satisfy character condition they are regarded as a candidate
the following shows the statistics of the testing corpus a the political section there are many items of news about the legislature
sections NUM NUM and NUM propose tile identification and classification methods of chinese personal names transliterated personal names and organization names respectively
titus the greater structural complexity unnecessarily increases the
we wouhl like to a knowledg
syntactic analyses for parallel grammars auxiliaries and genitive nps
the analysis in NUM thus effcetiw ly
most of natural languages provide two types of lexical items to describe the motion of an entity with respect to some location motion verbs to run to enter and spatial prepositions from towards
for the following we will focus on col verbs the change of location verbs mainly because they are rich in spatiotemporal informations but also because we have at disposal exhaustive lists of french col verbs
we have realized a systematic and fine linguistic study on these verbs looking carefully at each of them one by one and we have NUM col verbs in french in order to extract their intrinsic spatiotemporal properties
note that the combination for such items does not behave the same in english where the final information is explicitly brought by the preposition into which is a directional preposition and where this particular combination does not create new information
we also address compositional semantics for motion complexes ie a motion verb followed by a spatial preposition and show that the complexity and the refinements of the linguistic studies presented just before are justified and required at the compositional level in order to capture the different behaviors in the compositional processes that exist with the french language
we have followed the same approach with french spatial prepositions but using a structuration of the space induced by the location introduced in the pp by the preposition and not induced by the lref as for verbs
the latter case is more interesting most of the french motion verbs are intransitive and the interaction between motion verbs and spatial prepositions gives detailed informations about the way human beeings mentally represent spatiotemporal aspects of a motion
the verb sortir to go out implicitly suggests an initial location the preposition darts which means in but which is translated here by into is a positional preposition and as so only denotes the static spatial relation inside
as we have come to these distinctions by examining different linguistic material we conclude that language structures space in the same way whatever sort of lexical items motion verbs dynamic static spatial prepositions we examine
in a realistic environment the correct attachment must be selected among several possibilities not just two
this method has been criticised because it does not consider the pp object in the attachment decision scheme
unfortunately in language engineering applications manually tagged corpora are not widely available nor easily implemented NUM
on the other side the exportability of disambiguation cues obtained in a given domain e.g.
first a kernel of shallow grammatical competence is used to extract a collection of noise prone syntactic collocates
null tables NUM and NUM show the average mi standard deviation and variance for the two domains
to evaluate numerically the benefits of the feedback algorithm several experiments and performance indexes have been evaluated
section NUM presents arguments against the lexical nature of sense extensions rules and for their status as reference transfer rules
section NUM examines the arguments made for their lexical status which we find wanting
thus there are two levels at which closed class forms across all languages are severely constrained as to the conceptual material they can refer to the level of conceptual categories and the level of the member notions within any conceptual category
research inroads have been made into the structural comparison of language with each of several further cognitive systems besides that of visual perception specifically with the reasoning inferencing system the kinesthetic somatosensory system the affect system and the cognitive system for cultural structure
while the sentence has only three open class forms each packed with a great deal of referential content the closed class forms on the other hand are much more numerous with each form expressing a limited structural concept
that is the concepts and smaller conceptual categories that are expressible by closed class forms in language cluster together in extensive imaging systems as i have termed these major categories each of which orchestrates one major aspect of structuring
on the other hand the preposition is more geometrically abstract than standard mathematical topology in that it is also closure neutral it applies equally well to a completely or a partly closed surrounding as in in the ball and in the bowl
further study is needed for the problem of whether there is a principle of principles that provides a unified explanation for why conceptual structuring in language is accomplished by the particular set of factors found in the universal inventory and not by some other set
thus with the sentence there are some houses inn the valley the closed class forms direct a hearer to conceptualize the referent scene in the synoptic mode i.e. with a stationary long range perspective point and with a global scope of attention
in this regard language is perhaps unique within the range of cognitive systems such as perception reasoning and affect so that mapping out the conceptual structuring system of language may serve as a model for comparable undertakings in the other cognitive systems
but the new closed class forms in there is a house every now and then through the valley direct one to conceptualize the same scene in the sequential mode i.e. with a moving close up perspective point and with a succession of local scopes of attention
by contrast the more recent tradition of cognitive linguistics centers its research directly within the semantic stratum of language in order to observe how languages organize meaning and structure conception and it examines the more formal stratum of language for its role in supporting these semantic functions
the newly added connection serves as an additional anchor for a more accurate estimation of relative distortion
the proposed algorithm relies on an automatic procedure to acquire class based rules for alignment
an abbreviated sample of the final decision list for plant is given below
sfor the purposes of exposition i will assume a binary sense partition
the bulk of the sample points constitute the untagged residual
the redundancy of language with respect to collocation makes the process primarily self correcting
using only two words as seeds does surprisingly well NUM NUM
however spurious words in example sentences can be a source of noise
first subjects were asked to perform a linear rather than a hierarchical segmentation where a linear segmentation simply consists of dividing a narrative into sequential units
for np recall and precision are not as different precision is higher than pause and cue and fallout and error rate are both relatively low
the elimination of empty rules is also essential because empty rules in the input to the rest of the gnf procedure lead to empty rules in the output
the method described by hindle rooth was reimplemented by using the lexical association strengths estimated from all pp cases
to implement this we modified hindle rooth s method to estimate attachments to the verb first noun
p posiaffix capitalization NUM p posiaffix x p posicapitalization
for the v np pp pattern this means preferring attachment to the noun phra se
let the values for the expected cell counts that are estimated by the model be represented by the symbol 7hljk
the iterative procedure has the following steps NUM start with initial estimates for the estimated expected cell counts
the cm process will support the following tipster goals use of api s modular substitution and conformance to applicable standards
NUM any requests for change rfc to the tipster architecture to cover the discrepancy or deviations in the tacad
the trivial model has both properties
the results are shown in figure NUM
training and test parts are disjunct
the advantage of model merging in this respect
unmarked transitions and outputs have probability NUM
the third method uses manually defined categories
in n gram approaches the topology is fixed
both are trained on the same data
merging starts with one of the constraints
ordination NUM s vp s e eats ll i ot tlrinks ll i np vp and l c cnokies 0i chapman or beer chapman v np v np i i i
for example from the elementary tree a drinks lcb NUM NUM NUM rcb the conjoin operation would create the auxiliary structure fl drinks lcb NUM rcb lcb NUM NUM rcb shown in fig NUM
an account for coordination in a standard ltag can not be given without introducing a notion of sharing of arguments in tile two lexically anchored trees because of the not ion of localily of argurnents in ipsfag
we have shown thai an acconnl tbr coordination can be given in a i i ag while maintaining the notion of a derivation strncture which is central to the l i a i approa h
null coordination of two structured categories cq a2 succeeded if tile lexieai strings of both categories were contiguous the functional types were identical and the least nodes dominating tile strings spanned by the component tree have the same label
if a contracted node in a tree after the conjoin operation is a substitution node then the argument is recorded as a substitution into the two elementary trees ms for example in the sentences NUM and NUM
in fig NUM the tree fl dried adjoins into beans and trees john and a beans substitutes into a cooked to give a derivation tree for john cooked dried beans
in conclusion we have to include cat or beast in the p which satisfies formula NUM
similarity to title this attribute records information on how similar a given sentence is to the title
this means that it is now possible to determine how representative particular discourse phenomena are how frequently they occur whether they are related to other phenomena what percentage of the cases a particular model covers the inherent difficulty of the problem and how well an algorithm for processing or generating the phenomena should perform to be considered a good model
computational theories of discourse are concerned with the context based interpretation or generation of discourse phenomena in text and dialogue
to date most coders enter data by hand in a word processor or using home grown hastily constructed tools
given the current state of the art we expect these issues to concern the community for some time
this issue brings together a collection of papers illustrating recent approaches to empirical research in discourse generation and interpretation
in recent years there has clearly been a groundswell of interest in empirical methods for analyzing discourse
for speech recognized input we used the first best hypotheses of the speech recognizer
we chose the wall street journal corpus because it follows standard stylistic conventions especially capitalization which is essential for nominator to work
a few simple indicators can unambiguously determine the entity type of a name such as mr for a person or inc for an organization
many wordsequences that are easily recognized by human readers as names are ambiguous for nominator given the restricted set of tools available to it
in the tree shown in l igure NUM has the effect of applying the lambda ext ression la laughs a to anna
figure NUM recall preeison curve of indexing meth ods
simple and compound nouns of a document set
given two listril utions mi and k4 for l rcb and NUM respectively tim discrimination l is defined as follows la lahut NUM
p n is the probability of candidate noun and l fln is the prot ability era fun tion word given the candidate noun
all nomi nals nix manually aleut fled and eoinpoulid ltoillis were deconq oscd into q proprinte simple nouns by t expert in lexcr
thus a good index term should distinguish a certain class of documents from the rest of the documents and be relevant to the subject matters of the class of documents to be indexed by the term
the noun dictionary is used to identify whether a noun is simple or compound and the basic stemming rules are used to differentiate non final words from others such as function words and verbs
section NUM explains tile proposed method in detail
i i lcb eiitove iton nonlitta
second in real environments and especially in sun languages syntactic noise seems to be a systematic phenomenon
this knowledge is fully corpus driven and it is obtained without a preliminary training set of hand tagged patterns
where f is the high level semantic tag assigned to the modifier n and pl NUM is the plausibility function
l or xampl our l ag sol isl inguislms pr l r n uus i i lcb i n
after analyzing the results more closely it was found that the l earner had learned a very spec i ie rule regarding tile reduction of prel osition subordinate conjunction eombinations late in the learning process
l introduction we have develol ed a spanish l arb l hi ch i s l a g g r which al l lies and extends ih ill s alg rilj u for unsul crvised l a rniug
this is because the learner is trained to reduce the ambiguity of possible tags of a word say n v adj tags but if the lexicon lists only a subset of the possible tags say n and v tags the system will never learn to assign an adj tag even when the word is used as an adjective
first we discuss our general tttproach including extensions we rim it NUM the algorithm in order t o hamllc ml kllowl w h ih a l d pa ra ll d crize ea l llillg and ta gging ot l ions
i et us focus now on the elelncnts which are not shared m NUM and r
phonological morphologi al sy tactic lexieo semmltic a nd pragmatic a nd ext ralinguistic i.e.
a crucial problem in pa rsing italian is tll assignmenl of subject a nd object relations to sentence constituents
despite sharing this assumption nativist researchers disagree strongly about the exact constitution of this universal grammar
the more difficult problem of decomposing the learned underlying surface correspondences into simple individual rules remains unsolved
ostia takes as input a training set of valid input output pairs for the transduction to be learned
a conflict arises whenever two states are merged that have outgoing arcs with the same input symbol
the introduction of an end of string symbol serves to expand the range of functions that can be represented
when the ocr encountered the fifth generation copies n NUM it garbled ninny words
when it encountered the fifth generation copies n NUM it also garbled many words
also it would be used as an automated dictionary selector for the ocr which uses domain specific dictionaries
the greater the value of sim di dj the more the similarity between d i and dj
however the learning problem remains unchanged if the rules are required to apply in some particular order
there are many different languages in common use around the world and many different scripts in which these languages are typeset
this is one of the state of the art ocrs in terms of speed and accuracy see rice et al NUM
submissions from the cawg will be reviewed by the se cm and approved disapproved by the architecture committee
only the pairs with their sire w j we value greater than log NUM frain are considered in this step
table NUM results of business letter table NUM summarizes the numbers of word sequences extracted by step NUM for each corpus the
content word extraction i english uunm j word sequence action ctionary
getting translation pairs of complex expression is of great importance especially for technical domains where most domain specific terminologies appear as complex nouns
the reason would be that scientific papers do not repeat fixed expression and the terminologies are used not in a fixed way
this allows us to distinguish between the correct spatial expression in the stock and the incorrect according to our application one in the triangle
the mathematical form of a loglinear model is a s follows
the following figure illustrates this situation
NUM NUM levels of conjunctive particles in japanese
particles that reflects modality within them
lexical information for determining japanese unbounded dependency
ite succeeded because she helped him
he returned while she was talking
the following figure illustrates this interpreting mechanism
lcb kamei k muraki doi hum cl
for the trouw data at m NUM NUM we count NUM NUM hapax legomena hence m NUM NUM
w fftopko kotae answel9 ta l ast
we can achieve this effect by instead taking our original lattice the right way up and using bitwise disjunction of elements to represent generalizations
to implement the extensions just described requires only the addition of the right number of extra argument places to hold the original atoms where relevant
we represent this as a tuple with a position for each of the relevant categories a b c d
it is thus unlikely that the compilation technique would be able to completely compile away the complex non atomic type hierarchies used in say hpsg
although the details are rather complex it turns out that it is possible to achieve this by combining the boolean encoding technique in conjunction with the use of selectors as previously described
now when compiling the grammar and lexicon for each category figuring in a linear precedence statement ca cb do the following
thus we will frequently write np lcb person NUM number sing rcb to mean lcb cat np person NUM number sing rcb
we will use the prolog notation for lists thus bar i x stands for the list whose head is bar and whose tail a list is x
NUM NUM o o o o NUM NUM o o person or i NUM NUM NUM NUM NUM NUM NUM NUM o plant NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
informally we regard a bitstring like those in the rows of the array above as a representation of the disjunction of the members of the set of lower bounds of the type
the whole integration of the project outcomes will be available at the end of the first phase in NUM
it came into birth in early NUM s with the emergence of personal comt uters pcs
such independence assumptions are not necessary
the system adds one to the con cot translation fi equency when the translation results have the same character strings as the proofl ead translation results and adds one to the erroneous translation frequency when the translation rules have different character strings to the proofread translation results
the system selects the correct translation result according to two criteria when there are several candidates of translation results one criterion is the translation rule which has a higher fitness value and the other is the translation rule which is more similar to the source sentence
this head modifier normalization has been used in our system and is further described in this paper
the following term extraction methods have been used they correspond to the indexing streams in our system
both cornell s smart version NUM and nist s prise search engines were used as basic engines NUM
recently we have turned our attention away from text representation issues and more towards query development problems
this state of affairs has prompted us take a closer look at the term selection and representation process
our nlir system encompasses several statistical and natural language processing nlp techniques for robust text analysis
in addition the performance of each stream within a specific range of ranks is taken into account
top table NUM summarizes selected runs performed with our nlir system on trec NUM database using queries NUM through NUM
since names are traditionally capitalized in english text spotting them is relatively easy most of the time
at first the positions are absolute the square a4 then relative at the left of the square a4 above
but facilitated communication is devoted to persons who are physically unable to communicate but do not have a difficulty with communication at the cognitive level
below we outline how r can be extended in order to capture more then just the basic lfg constructs and to allow for different styles of qlf construction
the f structure associated with the company which sold apcom started a new subsidiary is a ahere attd in tile following we will sometimes omit tags in the f structure representations
case of l nglish l ren h alignnt mt ko rean and gnglish have dilfer mt word units to be aligned for an english sentence consists o words whereas a t oreatt sentence consists of wordl hrases compound words
typically a word phrase is otnl osed of one or more content words and postpositional function words
the alignment nlethod is realized through he rcestimation of its probal ilistic parameters from tim aligne d
vnlu s word to word prol al ilties
phrase sequence tp t i th word a nd i denotes its sc l c
in l he al ove equation p nlc denotes the l roba bility that the l nglish word e generates n l rench words and p fle denotes the probability that the l mglish word e generates the l rench word NUM
o every pair of sentences of e and k we assign a value p elk the probability that a translator will pro luce e as its translation of k where e is a sequence of english words and k is a sequence of korean words
NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
table NUM samples of corresponding word sequences as incorrect correspondences
the NUM node level is considered to be a lowest boundary and the NUM node level is expected to be the tar null get abstraction level
level NUM grout ings contain a very abs racted level of synsets such as action time period and natural object
fig i shows a siml le exatnple o a fragment of a oncel ttud taxonomy will associated featttres
determination of semantic similarity between words is an important component of linguistic tasks ranging from text retrieval and filtering word sense disambiguation or text matching
we describe a similarity calculation model called ifsm inherited feature similarity measure between objects words concepts based on their common and distinctive features
from the corpus typed surface triples are triples of surface words holding some fixed linguistic relations hereafter call this simply surface triples
of course the distributional semantics approach assumes that such kind of errors or noise are hidden by the accumulation of a large number of examples
because ifsm depends on the features derived from the network rather than on the network itself judgements of similarity depend on the exact features assigned to c1 c2 and c3
the taggers should also be assisted in targeting exactly which distinctions they are to make
the science journal shows a stable accuracy of translation pair extraction
i illustrate the impact that this research can have with a case study describing the development of keycite a new citator product that would not have been possible without current generation text extraction and classification techniques
in this talk i identify areas of research that are likely to have significant commercial impact either because they enable new products or because they change the traditional economics of information delivery
we believe that the proposed method gives results of good performance compared with previous related work
the condition may be loosen to get better recall ratio though we may lose high precision
in theory expected word n gram counts can be obtained by the generalized forward backward algorithm
the system s performance is compared with a word list derived from two on line chinese dictionaries NUM words
this nmthod should adapt the dictionary of the word segmenter to new domains and applications
our goal is to provide a method to automatically extract new words from japanese texts
we define a statistical word model to assign a word probability to each word hypothesis
the higher order n gram counts involving unknown words are also obtained in the same manner
let oj be the jth word segmentation hypothesis for the ith sentence in the corpus
the method is derived from an approximation of the generalized version of the forward backward algorithm
in the second analysis the system guesses NUM
this would change the probabilities of the derivations however the probabilities of parse trees would not change since there would be correspondingly more derivations for each tree
it is important to run multiple tests especially with small test sets like these in order to avoid misleading results
factoring in that the hp is roughly four times faster than the sparc the new algorithm is about NUM times faster
for a given sentence and a given parse tree there are many different derivations that could lead to that parse tree
thus there are aj bk NUM c NUM possible subtrees headed by a j
we call a pcfg tree isomorphic to a stsg tree if they are identical when internal non terminals are changed to external non terminals
these results are disappointing the pcfg implementation of the dop model performs about the same as the pereira and schabes method
it would be very interesting to see how our algorithm performed on bod s split into test and training but he has not provided us with this split
we performed a statistical analysis using a t test on the paired differences between dop and pereira and schabes performance on each run
last relationships between concepts can be extracted such as the part of relation between tronc and artere and segment and artere fig
NUM modifier removal within the whole tree ev null ery phrase which represents a modified constituent is replaced by the corresponding non modified constituent
we argue that the various cliques in which a word appears represent different axes of similarity and help to identify the different senses of that word
the objective is then to reduce automatically the numerous and complex nominal phrases provided by alethgram and lextei lcb to elementary trees l lcb enanlt
fr direction des etudes et recherches electricitd de fra nce NUM av du g z de gaulle f NUM clamart f irstname
for instance removing node np0 yields two sub trees np which is elementary see below and pp2 which needs further simplification
stc ms de n i troilc coiflfft l l tollc ditch
ens f c l fr pub graphx i hc graph interactive handling software that enabled us to visualize and handle the sycladi NUM graphs
we discuss the resuits on nominal phrases of two technical corpora analyzed by two different robust parsers used for terminology updating in an industrial company
c empty heap h0 input sentence while icl m
in this section we present two methods for developing segmentation algorithms that combine the features of multiple linguistic devices in more complex ways than simply combining the outputs of independent algorithms
the NUM test narratives range in length from NUM to NUM phrases avg NUM NUM or from NUM to NUM clauses avg NUM NUM
the large scattering of narrow bars NUM x NUM illustrates the inherent noisiness of the data arising from the fact that subjects assign boundaries at varying rates
although we do not have enough subjects on any single narrative to compare two distinct sets of seven subjects we do have four narratives with data from eight distinct subjects
our initial experiments use only the features marked as o while our later experiments use the full feature set along with modifications to the noun phrase features
for example the contexts cons NUM NUM cons NUM NUM cons NUM NUM are the same as cons NUM NUM but omit references to the head word of the 1st tree the 0th tree and both the 0th and 1st tree respectively
malization constant aj are the model parameters NUM aj oo and fj a b e lcb NUM NUM rcb are called features j lcb NUM k rcb
3more than one frequent cb can be picked if there are no clear winners
1a parser which directly produces the pred arg structure is probably preferable to this method
i investigate ways to generate a summary without full interpretation of the original text
however some inference would be necessary in order to infer whether lfs are
for example the following two sentences from the sample text are very similar
as a result the following sentences from the text are selected for the summary
essentially this leaves the first sentence of each segment with the cb the inmates
if there is a pronoun in the sentence it is preferred to be cb
the heuristic used is heuristic NUM select repeated or semantically synonymous lfs i.e.
one way to find restatements in the text is to simply search for repeated phrases
researchers typically use traditional lexical scanner and parser tools for projects requiring the recognition of complex text elements
today s research results are enabling new products and are leading to fundamental changes in the business models of traditional publishers and online service providers
each training sample has the form t al NUM a2 b2 can bn rcb where ai is an action of the corresponding procedure and bi is the list of contextual predicates that were true in the context in which al was decided
the constituent we could join NUM contains a and the current tree is a NUM contains a and the current tree is a NUM spans the entire sentence and current tree is
the chunk tags are then used for chunk detection in which any consecutive sequence of words win w m n are grouped into a flat chunk x if wm has been assigned start x and wm l w have all been assigned join x
furthermore if lcb al an rcb are the possible actions for a given procedure on a derivation with context b and they are sorted in decreasing order according to q ailb we only consider exploring those actions lcb al am rcb that hold most of the probability mass where m is defined as follows
finally one can abandon the requirement that there be only one way to derive each tree in the ltig
bigger focus sets tend to give more information and sound more natural however the generation algorithm is concerned only with presenting alternatives and not with selecting between them
however in the context of language generation undergeneration is not necessarily a serious problem provided that there is the ability to adequately describe any model
candidate set r lcb rl r2 rcb candidate set s lcb sl s2 rcb choose scoping r s the following dependency function is constructed
compt saw lcb rcb power lcb rcb the quantifiers every and both are now chosen for r and s respectively giving the following sentence
definition NUM focus maximum and minimum for a variable with wide scope the focus maximum fmax and focus minimum fmin are the same
what is required is that the resulting focus set for r is the set of all representatives who satisfy restriction 17b under the chosen partition and quantifier
as a variable s restriction is processed the resulting focus set is passed back up to act as the candidate set for the same variable in the embedding structure
rx contains no embedded structure xs lcb x p x rcb qx nil scpx nil end process res
pile second class is called basic technology which is related to the basic software libraries for korean language processing
one could allow only one canonical derived tree
rule NUM completes the recognition of a subtree
adjunction replaces a node with an auxiliary tree
an efficient earley style parser for tigs is presented
superscripts are sometimes used to distinguish between nodes
first tig prohibits elementary wrapping auxiliary trees
no change is necessary in the a1 rule
example of the operation of the ltig procedure
the sample of heid words in max havelaar consisted of NUM tokens representing NUM types of which NUM hapax legomena
the horizontal axis plots the NUM equally sized text slices the vertical axis the frequency of ahab in these text slices
p m NUM NUM the fit x2 NUM NUM NUM p NUM
note that when the n tokens themselves constitute a sample from a larger population e v m is in fact an estimate
for large n and m however the binomial probabilities sampling with replacement are a good approximation of the hypergeometric probabilities sampling without replacement
this is represented textually above using curly braces
other predictions can not lead to successful matches
while this example is shorter than most texts in our corpus the relevant free text portions of the messages are typically no longer than a few paragraphs
see hard tag type below the government met japanese team was accorded the opportunity to test this hypothesis dunng the course of preparing dry run keys for the initial systems test in early april
in the test corpus NUM of the tags were of the enamex type that is the tagged items were references to organizations people and places table NUM
therefore for each lexically based contextual predicate there also exist one or more corresponding less specific or backed off contextual predicates which look at the same context but omit one or more words
the occasional mistake made by the systems involved not identifying a monetary unit other than the predominant dollar or yen such as NUM c british pound
subcat schema rule where subcat close macro closesubcat
this bitstring will be the description of living
this structure is of course recursive
we can do this in the following way
thus we will have rules schematically like
a better implementation would use a new feature
the lattice that we will use for illustration is
a lexical item can be represented by a category
for vcrbs and adjectives wdcncy patterns formulated as shown in figure NUM are collect ed beforehand in the dictionary called the valency pattern dictionary
some adverbial particles such as wa or me often stand in for case marking parlicles and give the case m additional fimclion
however it is possible for many japlmese adjectives and so me verbs to dominate two surface subjective cases within a simple sentence
as alignment proceeds the number of ones in asm reduces while the elements of am increase
given an asm mutual information and t score are computed for all word pairs in possible sentence correspondences
his method is also effective for japanese english computer manuals both containing lots of the same alphabetic technical terms
the i j element of am indicates how many times the corresponding words appear in the i j sentence pair
the method by combining two kinds of word correspondences achieves adequate word correspondences for complete alignment
japanese has a quite different system of closed words which greatly influence the length of simple features
on the contrary am is introduced to represent how a sentence pair is supported by word correspondences
dictionaries demerit they can not capture context dependent keywords in the corpus and are weak against incorrect word segmentation
if the texts contain m and n sentences respectively the asm is an m x n matrix
our basic data structure is the alignable sentence matrix asm and the anchor matrix am
the dictionary lookup process is straightfo ward
in gi osshuru this da nger
consider the case of english verbs ending in ed which are systematically ambiguous between being simple past tenses and past participles
first let us discuss the final case that of er ambiguity in dutch beginning with the derived and underived nouns
such words contribute to the overall proportional mass of the underived nouns thus boosting the estimate of the overall mle for this class
we first randomized the list of en tokens from the udb corpus then divided the randomized list into ten equal sized parts
one of the important functions of the past participle form is as an adjectival modifier or predicate for example the parked car
the problem then is to provide a more reasonable estimate of the relative probabilities of the various potential functions of such forms
for reasons that are not clear to us a predominant number of the high frequency verbs can not felicitously be used as prenominal adjectives
obviously the lexical prior probability of this form expressing the finite plural is not zero the mle is a poor estimate in such cases
lacking good contextual models one is forced to fall back on estimates of the lexical prior probabilities for the various functions of a form
a common sorting and priority ordering may be applied by the user
such supervision according to ash is a sensible cost effective alternative to incarceration that t should not alarm civil libertarians
the problem is how to judge which clauses are important sophisticated discourse analysis is needed in order to interpret the intentional and rhetorical structure of the original text and then prune it in the appropriate ways
jail the former prisoner plugs in to let corrections officials know they re in the right place at the right time
a shift indicates the start of a new discourse segment NUM in the method that i am proposing the original text is first divided into segments according to centering theory
notice that a simple trigram would not recognize that person answers by plugging in in NUM b as a restatement of the prisoner plugs in
architectural choices should recognize the desirability of incorporating multi level security in component implementation
then for each domain the relevant rules have to be annotated with an indication of the daughter that is to be treated as the distinguished one
the three sets of choices according to NUM are would generate a neutral statement choosing tone la to accompany the presentation as in la die ergebnisse sind unten dargestellt z the results are given below
the use of context vectors results in a natural method for specifying degree of relationship between incoming news feeds and customer interests
it is also a natural mechanism for detecting novel information themes a fact that could be advantageously used by the intelligence community
the hnc approach to information retrieval is based on context vectors which are unit vectors in a high dimensional vector space
since the context vector methodology requires only that a finite vocabulary of entities be defined for the domain of information this technology has been extended to other media images in particular
these retrieval agents can be scheduled by the user to access information at specified possibly repeated times thereby removing the requirement that the user actually be present during the retrieval operation
hnc s context vector technology has also been extended to the problem of information routing and filtering resulting in a cots product known as convectis
hnc has also responded to the explosion of information available on the internet by developing a system of autonomous retrieval agents based upon the matchplus technology
we plan in future work to address a number of shortcomings of these experiments for example including some spontaneous speech corpora and looking at a wider variety of rules
as a premier developer of neural network based technologies hnc has played an important role in successfully bringing innovative approaches to the problem of information retrieval including multilingual information retrieval
the multilingual matchplus approach is able to perform information retrieval in multiple languages without the necessity of specifying any grammatical information about the language thereby greatly reducing system development time
a csc is a distinct part of a computer software component item csci
the extraction component shall be compatible with other components and modules of the tipster architecture
the priority can be scaled based on where in the document the match occurs
the architecture shall support various generic information types that are applicable to tipster processes
previously created and saved detection criteria may be obtained from the appropriate library and modified
the statement shall be sufficiently complete so that relevance can be determined with reasonable certainty
that is for each word in common we divided the number of times each subject used the word by the total number of words uttered by that subject in order to determine what proportion of the subject s conversation consisted of the uses of that word
although the actual measurement of lexical items was done for the english speech of the japanese to english interpreters these interactors in the conversation will be referred to below as agents to conform to the human human setting in which we assessed the lexical accommodation of the agents directly
the results for this acquired rule set are surprisingly encouraging
overall however the machine crafted rules still lag behind
rightmost word in the phrase is inc
the interprcter maintains a temporary lexicon on a document by document basis
we have applied a variety of techniques towards this task
our performance here is high especially for person names
this was done in the ore defragmentation rule described earlier
succeeds if any word in the phrase passes the test
processing takes place by sequentially relabeling the corpus under consideration
this initial labeling is refined by two sets of transformations
as example of the discourse processing consider how the system handles the user s first utterance in the dialogue okay let s send contain from detroit to washington
the points reflect the performance of sphinx ii without speechpp when using NUM NUM and NUM of the available training data in its lm
while keyboard performance is not perfect because of typos we had a NUM word error rate on keyboard it is considerably less error prone than speech
all we conclude from this experiment is that our robust processing techniques are sufficiently good that speech is a viable interface in such tasks even with high word error rates
we are addressing this problem in our current research we are developing a domain independent plan reasoning shell that manages the plan recognition evaluation and construction around which the dialogue system is structured
in each the words tagged ref indicate what was actually said while those tagged with hyp indicate what the speech recognition system proposed and hyp indicates the output of speechpp our post processor
this system yields an overall word error rate of NUM on trains NUM dialogues as opposed to a NUM error rate that we can currently obtain by using language models trained on our trains corpus
the discourse manager breaks into a range of subcomponents handling reference speech act interpretation and planning the verbal reasoner and the back end of the system the problem solver and domain reasoner
the valency pattern for each usage for each predicate is defined in the valency pattern dictionary
finally the outpnt english sentence is generated from the stnlcture
a valency pattern is defined for each usage for each predicate
sentences olher them double subject construction as sentence with an adjective predicate
as a result type NUM mmlysis involves changing the valency structure
the algorithm that overcomes these problems has been explained in detail
this method has been applied to japanese sentence analysis in alt j e
such particles arc callcd case markiu g pm ticl es
in order to describe the conceptual features of correct expressions like above the triangle we consider this sort of prepositions as functions which applied to an object which is not a place give a place as result thus the expression above the triangle can be used with a verb like put which requires a place as a complement put the gray circle above the triangle
this leads us lo lhe wrong interpretation as shown in lhe left bottom of figure NUM
second type NUM is set by the valency paltem for the predicate in lhc input
table NUM thresholding by the kappa coefficient
the process described above is repeated until the treebanker can narrow the parse forest down to a single correct parse
figure NUM two atr lancaster treebank sentences NUM words italicized NUM words large font from chinese
however the claws tagger performs basic and preliminary tokenization and sentence splitting for optional correction using the xanthippe editor
finally parsing and tagging consistency among the first three treebankers appears high
finally we lay out plans for the further development of this new treebank
ii0 the treebank editor also displays the number of parses in the parse forest
morph l gical analysis as we have seen norl hological amllysis is cs
therefore this judgment is performed by checking whether the valency pattern for the predicate includes both a subjcctive case and an objective case
the ealizal ion of l hesc lesig l goals i e lljircd exl ensive ascs abou t rench ntorl hol ogy and lexicon
in this exalnple the disambiguator chooses the 6crire masc sg p aprt reading ms the tnost likely one as shown in figure NUM
search looks not only lbr further occurrences of the same string but mso for thrice null tional varbml s of the word
as a constraint on the application of the conjoin operation the contraction sets of the two trees must be identical
we do not choose between the two representations but continue to view the conjoin operation as a part of our formalism
lexical association strength between the noun and the preposition
the rule tagged surface lexicon described in ss2 NUM and the counts derived from the forced viterbi described in ss2 NUM can be combined to form a tagged lexicon that also has counts for each pronunciation of each word
the loglinear model on the other hand predicts attachment with significantly higher accuracy achieving a clear separation of the central NUM of the evaluation samples
is the word in sentence initial position and capitalized in any other position and capitalized or in lower ca e
finally t must be different from t because it must be larger than t
the al initial tree is retained under the assumption that a1 is the start symbol of the grammar
in step NUM the al initial tree is lexicalized by substituting the remaining a2 initial tree into it
NUM return the case with the highest score as well as all ties
3a number of other values for the missing features weight were tested as well
furthermore we have focused on applying the linguistic bias approach to feature set selection for case based learning algorithms only
in the long run it may obviate the need for separate instance representations for each linguistic knowledge acquisition task
siemsl siemens corporate research siemens merging by ellen m voorhees used the smart retrieval strategies from trec NUM in this run their base run for the database merging track
expansion did not work as well as in trec NUM and additional work comparing the use of infinder and the use of the top documents for expansion is reported in NUM
the three techniques are a vector space system a passage retrieval method using a hidden markov model and a topic expansion method based on document links generated automaticauy from analysis of common phrases
but adding terms also increases the noise factor so accuracy may need to be improved via a precision device and hence the use of passages subdocuments or more local weighting
even though both systems added about the same number of expansion terms using only the top NUM documents as a source of terms for spreading activation might have provided too much focussing of the concepts
the simple replication of words led to a NUM increase in performance adding the associated words the pircs2 run upped this increase to NUM improvement over the initial automatic query
for other groups however the goals are more diverse and may mean experiments in efficiency unusual ways of using the data or experiments in how users would view the trec paradigm
this is consistent with the difference in their topic expansion techniques in that the automatic expansion even manually edited is likely to bring in terms that users might not select from non focussed sources
some groups explored hybrid approaches such as the use of the rocchio methodology in systems not using a vector space model and others tried approaches that were radically different from their original approaches
additionally charts have been published in the proceedings that consolidate information provided by the systems describing features and system timing and allowing some primitive comparison of the amount of effort needed to produce the results
for example one common type of keyword is a relation whose value may be another spl plan or a reference to such a plan
before a sentence is entered into the sentence bank it is passed to the tagger to determine the part of speech of each word in the sentence
in the sentence bank each token is presented as a unit that is linked to the underlying spl plan template so that the template can be edited
this environment including its spl plan templates and sentence bank provides an easy way for penman users even novices to create and maintain spl plans
the former category describes most of the process hierarchy i.e. verbs and most relations whereas the latter describes the logical and rhetorical relations
splat s graphical environment provides additional support to the user in the form of menu driven access to penman s linguistic resources and management of partially built sentence plans
each spl plan contains one or more head concepts from the upper model and a variable that enables the plan to be referred to by other spl plans
the proposed parse p is identical excluding pos tags to the treebank parse t rises substantially to about NUM from NUM when the perfect scheme is applied
thus there are three parameters for the search heuristic namely k m and q and all experiments reported in this paper use k NUM m NUM and q NUM
table NUM shows the average performance of the pause algorithm for statistically validated boundaries at the NUM
this set was used during development of the attachment algorithm ensuring that there was no implicit training of the method on the test set itself
hindle and rooth s method scored NUM NUM accuracy NUM correct on this set whereas the backed off measure scored NUM NUM NUM correct
in the nl and n2 fields m1 words starting with a capital letter followed by one or more lower case letters were replaced with name
NUM NUM etfe rt ff hand tagged tex s
whenever the new intention requires information that is not currently in the cache that information must be retrieved from main memory
the second hypothesis is that the content of the return utterance indicates what information to retrieve from main memory to the cache
when conversants start working towards the achievement of a new intention that intention may utilize information that was already in the cache
a new focus space is pushed on the stack during the processing of dialogue a when the intention of utterance NUM is recognized
thus these types of extended embedded segments suggest that the limited attention constraint must be sensitive to some aspect of linear recency
additional evidence for the influence of linear recency arises from the analysis of informationally redundant utterances irus in naturally occurring discourse walker
the phrase as far as the certificates are concerned indicates that this new intention is subordinate to the previous discussion of the certificates
these types of irus show that hierarchical recency as realized by the stack model does not predict when information is accessible
the evidence above suggests the need for a model of attentional state in discourse that reflects the limited attentional capacity of human processing
as cases of infelicitous discourses constructed as variations on naturally occurring ones while remaining consistent with evidence on human limited attentional capacity
the pairs contained NUM translations classes for take whose occm rences ranged from NUM to NUM we first converted the sentence pairs into an input table consisting of the case attribute english word form value and japanese translation for take class
in the retrieval experiments with the training trec database we noticed that both joint and venture were dropped from the list of terms by the system because their idf inverted document frequency weights were too low
one way to deal with this problem is to allow the system to fall back on partial matches and single word matches when concepts are not available and to use query expansion techniques to supply missing terms
in trec NUM the average query length is less than NUM terms which we considered too short for using locality matching and this part of the weighting scheme was in effect unused in the official runs
in particular we demonstrated that natural language processing can now be done on a fairly large scale and that its speed and robustness has improved to the point where it can be applied to real ir problems
the in document frequency factor is usually normalized by the document length that is it is more significant for a term to occur NUM times in a short NUM word document than to occur NUM times in a NUM word article
for example the pair retrieve information will be extracted from any of the following fragments information retrieval system retrieval of information from databases and information that can be retrieved by a usercontrolled interactive search process
it is certainly true that the compound terms such as south africa or advanced document processing when found in a document give us a better idea about the content of such document than isolated word matches
the statistical weighting formulas based on terms distribution within the database such as idf are far from optimal and the assumptions of term independence which are routinely made are false in most cases
since each unique term can be thought to add a new dimensionality to the representation it is equally critical to weigh them properly against one another so that the document is placed at the correct position in the ndimensional term space
natural language processing is used to preprocess the documents in order to extract content carrying terms discover inter term dependencies and build a conceptual hierarchy specific to the database domain and process user s natural language requests into effective search queries
when building semantic tagsets many questions need to be answered for example to what extent are semantic tagsets language specific
NUM NUM lexicon prol le nj
for compatability with existing single byte text the most significant bit of the first byte is used to distinguish between multi byte characters and single byte characters
first the number of different characters that can be represened with a negative value is NUM and usually ip NUM characters
about the cognitive status of frames
encode multiple frame elements for a single constituent
this is both a practical and theoretical problem
such cases occur if a general dictionary is used in segmenting technical articles e.g. in law medicine computing etc
apart from recurrence if there are a lot of backing up operations the kmp algorithm would perform better than the brute force implementation
it is important to notice that a matrix is constructed for each extraction task and the agreement coefficient k is determined for each task not for each sentence in the text
for a sentence s its degree of distinction d s from other sentences is defined analogously to the s m a rity function above
this had left us with nine sets of data with associated threshold values NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM and NUM NUM
though NUM of NUM subjects were assigned to one test due to the lack of enough subjects we had to ask the remaining NUM subjects to work on q five tests
we asked NUM naive subjects students at graduate and undergraduate level to extract NUM of sentences in a text which they consider most important in making its summary
we discarded data sets with k NUM NUM because they lacked a su cient number of sentences for evaluation the column type data has only NUM sentences at NUM NUM cf
but at the moment it is not clear to us what is a good attribute for representing texts like columns for which the abstracting model was found not effective
we have also found that a set of attributes vary in effectiveness from one text type to another though texts under consideration are all om the same domain
the number of subjects assigned to one extraction task varied from NUM to NUM NUM of the time we had over NUM subjects working on a same task
therefore the rhetorical relation that links e and d signals that among the elements in cf c the envelope is the best candidate to be the primary focus of the following sentence d
the global approach to the generation of anaphoric expressions presented in this paper and in particular the treatment of pronominalization has been developed together with massimo zancanaro whose help i gratefully acknowledge
e appears in a list inside a concept definition german itdian use a bare singular or bare plural noun phrase english use a definite singular or definite plural noun phrase
l equirement NUM suggests that the head of the noun phrase should be chosen among terms of common use or more in general among terms that the user is likely to know
the model we propose is an implementation of the theoretical investigations of martin and is based on a clear representation of the knowledge sources and choices that contribute to the identification of the most appropriate linguistic expressions
the algorithm is activated on each object entity to be referred and accesses the following available contextual information null background the cultural and social context of the reader
definite expressions are built selecting the appropriate determiner the this that and the information head modifiers to put in the noun phrase
a presupposed element presuming may belong to the cultural social context and therefore be described with a unique reference or it may belong to the textual context
the advantage of this solution is that it allows us to treat with a uniform approach different types of exceptions that in literature are solved with separated ad hoc solutions e.g.
for example we have chosen relations like haspartnership owned by or attribute of characterizing distinguishing descriptions like the applicant s spouse or the applicant s estate
nevertheless the verification of pruning operations dominates all other steps of computation
figure NUM result of merging states NUM and NUM of figure NUM
table NUM phonological features used in alignment
therefore using variables is essentially free and contributes nothing to overall complexity
we need some method of interleaving three rule transducer induced from NUM NUM samples
we tested the algorithm using a synthetic corpus of NUM NUM input output pairs
as the corpus contains no such cases no errors were produced
voiced stop will be emitted along with the current input symbol
the subsequential transducer for 2a is shown in figure NUM
rule based variation in phonology has traditionally been represented with context sensitive rewrite rules
if adept correctly captures all the fields for a documents format an sgml encoded document is transmitted to the rose system for information dissemination
NUM NUM document processor dp the dp identifies and extracts all sgml tags defined in the mapping template for the specific source
for cases where the original document trigger is garbled due to a transmission error the user can elect to define a temporary trigger
system adaptation adept enables data administrators to manually adapt the system s configuration mapping templates to meet new or changed formats
a data administrator can manually change the value of a tag and resubmit resolved document s for reprocessing by the system
am provides login control and user permissions maintains the system s security and audit log and enables backups restores of the system databases
dp creates annotations with the value na not available for those nonrequired sgml tags not present in the document
figure NUM NUM illustrates how adept will be inserted into the rich open source environment version NUM rose testbed environment at oir
the adept project has completed the system requirements review srr as well as the preliminary design review pdr
a critical design review cdr is scheduled for late june NUM to be followed by a tipster engineering review
personal name is one of important clues
here ci denotes a chinese character
some organization names have nested structures
numeral and classifier are also helpful
syllable order may be a clue
the concept may be used here
thus another clue should be found
the following subsections present other clues
every section has its own characteristics
when an anchor is identified the list of name candidates is scanned for ambiguous variants that could refer to the same entity
nominator cycles through the list of names identifying anchors or variant names that unambiguously refer to certain entity types
many of these uncategorized names are titles of articles books and other works of art that we currently do not handle
it is based on the confidence scores yielded by heuristics that analyze a name and determine the entity types it can refer to
we can describe the splitting heuristics as determining the scope of ambiguous operators by analogy to the standard linguistic treatment of quantifiers
the conjunct may be present in the sentence but even if it is not it can be added in a linguistically satisfactory way
a more difficult situation arises when a sentence initial candidate name contains a valid name that begins at the second word of the string
since the left and right substrings do not satisfy any conditions we proceed to the next operator on the left and
entity type ambiguity is quite common as places are named after famous people and companies are named after their owners or locations
consider examples such as western co of north america commodity ezchange in new york and hebrew university in jerusalem israel
the result is field mt l
i l iug the general argument first firstly a text without its context is itself an abstraction
the statistics used by kankei for partial or full matching can be obtained in various ways
NUM ultimately the trains disambiguation module will contain functions measuring rule habituation and distance effects
the need to choose weights is a drawback of the approach
kankei i is a first attempt at a trains disambiguation module
len schubert s supervision and many helpful suggestions are gratefully acknowledged
such a technique will be referred to as full matching
then it will become necessary to weight the scores of each disambiguation technique according to its effectiveness
verb np head preposition obj head i adverbl adverb2 figure NUM format of an attachment pattern
in cases where nominator can not resolve an ambiguity with relatively high confidence we follow the principle that noisy information is to be preferred to data omitted so that no information is lost
nominator identifies the referent of the full form see below and then takes advantage of the discourse context provided by the list of names to associate shorter more ambiguous name occurrences with their intended referents
it applies a set of heuristics to a list of multi word strings based on patterns of capitalization punctuation and location within the sentence and the document
the networks now include more delicate grammatical distinctions in order to realize the variations that have intonational consequences
numbers following the at the beginning of a tone group indicate the type of tone contour
we can only make predictions about complete clauses hence the grammar prevents the generation of utterances with ellipses
we are aware however that generally there is no one to one correspondence between information unit and clause
the scenario itself excludes the command and the statement option since the system is in need for information
which tone one chooses for this type of question depends on how involved one wants the system to appear
the added discriminations are constraints on the specification of an appropriate intonation rather than constraints on the structural form
NUM the primary tones are the undifferentiated variants whereas the secondary tones are interpreted as realizing additional meaning
when spoken mode is envisaged as output the intonation contour is the major means to convey this information
to illustrate this point imagine an information seeking dialogue where the user wants to know a specific train connection
we would like to structure our entire vocabulary around this word as a series of similarity layers
we see our classification as a means towards the end of constructing multilevel class based interpolated language models
gildea and jurafsky learning bias and phonological rule induction the induction of decision trees adds a new stage after the ostia algorithm completes
in practice we found the use of alignment information significantly sped up the algorithm by allowing states to collapse more quickly
but the key insight is that the current transducer is incorrect because the absence of particular make a number of complex unnecessary decisions
in contrast to figure NUM the arcs now correspond to the natural classes of consonants stressed vowels and unstressed vowels
compare figure NUM with the correct transducer in figure NUM we have used set subtraction notation in figure NUM to highlight the differences
any alterations at level s will not bear on the classification achieved at s NUM
the vocabulary items extracted from the training set were clustered according to the method described earlier
this represents a pragmatically sensible baseline value against which any variant language model should be compared
however as n grams become less frequent we would prefer to sacrifice predictive specificity for reliability
it is important to note that while we are changing the model of transduction we are not increasing its formal power
each of the ten parts was held out as the test set and the remaining nine tenths was used as the training set over which the two mle estimates were computed
the solid horizontal line represents the proportion of infinitives calculated over all frequency classes and the dashed horizontal line represents the proportion of infinitives calculated over just the hapax legomena
such improvements could not be previously obtained because these knowledge sources have not been used together for this task before
to achieve this all the possible graphemic transcriptions of phonemes were coded as separate graphemic symbols e.g. 7r and 7rtr are two different graphemic symbols even though they are both pronounced p
where n x is the number of occurrences of x in the training corpus and n x is an estimation of the number of occurrences of x in the application corpus
an important advantage of the system presented here in comparison to rule based or dictionary look up systems is that it produces only one or at least very few graphemic suggestions for each phonemic word
we have presented a system for phoneme to grapheme conversion ptgc at the word level that uses the principles of hidden markov models to statistically correlate the graphemic forms to the phonemic forms of a natural language
the input degradation does not affect the overall system performance very much in any of the four output positions even when more than NUM of the input words have at least one incorrect phoneme
the bias is one occurrence for each transition that has never occurred in the training corpus computational linguistics volume NUM number NUM and the model is normalized so that it fulfills the statistical constraints i.e.
in its current version the method is based on second order hmm and on a modified viterbi algorithm which can provide more than one graphemic output for each phonemic input in descending order of probability
this implies that considering storage we need to keep in memory NUM x NUM x NUM double precision floating point numbers for matrix a along with the other data of the model and the algorithm
the smallest nonzero value of p in this representation and effectively its best resolution is NUM NUM x NUM NUM corresponding to the greatest value of log p which is NUM NUM NUM
however by measuring the ambiguity introduced by the speech recognizer output it can be seen that the ptgc system in fact improved the performance of the overall system recognizer simulator and ptgc
note that lower wdues for all scores are preferred
ltag trees are taken to be templates
figure NUM structured category for cats cookies
lcb anoop j oshi rcb olinc
and john wants to try to see mary and bill susan
figure NUM example of a tree traversal
figure NUM lexicalization in a ltag
the temple translator s workstation is incorporated into a tipster document management architecture and it allows both translator analysts and monolingual analysts to use the machine translation function for assessing the relevance of a translated document or otherwise using its information in the performance of other types of information processing
there is no clear notion of a derivation structure for this process
fable NUM processing time second ior word shape token wst generation and ocr our system categorized the test documents in word shape token and ascii format as described in the previous section
in the digital network world documents are usually distributed in either text file or image format where the former is a sequence of character codes e.g. ascii and the latter is a bitmap
table NUM shows that the mapping between character shape codes and original characte is one tomany we use only seven character shape codes lcb a x e n i g j rcb NUM to represent all alphabetical characters
unlike ocr which attempts to correctly recognize each word using lexical information word shape token generation is only faithful to the original image thus it makes many errors with low quality images whereas ocr indicates illegible characters
figure NUM document image above and generated word shape tokens below note there is all error many xxag in the second line due to a small ink drop our character shape code recognition does n t require a complicated image analysis
because of sense ambiguity the collision sets became NUM NUM in the enea corpus and NUM NUM in the ld
as there are no constraints for daughters to be adjacent to each other there may be an arbitrary munber of constituents between the licensing daughter and the head daughter
finally u must be different from t because it must be larger than t
description many cereals are made from corn wheat or rice
description ray watched his father clean the ashes out of the fireplace
concept clustering and knowledge integration from a children s dict ionary
usage asia is what is left after something burns
i his research was supported by the institute for robotics and intelligent systems
usage it is a soft gray powder
we then see the definition of message the new concept cluster and the resulting cckg
once built the cckgs will help us in our comprehension and disambiguation of the text
we deem words in the definition to be important if they have a large semantic weight
our goal is to create a more interconnected graph rather than sprouting from a particular concept
if the root form did not occur in the corpus then the inflected form was used
each member of the jury examined the quality of a set of NUM letters NUM produced by the sa system NUM by the automatic hybrid system and NUM humanwritten for identical cases
whether the addressee is aware of an event whether an event is in the addressee s favor and so on lastly the linguistic generation submodule realizes each event li om the text surface structure
in the lollowing section we have describe lcb l tile three techniques under assessment semi automatic non linguistic fill in the blank intcrlhcing atut rcb matic linguistic and tentphtte hybrid gerlct ation and human wril ing
there is no proo o1 this but several people who know the semi autotnatic systetn were of tim opinion that the scmi automatic letters ttsed in the test were butter than the average semi atttomatic letter
automatic hybrid system the principal strong points of the automatic linguistic and templates system based on alethgen are as follows in decreasing order of variation in relation to lhe semi automatic averages
the first conclusion is that semi automatic systems just as real situation human writing are subject to human mistakes and that the texts they produce may be difficult to understand
however this was not the case for the senti automatic system in particular due to problems of comprehension but also due to grammatical mistakes in the fill in the blank system
the output of the conceptual planner is the text s deep structure in which the events to be era tied out are not yet in a definitive order
the order of results for tile different techniques is always tile same for all tile criteria first truman writing second the automatic hybrid approach trod third tile senti automatic system
vs automatic letters vs sa lcttcrs vs sa letters ideal human letters vs sa letters NUM NUM the results obtained by the ideal human letters md those generated automatically are close
the coverage loss due to grammar specialization was then measured on the NUM NUM utterance test set
as we have so the four different analysis alternatives measured on the NUM utterance test set
we expect to be able to report on this work more fully in the near future
next account is taken of the connectivity of the chart
for a lexical edge this reduces to its word or word class and its tag
the precise definition of the rule chunking criteria is quite simple and is reproduced in the appendix
in the first increasingly large portions of the training set were used to train specialized grammars
utterances which took more than NUM cpu seconds to process were timed out and counted as failures
the slight improvement in coverage with ebl on is not statistically significant
the proof is by induction on the depth of the trees
there are two existing ways to parse using the dop model
for the string xx what is the most probable derivation
these numbers correspond to the number of subtrees in figure NUM
these trees can be combined in various ways to parse sentences
conclusion we have given efficient techniques for parsing the dop model
it is not clear what exactly accounts for these differences
table NUM dop versus pereira and schabes on bod s data
examining bod s data we find he removed e productions
other nlp applications could reuse such a simple annotation in order to determlne for example selectional restrictions or text classifications
our method extends the usage of the dice coefficient in two ways it deals not only with correspondence between the words but with correspondence between word sequences and it modifies the formula measure so that more plausible corresponding pairs are identified earlier
the initial value of fmi is set at the half of the highest number of occurrences of extracted word sequences and is lowered by dividing by two until it reaches to or under NUM then it is lowered by one in each iteration until NUM
furthermore the fst automata developed for the purpose of messag e extraction have been designed along the lines of this annotation scheme
though the method proposed in this paper deals only with consecutive sequences of words and is intended to provide a better base for the structural matching that follows the results themselves show very useful and informative translation patterns for the domain
in the formula f we f wl represent the numbers of occurrences of we and wl and f we wl is the number of simultaneous occurrences of those words in corresponding sentences
we have achieved an lri style coupling of ibur different modules word recognition module syntactic parser semantic module and prosodic boundary module
for the last case we implemented a precompiler for word based prediction which to our current experience is clearly superior to the previous word class based prediction
and the output of the fst automata has been defined in such a way that they can be used for an automatic rule based semantic annotation of new text input the annotation being limited to the temporal expression
german research center for artificial intelligence lcb declerck klein dfki uni sb de there are hardly any an notation schemes including semantic information with the exception of princeton wordnet which will be extended by eurowordnet for european languages
in the last NUM years there have been numerous architecture proposals for distributed problem solving among computing entities that exchange information explicitly via message passing
we present an analysis of the runtime of our algorithm and bod s
after all words have been collected in s the initial set of possible translations p is set equal to s and champollion proceeds with the next stage
on the third run the word triplet honneur officieues langues is selected out of NUM triplets with a score of NUM NUM
instead of computing a correlation factor for each of the NUM k elements with the source collocation champollion searches a part of this space in an iterative manner
testing champollion on three years worth of the hansards corpus yielded the french translations of NUM collocations for each year evaluated at NUM accuracy on average
we have experimented on up to two full years of the hansards corpus amounting to some NUM NUM sentences in each language or about NUM megabytes of uncompressed text
our concern is whether our algorithm will actually generate all valid translations those with final dice coefficient above the threshold while it is clear that the exhaustive algorithm would
figure NUM shows a graph of overall tagging accuracy versus percentage of unknown words in the text
because all capital letters map to a table NUM it is difficult to identify words with only capital letters which are sometimes important content representing words e.g. acronyms
using a vector space classifier with a scanned document image database we show that the word shape token based approach is quite adequate for content oriented categorization in terms of accuracy compared with conventional ocr based approaches
when they were in lower quality n NUM the ocr based approach had stronger correlation between the accuracy and the size than the word shape token based approach
in the ocr based approach with the first and the third generation copies n NUM NUM the test documents were large enough for this categorization task
as a result we obtained NUM topic tagged document images for each nth generation photo copies n NUM NUM NUM figure NUM shows scanned image samples
but this time they were mistaken for other word shape tokens e.g. many xxag in fig NUM and acted as a negative factor to reduce the accuracy
first the system transforms the test document image into a sequence of word shape tokens as described in the previous section where conventional systems perform ocr to generate a sequence of ascii encoded words
from the experimental results in the previous section our hypothesis that word shape token based approach is quite adequate for content oriented categorization was strongly supported at least for the document images from first and third generation photo copies
NUM identifiers starting with an uppercase letter denote variables otherwise they are instantiated symbols a lexical entry is described by the term synword morpheme category
but there is essentially no information in such a representation
graph showing the performance of the top down classification system compared to two recent systems those of hughes and atwell and of finch and chater
the projected contour map highlights the main feature of this relationship at various frequencies each of the NUM class bigram models is used
we are interested in that classification which maximizes the average class mutual information we call this t o and it is found by computing
NUM suggests that language models based upon fixed place classes can be only slightly worse than some similarity models given approximately equal training texts
to confirm the correlation between the conjunction level and the pause length we have analyzed speech data spoken by a professional news announcer male reading newspapers and magazines at a regular speed
the following section determines the role of the underlying instance representation in case based learning of natural language by comparing the accuracy of the cbl algorithm on a number of natural language learning tasks using different instance representations
it therefore simply becomes a new edge on the agenda
such information will be presented in the representation of the feg associated with the predicate
we plan to create a starter lexicon containing some NUM NUM lexical items indexed to examples of their use
in this usage there is no corresponding intransitive my influenza arthritis healed
the main steps in developing a probabilistic classifier and performing classification on the basis of a probability model are the following
the purpose of a probabilistic model is to characterize the uncertainty in the classification process
since datr is no more than a language it does not itself dictate how a datr lexicon is to be used
nodes can be used for states or else states can be encoded in attributes that are prefixed to the current input path
consider an example such as the following dagi vp agr v agr v agr per NUM vp agr gen masc
this looks like simple re entrancy from which we would expect to be able to infer dagi vp agr per NUM
one is that their use makes it hard to spot multiple conflicting definitions vars vowel a e i o u
computational linguistics volume NUM number NUM been defined at all since there would have been no leading subpath with a definition
one way to achieve this is to define a value for syn form which is itself parameterized from the values of these other features
however the components of syn form present sing third are themselves values of features we probably want to represent independently
instead datr allows their relatedness of meaning to be captured by using the definition of one in the definition of another
erties of the ambiguous object and the context in which it occurs the non classification variables
we trained three segmentation models namely part of speech trigram word unigram and word trigram after we replaced those words appeared only once in the training texts with the unknown word tag unk as described in the section of word model
the first was trained using all words in the training texts while the second was trained using those words whose frequency is less than or equal to NUM in principle the spelling model of unknown words must be trained using the low frequency words
because of the difference in the segmentation of NUM z it p the number of words in corpus segmentation std NUM differs from that of system segmentation sys NUM
a histogram of the relevant features for part of speech tagging across the NUM folds of the cross validation experiments are shown in figure NUM
the general strategy we have adopted for finding initial phrase seeds is to look for either runs of lcxcmes in a fixed word list or runs of lexemcs that have been tagged a certain way by our part of speech tagger
we still have much to explore especially with the learning procedure lndccd while the lcamcr induces til e sequences that pcrfi rm well in tim aggrcgatc individual rules clearly show their mechanical genesis
the fragment of interest here is demonstrably a subset of the regular sets and while these languages are traditionally analyzed with finite state automata our approach relies instead on the rule sequence architecture defined by eric brill
in the word unigram model the unigram count c wi for unknown word wi is given as the product of the total unigram count of unknown words c unk and the word model probability p wil unk
for example a rule whose yield is ioo and sacrifice is NUM deg is treated as equally valuable as one whose yield is only NUM deg but which introduces uo overgeneralization at all sacrifice o
we extend the machine bt to encode this rule by replacing s current start state s with a new one s and adding a transition from s to the former start state s thus
our information extraction prototype alembic is in fact based on a pipeline of rule sequence processors that run the gamut from part of speech tagging to phrase identification to sentence parsing to inference aberdeeen et al
the pos matrix is used to disambiguate word boundary as well
we found that a sentence with NUM words can generate NUM syntactic patterns of word chain
each stage will gradually weeds out word boundary ambiguities tag ambiguities and implicit spelling errors
each stage will gradually weed out word boundary ambiguities tag ambiguities and implicit spelling errors
the NUM x NUM pos matrix obtained from NUM NUM sentences corpus
the former can be detected easily by using a dictionary based approach
each word given by either way of grouping has a meaning
finally the results of applying this algorithm will be presented
if there is any explicit spelling error it will be detected and suggested for correction
each step will gradually weeds out word boundary ambiguities tag ambiguities and implicit spelling errors
the results of the experiment are summarised in figure NUM figure NUM a plots recall versus precision that have been obtained in the early prior to learning stage step NUM after NUM step NUM and NUM step NUM learning iterations
therefore in a subsequent experiment we clustered the head of pps in the collision sets using a set of high level semantic tags for a discussion stwo esl s occurring exactly as the average NUM NUM in ld are in perfect correlation when their mi is equal to NUM NUM
g n p n NUM azienda di credito NUM NUM g n p n NUM vigilanza di credi to NUM NUM g n p n NUM servizio di credito NUM NUM NUM NUM non minimal attachment non consecutive word strings
finally table NUM reports the performance at the step NUM phase of the h r lexical association la NUM we experiment this disambiguation operator just because the hlzr method has among the others the merit of being easily reproducible
however the massively increased coverage obtained here by relaxing subcategorisation constraints underlines the need to acquire accurate and complete subcategorisation frames in a corpus driven fashion before such constraints can be exploited robustly and effectively with free text
thus given a sentence n words long the apb raised to the nth power gives the number of analyses that the grammar can be expected to assign to a sentence of that length in the corpus
we assessed its coverage and that of previous versions on a development corpus and an unseen corpus and demonstrated that the grammar refinement we have carried out has led to substantial improvements in coverage and reductions in spurious ambiguity
although the grammar enumerates complementation possibilities and checks for global sentential wellformedness it is best described as intermediate as it does not attempt to associate displaced constituents with their canonical position grammatical role
we also evaluated the accuracy of parse selection with respect to treebank analyses and by varying the amount of training material we showed that it requires comparatively little data to achieve a good level of accuracy
the results show convincingly that the system is extremely robust when confronted with limited amounts of training data when using a mere one sixty fourth of the full amount NUM trees accuracy was degraded by only NUM NUM
in addition a significant number of observations have been made as to structural properties that appear to run in common through all the cognitive systems
on the other hand some factors with a significant structural role in visual perception are at best minimally represented in the closed class forms of languages
some occur in only a few languages e.g. the category of rate with inflectional indication of fast and slow
the second domain in which closed class forms perform a structuring function is in the conceptual inventory of language in general or of any single language in particular
current theories of semantic change that include such processes as grammaticization and semantic bleaching have been good at accounting for the starting points of such change
the principle is that closed class forms that express spatial or temporal relations are largely topological in character and exclude euclidean specifics of magnitude and shape
as a whole the total inventory of structural concepts that can be expressed by closed class forms exhibits the following property it is hierarchically graduated
this task which represented about NUM of the data for both speaking styles was chosen because it was midway in planning complexity and in length among all the tasks
the current investigation of discourse and intonation is based on analysis of a corpus of spontaneous and read speech the boston directions corpus NUM this lected in collaboration with barbara grosz
for the duration of the experiment the speakers were in face to face contact with a silent partner a confederate who traced on her map the routes described by the speakers
this study revealed strong correlations of aspects of pitch range amplitude and timing with features of global and local structure for both segmentation methods
in addition these concepts fall into a relatively few large major categories each of which structures a conceptualization with respect to some major factor
text and speech labelings for both speaking styles indicate markedly higher inter labeler reliability than do scores for text alone labelings which av eraged NUM and NUM
thus in addition to lending themselves to on line processing local measures may also capture valuable prominence cues to distinguish between segment medial and segment final phrases
these more robust results for text and speech labelings occur even though the data set of consensus labels is considerably larger than the data set of consensus text alone labelings
while scont phrases for both speaking styles exhibited significantly shorter preceding and subsequent pauses than other phrases only the spontaneous condition showed a significantly slower rate
the system processes the spl input by querying different knowledge resources including the nigel grammar the upper model and a domain model eventually producing a realization of the spl plan in the form of an english sentence
splat allows the user to create spl plans in a supportive on line environment a graphical menu driven interface provides guidance on the allowable structure of spl plans and access to the various penman resources such as the upper model and the generator itself
for example if the sentenc in table NUM was in the sentence bank and if the pattern specified that a word with a syntactic part of speech dt determiner was to be followed by a word whose lexical item was person then it would be retrieved
both modules follow an interlingua based approach
proaches has some clear strengths and weaknesses
to handle this problem we are
we are currently investigating other methods for combining the two translation approaches
by extracting the represented structure of concepts
NUM l hoenix is signitlcantly better suited to analyzing such utterances
results of using this combination scheme are presented in the next section
null the discourse processor is a component of the glr translation module
each concept has one or more tixed phrasings in the target language
the former is estimated from the corpus segmented into words
table NUM base forms for butter
figure NUM a forced viterbi phonetic labeling for a
see figure NUM for details of the algorithm
figure NUM applying rules to the base lexicon
for example men are about NUM more likely to flap more likely to reduce vowels ih NUM and er and slightly more likely to reduce lqums and nasals
since ese are coarticulation or fast speech effects our initial hypothesis was that the difference between male and female speakers was due to a faster speech rate by males
r d if NUM tile length of a is larger than or equal to NUM and NUM the concatenated string ab ba can not be segmented as a sequence of two registered words a b b a where a a then record an evidence of inner word co occurrence of a and b
natural language parser analyser is essential for allowing advanced functions in document processing systems such as keyword extraction to characterize a text key sentence extraction to abstract a document grammatiea style checker information or knowledge retrieval natura lmlguage understanding naturai language interface and so on
we limit the information to morphologic fl word md syntactic levels such as the presence of coi ima adverbial noun surface or syntactic similarity NUM without using semantic information for structurm analysis
the dietiomu y contains funetionm words such as postpositional particles auxilim y verbs formal nouns n adverbm nouns q sn conjunctions n adverbs and so on inflective suffixes and exceptional content words which can not be or axe not covered by the allocation rules
table NUM pronunciation sources used to build fully
NUM NUM applying phonological rules to build a surface lexicon
but a general purpose parser requires NUM a laxge dictionary database with more than several tens of thousands words NUM advanced techniques for disambiguation and processing semantics aald NUM substantial machine resources such as a lot of memory and high speed cpu
figure NUM an example of computing the expected word frequencies
figure NUM one step in the generalized forward algorithrn
recailis defined as m std and precision is defined as m sys
table NUM new word extraction accuracy NUM test sentences
though an ideal machine translation system could devour input sentences of unrestricted length a typical stochastic system must cut the french sentences into polite lengths before digesting them
use only gr in the model growing process that is select features based on how much they increase the likelihood l r p
perhaps most intriguing are those phrases that lie in the middle such as taux d inflation which can translate either to inflation rate or rate of inflation
gives the model sensitivity to the fact that the nouns in french noun de noun phrases beginning with systome such as syst me de surveillance and
only recently however have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition
at the other end of the pay scale reside natural language researchers who design language and acoustic models for use in speech recognition systems and related applications
many of the types in this ontology will have associated role constraints for instance a mental process requires a sensor role which must be filled by a conscious entity
by providing a more abstract form of representation text planners using wag need less knowledge of grammatical forms and can spend more of their efforts dealing with issues of text planning
a vag also allows the representation of non verbal moves e.g. the representation of system or user physical actions which allows wag to model interaction in a wider sense
this is desirable so that the sentence realiser is independent of the text planner it can act as an independent module making no assumptions as to the internal representations of the text planner
feature specifications can be arbitrarily complex consisting of either a single feature or a logical combination of features using any combination of and or or not
although these metafunctions apply to both the semantics of sentence size and multi sentential texts this paper will focus on sentential semantics since we are dealing with the input to a sentence generator
language as interaction i.e. an activity involving speakers and listeners speechacts etc interactional meaning includes the attitudes social roles illocutionary goals etc of interactants
ideational potential ideational potential is represented in terms of an ontology of semantic types a version of penman s upper model um bateman NUM bateman et al
in writing a speech act specification the is field is used to specify the the speech act type the same key is used to specify ideational types in propositional units
for trec NUM and trec NUM disks NUM NUM and NUM were all available as training material see table NUM
besides a beginning and an end marker each topic has a number a short title and a one sentence description
systems that provide an unranked set of documents are known to be less effective and therefore were not tested in the trec program
the trec NUM adhoc evaluation used new topics topics NUM NUM against two disks of training documents disks NUM and NUM
one of the important goals of the trec conferences is that the participating groups freely devise their own experiments within the trec task
whereas the corneu results represented a major improvement in performance over the trec NUM algorithms their overall performance dropped by NUM
figure NUM shows the recall precision curves for the NUM trec NUM groups with the highest non interpolated average precision using manual construction of queries
figure NUM shows the recall precision curves for the NUM trec NUM groups with the highest non interpolated average precision using automatic construction of queries
it is interesting to note that many of the systems did critical work on their term weighting similarity measures between trec NUM and trec NUM
secondly language tools are running on the http server with the aids of cgi as well as being ftp ed to users as executable codes
the frequencies of use of the lexical markers in metaphoric contexts are represented in the relevance attribute see example below
for our purpose we choose to present these approaches in two main groups depending on how they initiate the semantic processing
studying the relation between the syntactic regularities and the lexical markers one can observe that the first build the ground where to find the second
on the other hand metaphor will not be a marker if used as the subject of the sentence like in this one
spot trw s multi lingual text search tool
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
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
allow users to select their desired search engine
spot currently interfaces to this fdf archival database
for this functionality internationalized support is inadequate
key terms in the browser display are highlighted
note that a 3sat instance is satisfiable iff at least one of the literals in each conjunct is assigned the value true
the sentences parses that correspond to non consistent assignments each have a probability that can not result in a yes answer
the reduction simply takes the elementary trees of the mps for stsgs and removes their internal structure thereby obtaining simple cfg productions
the proof of the previous section helps in understanding why computing tt e mpp in dop is such a hard problem
and a third possible solution is to adjust the probabilities of elementary trees such that it is not necessary to compute the mpp
then pi NUM NUM NUM ni for some real NUM that has to fulfill some conditions which will be derived next
elementary trees exactly one of the disjuncts has as a child the terminal t in each of them this is a different one
a word graph over the alphabet q is q1 x x qm whereqic q foralll i m
the probability of an elementary tree rooted by a literal is NUM the probabilities of elementary trees rooted gm with ck do not change
again the algorithm needs knowledge about classes of segments to fill in these accidental gaps in training data coverage
they ranked the value of a grammar by the inverse of the number of symbols in the system
this is the case with NUM where there is no transition from state NUM on phone uh2
a node is built for this feature and examples are divided into subsets based on their values for it
in addition a subsequential transducer may require many more states than a nondeterministic transducer to represent the same rule
we show that ostia a general purpose transducer induction algorithm was incapable of learning simple phonological rules like flapping
a fundamental debate in the machine learning of language has been the role of prior knowledge in the learning process
the transducer s input string is the phonologically underlying form while the transducer s output is the surface form
the same NUM binary phonological features used in calculating edit distance were used to classify segments in the decision trees
in a transducer based formalism generalizations about segments in similar contexts follow naturally from generalizations about the behavior of individual segments
the models are fully integrated resulting in an end to end system that maps input utterances into meaning representation frames
transition probabilities are estimated directly by observing occurrence and transition frequencies in a training corpus of annotated parse trees
for most utterances we make the simplifying assumption that we need only look at the last i.e.
the NUM elements in vectors x and y correspond to the NUM possible slots in the frame schema
our parse representation is essentially syntactic in form patterned on a simplified head centered theory of phrase structure
for nodes that do not directly trigger any slot fill operation the special operation null is attached
all decisions made by the system are graded and there are principled techniques for estimating the gradations
there is no need to specify an exact set of rules or a detailed procedure for producing such parses
this assumption is justified because the word tags in our parse representation specify both semantic and syntactic class information
in the left diagram the two barcharts show two different accuracy memsures percent correct overall accuracy and percent correct within the f NUM NUM cutoff factor answer set f NUM NUM set accuracy
a stochastic pos tagger assigns pos labels to words in a sentence by using two parameters lexical probabilities p wlt the probability of observing word w given that the tag t occurred
e.g. in a pos n gram model the states are mostly syntactically motivated each state represents a syntactic category and only words belonging to the same category have a non zero output probability in a particular state
unlike other techniques it not only induces transition and output probabilities from the corpus but also the model topology i.e. the number of states and for each state the outputs that have non zero probability
ae a markov model starts running in the start state q makes a transition at each time step and stops when reaching the end state qe
the second method has the advantage of drastically reducing the number of model parameters and thereby reducing the sparse data problem there is more data per group than per word thus estimates are more precise
unlike other techniques it not only induces transition and output probabilities but also the model topology i.e. the number of states and for each state the outputs that have a non zero probability
promem with type NUM cases figure NUM shows the analysis of sentence analysis of the type NUM example kare wa k mqio ga sukida he likes her
results for a tenfold cross validation for these data are shown in table NUM NUM lrl this case the overall mle would lead one to predict that for an unseen form in en the verbal function would be more likely
some of them are determined by the cooccurrence word in the left or right for the latter it will be processed by the next steps
together with the pos matrix some constraints called flag are used to change the pmij from NUM to NUM
in summary word boundary preference is used to prune the word chains which consist of impossibly occurred or rarely occured word segmentation
accordingly thai morphological analysis is not only expected to assign the right tag to the right word but should correct the implicit spelling error prior to parsing
for the proposed model it can work in time efficient and increase the accuracy of word boundary and tagging disambiguation as well as implicit spelling error
then the word generating function will be called for generating a set of candidate words to that position and the process will start pruning at this stage again
instead of using a corpus based approach a new simple hybrid technique which incorporates heuristic syntactic and semantic knowledge is proposed to thai morphological analyzer
this work attempts to provide a robust thai morphological analyzer which can automatically assign the correct part of speech tag to the correct word with time and space efficiency
NUM follow the procedure in section NUM NUM to calculate a composite probability tbr each connection candidate according to fan out applicability specificity of alignment rules relative distortion and dictionary evidence
in our attempt to improve on low coverage of word based approaches we use simple filtering according to fan out in the acquisition of class based rules in order to maximize both coverage and precision
we found that the algorithm can align over NUM of word pairs while maintaining a comparably high precision rate even when a small corpus was used in training
besides since the rules are based on sense distinction word sense ambiguity can be resolved in favor of the corresponding senses of rules applied in the alignment process
our results suggest that mixed strategies can yield a broad coverage and high precision word alignment and sense tagging system which can produce richer information fbr mt and nlp tasks such as word sense disambiguation
therefore by using a class based approach the problem s complexity can reduced in the sense that less number of candidates need to be considered with a greater likelihood of finding the correct translation
lockheed martin has participated in the architecture working group awg and provided a testbed of the architecture functions being defined throughout the course of the contract
new technologies have been developed for attacking problems in information retrieval information extraction and multilingual information processing
in summary hnc s context vector methodology has spawned a wide variety of solutions for government and commercial customers and holds considerable promise for serving as the technological foundation for future information retrieval and information management projects
the genetic system incorporates their research in identifying a range of basic entities i this material has been reviewed by the cia
they have both a pc and sun version at this time and currently access information stored in either sybase or oracle
table NUM performance of baseline specialized model when tested on consistent subset of development
figure NUM distribution of tags for the word about vs article
the word vocabulary and tag dictionary are the same as in the baseline experiment
thus specialized features may be less effective for those words affected by inter annotator bias
where fi hi is the observed probability of the history hi in the training set
the experiments in this paper test the hypothesis that better use of context will improve the accuracy
lastly the results in this paper are compared to those from previous work on pos tagging
search algorithm the search algorithm essentially a beam search uses the conditional tag probability
if one conceptual domain say plants is sketchily represented while another conceptual domain say animals is richly represented similarity comparisons within the two domains will be incommensurable
surface words are also reserved for later processings
j p lcb xu haase rcb media
we call this problem the word referent disambiguation problem
we also discuss a criteria for evaluating filtering heuristics
the result is called the abstracted deep triple set
in the case of substring extraction with NUM or more characters conventional method yielded substring of NUM NUM millions types and the total frequency of them amount to NUM NUM millions
in contrast the method proposed in this paper reduces many of such fractional substrings and condensed into a group of substrings that can be regarded as units of expression
in order to extract rigid expressions with a high frequency of use new algorithm that can efficiently extract both uninterrupted and interrupted collocations from very large corpora has been proposed
thus when extracting n gram strings there is a need to invalidate the related record of the spt so that m gram strings do not become involved in processes to follow
NUM relationships between extracted substrings in extraction of interrupted collocations substrings that are linked to or partially overlap one another are excluded from the scope of extraction
here let s consider combinations of NUM or more uninterrupted collocational substrings in different locations within a single sentence together with a method of determining the frequency of them
in contrast the new method has reduced this to NUM NUM million types total frequency of NUM NUM million times revealing a substantial reduction in fractional and unnecessary expressions
compared with the n gram method most of fractional substring has been deleted and the types m d the number of the extracted substrings have highly reduced
comparing the values of nmcs of record i and that of the record i NUM of the spt from i NUM to i n NUM substrings are extracted and their frequency are determined NUM
although the current status is just an opening spot the long term goal is to bltiklfully automatic servers for korean language information
technical approaches to korean began with the formation of the special interest group on korean information processing under tile korea information science society
worldwide web is composed of hyperdocuments and hyperlinks to handle multimedia data as well as to provide easy and timely access to electronic information
it will be extended to cover special symbols alien strings elliptical or abbreviated words and spell errors to earn higher accuracy
to this point we described the motivation and current status of the korean ip and took a brief look at resources and tools
ontology based lexicon is lexically oriented in that it guides the user to find a pragmatically or contextually equivalent expression corresponding to the source language expression
he need for language engineering plattbrms has been generally recognized and several researches are being undertaken around the worhl
the work is on the phase of feasibility study with intensive locus on collecting korean english bilingual information sources and developing tools for lexicon construction
he first work is done for computer science domain and it has NUM NUM entries each for korean and english
the beginning they focused on korean alphabets and sonw scrappy parts of character processing lacking the global view of the engineering approaches
using the above method we sampled NUM NUM points for each of several values for no no NUM NUM NUM and NUM
it is often convenient to use a slightly different notation when adopting such a regime to make clear that one particular feature value has a privileged status
however a pure unification formalism is often thought to be a somewhat restricted grammatical formalism especially when compared with the rich devices advocated by many grammarians
the paper describes a variety of apparently richer descriptive devices that can be compiled into unification grammars in ways that under normal circumstances will result in efficient processing
we then treat the correlation of the properties with the contexts as a kind of agreement between some feature on the preposition and one on the np
a currently more favored approach is to use a single vp rule or schema or subcat principle and a list of categories subcategorized for by a verb
they point out that what the rows of this array represent is the set of lower bounds of the row element via a bitstring encoding of sets
being complete lattices they also have the property that they can be inverted by taking immediately dominates to be immediately dominated by
the document management process is concerned with form NUM and form NUM documents
this requirement means that domain specific phrase lists can be a shareable resource
a the device is attached to a plastic wristband
NUM NUM NUM verification method demonstration and inspection
this includes the processing of documents which contain multiple languages in a document
this simplifies the generation task somewhat
a query library on the other hand may be modified frequently
components are major parts of an application they are built from modules
continues in these segments are deleted
a single annotation may be associated with multiple document locations spans
annotations may be treated together as sets of the same or differing types
this in turn would allow the construction of systems which make fewer demands on the willingness of users to adjust to misrecognitions and nfisunderstandings and which encourage users to interact with computer interpreters as if they were interacting with human interpreters
we expected that the addition of the concern for efficiency to that for social position would result in a higher level of accommodation in fact the level of accommodation observed in the human interpreted setting was the highest in all three experimental settings
the interactors maintained a level of accommodation high enough to satisfy their concern for social standing but since they were native speakers of the same language and the communication channel was clear and direct they had minimal concern for communicational efficiency
on the other hand this is the most difficult communication environment of the three involving as it does not only the limited understanding of the machine translator but also limited speech recognition a difficult to understand modulated speech signal and rigid turn taking constraints
the interpreters in the human interpreted setting were native speakers of japanese and though they were fluent in english the range of overlap between their english linguistic habits and those of the native english speaking clients was much smaller than that between two native speakers
however because the overlap was calculated for speakers engaging in cooperative dialogues concerned with the same task and via the same media it reflects the extent to which overlap occurs simply because these are speakers in similar situations talking about similar topics
as we have seen the lexicon of basic words and stems is represented as a wfst most arcs in this wfst represent mappings between hanzi and pronunciations and are costless
in the denomi11 we have two such lists one containing about NUM NUM full names and another containing frequencies of hanzi in the various name positions derived from a million names
however it is possible to personify any noun so in children s stories or fables jj nan2 gual meno pumpkins is by no means impossible
however for our purposes it is not sufficient to represent the morphological decomposition of say plural nouns we also need an estimate of the cost of the resulting word
any process involving reduplication for instance does not lend itself to modeling by finite state techniques since there is no way that finite state networks can directly implement the copying operations required
the method just described segments dictionary words but as noted in section NUM there are several classes of words that should be handled that are not found in a standard dictionary
NUM NUM guo2 NUM NUM c ps np g lo figure NUM partial chinese lexicon nc noun np proper noun
on a set of NUM sentence fragments the a set where they reported NUM recall and precision for name identification we had NUM recall and NUM precision
NUM ag clearly performs much less like humans than these methods whereas the full statistical algorithm including morphological derivatives and names performs most closely to humans among the automatic methods
example when malpractice is selected as the root key term a list of multiple word terms will be displayed including multiple key terms such as medical malpractice malpractice cases medical malpractice action medical malpractice claims limitations for medical malpractice etc see figure NUM functionality to shrink and collapse subtrees is also in place
these include four legal topics state tax medical malpractice uniform commercial code and energy and three news topics campaign legislature and executives
for figure NUM the user can move through the document by making use of the scroll bar document buttons in the navigation window or by dragging the mouse up and down while depressing the middle mouse button
though what constitutes a good term still remains to be answered we know that a good term can be a word stem a single word a multiple word term a phrase or simply a syntactic unit
the evaluation is clone with the tuned parameters of the sublanguage component and the weights of the eight scores decided by the training optimization
i w l e i umber of artmes ill sublanguage set sublanguage words are extra ted from the collected sublanguage artmes
we generally tried several parameter values and tile vahles shown in this paper are the best ones on our training data set
but it is very minor and these improvements are offset by a similar number of disimprovements caused by tile same reason
st atist ical langmtge models play a major role in current spee h re cognition systems
language scores are namely the word trigram model two kinds of part of speech NUM gram model and the number of tokens
in other words the combination of the scores should not be done by linear combination at the sentence level but should be done at the word level
under these assumptions the probability of a slot fill operation is p slot n i ft sn l semn NUM sem n semn NUM synn NUM synn synn NUM semupl semup4 synupl synup4 and the probability p s i ft t is simply the product of all such slot fill operations in the augmented tree
under this viterbi assumption the summation operator can be replaced by the maximization operator yielding mo arg max max p m o l h m s p m s t p w i t m d ms t this expression corresponds to the computation actually performed by our system which is shown in figure NUM
the discourse module computes the most probable post discourse meaning of an utterance from its pre discourse meaning and the discourse history according to the measure p m o i h m s p m s t p w i t
meanings ms are decomposed into two parts the frame type ft and the slot fillers s the frame type is always attached to the topmost node in the augmented parse tree while the slot filling instructions are attached to nodes lower down in the tree
NUM fill the origin slot with city boston NUM fill the destination slot with city atlanta these instructions are attached to the parse tree at the points indicated by the circled numbers see figure NUM
in the first phase every parse t received from the parsing model is rescored for every possible frame type computing p t i ft our current model includes only a half dozen different types so this computation is tractable
then for each vector x a search process exhaustively constructs and scores all possible output vectors y according to the measure p y i x this computation is feasible because the number of tacit fields is normally small
meanwhile researchers in content analysis have already experienced the same difficulties and come up with a solution in the kappa statistic
who phraset h o rse i rol abililics are reesl tna t c d
by definition a phrase in this paper refers to a linguistic unit of 1tlore general structure than it is recognized in general from the terms noun and adverb phrases
this proc ss wht n apl lied repeatedly must give a localty ot tint d est inmtion of the l ara rneters
when we checked randomly selected NUM sentence pairs by hand only aa s or all pairs have one to one correspondences between english words and korean words
with the onstraiut that the st tti ov w a ll alignnte tts should be NUM the r ility and equation NUM for i hras l r sl ondence prolmt ility
step t efore iterative applications of fligmnent ste NUM as is illustrated in tigure NUM partof sl eech tagging is don i elbre the actual align meat so that the
the existing methods for the alignment of indo european language pairs such as english and french take words as aligning units and restrict the correspondences between words to be one of the functional mappings one to one one to
correspondences using the tag sequence of phrase and words composing phrase the equatiou NUM can be rewritten as in equation NUM letting phrase match be represented by the tag sequence of phrases as well as words
for example the pause algorithm assigns a boundary between prosodic phrases NUM NUM and NUM NUM but not between phrases NUM NUM and NUM NUM
table NUM shows the average performance of the referring expression algorithm row labeled np on the four measures we use here
studies such as these suggest that our methodologies and or results have the potential of being applicable to more than spontaneous narrative monologues
the resulting algorithms are then tested on NUM new narratives
the input to each algorithm is a set of po
the question is how many l s is significant
level for each distinct tj NUM across all narratives
and the spread is low or NUM
real subjects from the same number of potential boundary slots
we do this by selecting the statistically significant response data
in table NUM the correspondence of these compound nouns was captured only in their constituent level
as a result our method outperforms conventional methods for texts of different lengths and different domains
note here that combined performs better than statistics in the case of longer texts too
both combined and dictionary use a cd rom version of a japanese english dictionary containing NUM thousands entries
the sentence pairs that have at least anc word correspondences are determined to be new anchors
we use hereafter the word correspondences whose mutual information exceeds itow and whose t score exceeds ttow
dictionaries merit they can contain the information about words that appear only once in the corpus
statistics demerit the amount of word correspondences acquired by statistics is not enough for complete alignment
a one in asm indicates the intersection of the column and row constitutes a possible sentence correspondence
there is clearly a limitation in the amount of word correspondences that can be captured by statistics
transliteration of ration is incorrectly extracted as a new word
the third type is the concatenation of noun s and particle
however they did not report any evaluation of their word extraction method
they claimed NUM word segmentation accuracy while we clmrn NUM NUM
it is a simple task to count word frequencies in a given text
it is a corpus of approximately NUM million words NUM NUM sentences
it has a variety of annotations on morphology syntax and semantics
known characters like the word bigram model used in the segmentation model
because university is registered in the dictionary
in the first analysis the system considers
because ostia had no knowledge of similarities among phones the induced transducer often had no transition specified for a given phone or had an incorrect one specified
the resulting decision trees describe the behavior of the machine at a given state in terms of the next input symbol by generalizing from the arcs leaving the state
later work in autosegmental phonology and feature geometry extended this assumption by restricting the domain of individual phonological rules to changes in an individual node in a feature geometric representation
NUM when using nondeterministic transducers for example those of karttunen described in section NUM multiple rules are represented by intersecting rather than composing transducers
one of the important properties of finite state phonology is that transducers for two rules can be automatically combined to produce a transducer for the two rules run in series
second even if the vocabulary were larger the necessary sample may require types of strings that are not found in the language for phonotactic or other reasons
using the model of transduction augmented with variables a machine with the minimum two states and perfect performance on test data was induced with NUM NUM samples and greater
a transduction is valid if there is a corresponding path beginning in state NUM and ending in an accepting state indicated by double circles in the figure
purely empirical approaches use a general domain independent learning rule error back propagation instance based generalization minimum description length to learn linguistic generalizations directly from the data
so haudling uncertain input is one way our research may evolve
alld instance selection as described in section NUM
scale can be measured in an absohtte mmmer e.g. lin ear size ill feet yards or millimeters or time ill seconds
but often natural language expressions do not refer to absohttc magnitudes but rather to abstract relative ones as in file case of big
this section contains a brief discussion of the senmntic treatment of adjectives which can not be reduced to the standard property based type of adjectival modification
in the following section we illustrate file structnre of those parts of the lexicon entry in mikrokosmos which bear on the description of adjectival memfing
location is also a conunon relation as in international adj NUM taking place in a set of two or more comltries
raskin NUM because it clearly violates the silnplistic subset forming notion of adjective meaning such that red houses are a subset of all houses
however the a term representing outside probability can not be calculated directly during a parse since we need the full parse of the sentence to compute it
the edge count percentages were generally within NUM of their final values after processing only NUM sentences so the results were quite stable by the end of our NUM sentence test corpus
we also derived an estimate of the ideal figure of merit which takes advantage of statistics on the first j NUM tags of the sentence as well as tj k
some figure of merit is needed by which to compare the likelihood of constituents and the choice of this figure has a substantial impact on the efficiency of the parser
in some of our figures of merit we use the quantity p nj k t0 j which is closely related to outside probability
the p n k t0 j term is just o l defined above in the discussion of the normalized o lfl model
this can result in a thrashing effect where the system parses short constituents even very low probability ones while avoiding combining them into longer constituents
we then computed several quantities for best first parsing with each figure of merit at the point where the best first parsing method has found parses contributing at least NUM of the probability mass of the sentence
the inside probability of the constituent n k is defined as NUM nj k p tj kln i where n i represents the ith nonterminal symbol
the purely temporal knowledge in mikrokosmos is recorded with the meaning of the entire proposition and adjective entries are not marked for it
for exampie if the dictionary look up fails the allocation rules extract each sequence of character in which all of the characters belong to the same character set
otherwise it is about the same as the brate force implementation with quadratic time complexity
this paper will focus in NUM byte characters because their internal codes are widely used
verbadjective j j and ad rerb q and successive adjunctive words l
second we noticed that dealing with syntactic ambiguities creates a large processing burden and even using semantic information does little to assist syntactic analysis at the current level
figure NUM observed running time of top k bfs on section NUM of penn treebank wsj using one 167mhz
sadverb ddhl adverbial particle dhl object noun onhl preposition pnfl the head of the prepositional phrase pnhl and subject snhl
when there was more than one possible semantic class for a word form we gave all of them NUM and expanded the input table using all the semantic classes
it is a NUM ary tree with the depth of NUM or NUM the semantic class at each node of the tree was represented by NUM top level to NUM lowest level digits
tsurete iku escort tsurete iku escort tsurete iku escort tsurete iku escort hakobu carry hakobu carry hakobu carry hakobu carry
although this heuristics does not guarantee to output the a that has the minimum errors on pen data the value was not too far off in our experience
we applied lasa NUM to bilingual english and japanese data and showed that it successfully leaned the generalized decision tree to classify the japanese translation for take
one may recognize that the restrictions in figure NUM are not semantic categories but are words this tree was learned from table i which contains word forms for the values
the decision tree learning algorithms dtlas are getting keen attention from the natural language processing research comlnunity and there have been a series of attempts to apply them to verbal case frame acquisition
we can replace the similar nouns in a case fl ame tree by a proper semantic class which will reduce the size of the tree while increasing the prediction power on the open data
different types of criteria may be used together such as a selection statement with keywords
the user shall be able to establish the document detection criteria in a variety of formats
renaissance technologies stephen a della pietra renaissance technologies
represents the space of all probability distributions
this is the case in the present situation
we will denote the gain of this model by
this strategy is implemented in the algorithm below
the maximum entropy concept has a long history
thus informed they manipulate their lineups accordingly
finally we pose the unconstrained dual optimization problem
table NUM summarizes the primal dual framework we have established
scores for sentence based comparisons will always be lower than scores for paragraph based comparisons because there will be fewer spurious hits
the best filter cascades improve lexicon quality by up to NUM over the plain vanilla statistical method and approach human performance
more of the incorrect translations are filtered out in the pos cog column making room for foremost
the filter based approach is designed to identify likely source word target word NUM pairs using a statistical decision procedure
the independent variable in the experiments was a varying combination of four different filters used with six different sizes of training corpora
table NUM is unusual it is atypical for more than two of the filters studied here to incrementally improve one lexicon entry
for example set all 7hijal NUM NUM
the problematic words are marked with
after one iteration the agreement with the original segmentation decreased by NUM percentage points while the agreement with the human segmentation increased by less than one percentage point
since we lack an objective criterion to measure the accuracy of a segmentation system we ask three the corpus has about NUM million characters and is coarsely pre segmented
third it seems puzzling that the trigram lm agrees with the original segmentation better than a unigram model but gives a worse result when compared with manual segmentations
and speech processing the johns hopkins university
an iterative algorithm to build chinese language models
at tel disambiguation the lexeme with the most likely tag is used to get the right entry of the selected word in the dictionary
a parse is a tree generated by a derivation
this suggests a mlmber of avenues for filrther research
and described problems when applying the ordinary sentence analysis to the four types
finally lhc valency structure as shown in figure NUM is obtained
earl processing phase is described below in detail with reference to figure NUM
the explain process edp figure NUM can be used by the explanation planner to generate explanations about the processes that physical objects engage in
for example in biology one encounters many process oriented descriptions of physiological and reproductive mechanisms as well as many object oriented descriptions of anatomy
to address the difficult problem of subjectivity we assembled NUM domain experts all of whom were ph d students or post doctoral scientists in biology
judges were asked to rate the explanations on several dimensions overall quality and coherence content organization writing style and correctness
because knight s operation is initiated when a user poses a question the first task was to select the questions it would be asked
although the knowledge base focuses on botanical anatomy physiology and development it also contains a substantial amount of information about biological taxons
when they are applied to particular structures in a knowledge base the accessors mask out all attributes that they were not designed to seek
the noun phrase generator then returns each functional description to the fd skeleton processor which assigns case roles to the sub functional descriptions
rather it encodes general knowledge that can support diverse tasks and methods such as tutoring students performing diagnosis and organizing reference materials
first to cope with knowledge structures that contain additional unexpected information the kb accessors were designed to behave as masks
the nonsegmentation property and unregistered words cause initial segmentation errors which result in erroneous analysis
this makes it possible to correct initial over segmentation errors and leads to higher accuracy
however the baseline actually fails because it can not capture the unregistered word
NUM proper nouns sometimes cause cross boundary errors at the initial morphological analysis
at this stage ma re analyzes the input compound noun by using newly found words
an input compound noun is first analyzed by jma and segmented into a sequence of registered words
the number of the articles is about NUM NUM which contain about NUM million characters
alter randomly selecting the test samples we confirmed that they were all compound nouns
this paper introduces a baseline to facilitate our evaluation of the effectiveness of our method
the search is continued until every wold in wl is used as a key
class based models may reduce this problem only to a certain degree depending upon the richness of the sublanguage and upon the size of the application corpus
in this experiment we wish to demonstrate that the type of syntactic ambiguities are much more complex than v n pp or n n pp sentences
each measure is evaluated for a different value of the testing threshold r that varies from NUM NUM to NUM NUM from left to right in fig NUM a
persistently ambiguous esl of the corpus may remain still ambiguous within the corresponding collision sets limbo will be their place forever
certain results can be obtained with purely statistical methods but there are many complex cases for which there seems to be a clear need for less shallow techniques
instead we use high level tags human time abstraction etc manually assigned in itafian domains and automatically assigned from wordnet in english domains
let mcpi ei be the mutual conditional plausibility NUM of ei the posterior probability of el pposti is defined as mcpi ei
to select the best value for r we measured the values of recall and precision defined in table NUM according to different values for r
hence once the learning algorithm reaches the hard cases and is still forced to discriminate it gets at stuck and may take accidental decisions
figure NUM shows an application of build in which the action is join vp
prov n and r li i via lay in lay ottl lagging for six mouth el auk rs with daily int eract ions llllolig i r el aukcrs and i iei w en i h l r el ank q s and i h atr g ra li iiitariali
je vous prie d agrdcr chbre madame l expression de men entier ddvouement
the cognate filter disallows all candidate translations of french premier whenever the english cognate premier appears in the target english sentence
when this happened the current implementation of the word alignment filter used several heuristics to choose at most one partitioning locus per word
so s and t can be used as loci for partitioning the source and target sentences into two shorter pairs of corresponding word strings
following this line of reasoning we have started to work on a user friendly interface for a bilingual lexicon english japanese
yet networks are also useful for resource development as they allow to reduce the gap between the developing team and the end user
for example the conjugate project between melbourne universi ty and tsukuba university having started to put ja panese call software on an ftp server http www
parsers while finding a freeware parser is not a problem anymore if you do n t know where to get hold of one just send me an e mail we have developed different parsers it is no easy to find the right kind of grammar for teaching purposes
each example being linked to that part of the corpus from which it has been taken each word in the example being linked to the corresponding dictionary entry
two features of our prototype are worth mentioning a the tool is implemented as a www application http tanaka www cs titech ac j p inui jld html
obviously this kind of communication is meaningful for the student since s he can talk about things s he is concerned with
students can even ask for help from friends or ex perts living elsewhere on the other side of the globe
last but not least existing nlp technology such as parsing or machine translation could be incorporated into the development of intel ligent dictionaries
the next stage consist in moving away fiom a fully clientserver set np to a semi stand alone implementation based on the platform independent language java
when writer s support and verification tools have gone far beyond present myopic spellers and grammar checkers text production may become a controlled process where the needs for clarity consistency continuity and innovation are skillfully balanced by uniquely well informed planners with effective tools at their command to steer development along rational lines
we can also note how writers intentionally simplify their style to make it amenable to existing unsophisticated translation tools
in some fields we can sense the writers anxiety to catch the eye of current poor retrieval systems
distortion or improvement effects of information technology on the development of natural languages on some occasions during coling
can we note more than mild tendencies towards stereotypisation and slightly more ominously an increasing verbosity
besides the distinction between man machine and man man tends to be blurred as communication becomes machine mediated to an increasing extent
as toohnakers is it our duty to suggest opportunities and issue early warnings as well as to supply on demand
established formulations are preferred particularly in titles to please not the reader but the computerised reader s digest
some countries the scandinavian countries being very clear examples have a national language policy with rules and recommendations generally taught and widely accepted to promote consistency clarity and continuity combined with adequate doses of motivated changes
we are just beginning to see non trivial computational support alerting writers and editors of e.g. provincialisms such as americanisms in british english and vice versa eurospeak jargon in the european national languages he when women are meant to be included high brow words and style when a broad audience is addressed
we denote this word graph with qm if qi q for alll i m
3without loss of generality we assume that the forthe variables ul un
a problem is in np if it is decidable by a non deterministic turing machine
computing the mps from a word graph is np hard even under sci gs
tree languages stsgs can generate tree languages that are not generatable by scfgs
the np complete problem which forms our starting point is the 3sat satisfiability problem
this method assumes full independence beyond the borders of elementary trees which might be an acceptable assumption
therefore if a sentence is generated by this stsg then it has exactly one parse
note that a parse can never be generated by more than n derivations of the first type
e.g. eine einfache fahrt kostet 6dm the ticket costs 6dm
wh wh question y n yes no question a answering s stating i imperative
NUM anything at all the system must know where the user wants to travel
the networks are restricted in that they omit some of the incongruent mood codings
concept to speech systems avoid the latter problem by generating spoken output from a pre linguistic conceptual structure
initial requests utterance b results from a real information need on the system part
the key systems are subsystems of the basic mood options see section NUM NUM
the networks are primarily based on the descriptive work by NUM
for a proper validation we need to analyze larger quantities of dialogue in order to have an empirically sound foundation
for a graphical overview of the stratified architecture we just described briefly see figure NUM
qlfs give partial descriptions of in present address dublin city university dublin NUM ireland josefocompapp dcu
initially those icd specifications will be based on the module interfaces in the first applications built under the tipster text phase ii program
of course the architecture also does not prohibit an application from providing additional capabilities which are outside the scope of the architecture
the identification of other part types will be added on a case bycase basis by architecture users who require the exploitation of those parts
three external interfaces are identified the document the information request the output section NUM NUM discusses documents in more detail
the manager is primarily concerned with the cost and staffing issues both immediate and long term which are associated with an application
procedures for establishing an application s compliance with the architecture as well as procedures for extending the architecture itself have been established
it provides a component and module design which has been jointly developed by a significant number of providers of advanced software of this type
on the one hand it is likely that the architecture will define specifications for more modules than are needed by any particular application
the architecture may also help the researcher to look for gaps in the technology since all modules in tipster applications will be documented
other general purpose methods that can he used to maximize include gradient ascent and conjugate gradient
in generating y the process may be influenced by some contextual information x a member of a finite set x
nevertheless the basic translation model has one major shortcoming it does not take the english context into account
the basic model is blind to context always assigning a probability of NUM NUM to dans and NUM NUM to pendant
fl x y lcb NUM otherwiseify enandaprilei i NUM i i
templates NUM NUM and NUM consider each in a different way various parts of the context
thus a common task in machine translation is to find safe positions at which example of an unsafe segmentation
we can consider each segment sequentially while generating the translation working from left to right in the french sentence
but the data contains only so much expert knowledge the algorithm should terminate when it has extracted this knowledge
within text slices where ahab is frequently mentioned the intra textual cohesion may similarly be strengthened
this happens because subjects are multiply assigned to extraction tasks
k NUM if there is complete agreement among the raters
shown in table NUM are some sample encodings of sentences in terms of the attributes above
NUM NUM collecting data on s lrnmary extraction by humans
each of the raters assigns each object to one category
sin table NUM there are more raters than subjects
k NUM if there is no agreement other than that which is expected by chance
table NUM human reliability and precision of abstracting by extraction averaged over NUM run
in brief the acceptance probability of y is a y x min NUM NUM r
as sketched at the beginning of section NUM we can generate dags from an av grammar much as proposed by brew and eisele
for purpose of illustration take feature NUM to have weight fll v and feature NUM to have weight f12 NUM NUM
for example let x be the tree in figure NUM and let ql be the probability distribution over trees defined by model m1
it is not immediately obvious how to use the iis algorithm to equip attribute value computational linguistics volume NUM number NUM grammars with probabilities
we create an unlabeled node to represent this grandchild of x and direct appropriately labeled edges from the children yielding b
that is we can define the dag weight c corresponding to rule weights fl ill fin generally as
probability NUM according to hence they can be ignored in the divergence calculation but probability NUM NUM according to ql
in this case a child with edge labeled NUM already exists so we use it rather than creating a new one
the section below describes the basic case based learning algorithm used throughout the paper
throughout the paper we employ a simple k nearest neighbor case based learning algorithm
results for the restricted memory bias representation are shown in table NUM
table NUM incorporating the recency bias by
it discards irrelevant features from the representation
again we use a simple majority vote and break ties randomly
this effectively dis table NUM results for the restricted memory bias representation
as cases are created they are stored in a case base
in the sentence i saw the boy who won the contest
we begin by specifying a large collection of candidate features
it is a transcription into mathematics of an ancient principle of wisdom
berger della pietra and della pietra a maximum entropy approach b
figure NUM on the other hand illustrates a safe segmentation
for historical reasons these proceedings are sometimes called hansards
these applications demonstrate the efficacy of maximum entropy techniques for performing context sensitive modeling
disallowing inter null x y for sentence segmentation
each seems to be making rather bold assumptions with no empirical justification
we define candidate features based upon the template features shown in table NUM
clues from character sentence and paragraph levels are employed to resolve chinese personal names
to test the performance of language models a good testing corpus is also necessary
NUM for the word sequence occurring more than fmin times the numbers of total occurrence and total bilingual co occurrence are counted
table NUM shows examples of translation results using genetic algorithms
table NUM shows the results of experiments using genetic algorithms
to resolve this problem we applied genetic algorithms to the method
the method of crossover was described in the section on learning process
the method of mutation was described in the section on learning process
the method for determining optimal translation results was described in subsection NUM NUM
the former are called sentence translation rules and the latter word translation rules
the system is expected to continuously evolve to higher learning and translation capability
therefore likes and wa are the crossover positions
in general there may be application dependencies with the same term having different meanings depending upon the applications NUM verification method demonstration and inspection
the architecture shall provide for the use of common parts of speech word lists with a range of different tags to support various document processing tasks in different applications
some items such as lexicons gazetteers glossaries dictionaries marking rules and grammars rules are comparatively stable subject to normal maintenance operations
compiled query is the detection component specific form of a query generated by a detection component from a statement of relevance and is understandable only by the detection component
the architecture shall recognize various types of annotations associated with specific passages of text as specified by a text span with begin and end values or the whole document
the goal is to permit the development of template schema and fill criteria connected to component specific patterns via an interactive process by a user with subject domain knowledge
auditing and administrative support shall be available for marking and filtering data to ensure proper document data access distribution and viewing and also to record improper access attempts
the inputs and outputs shall be defined with sufficient detail to allow an application s tipster components and modules to be exchanged with similar tipster components or modules
NUM NUM NUM NUM enhanced far term requirement an enhanced library with richer membership which covers more complex document structures than the basic library shall be implemented over the long term
query and detection criteria refinement refinement of all types of detection criteria including the queries for retrospective search or routing shall be supported by the architecture
using this extension to conjoin we can handle sentences that have the gapping construction like sentence NUM
the adjunction operation would now be responsible for creating contractions between nodes in the contraction sets of the two trees supplied to it
in fi r NUM the notation denotes that a node is a non terminal and hence expects a substitution operation to oc cur
dominates during a derivation allowing us to recognize cases of gapping such as john wants penn to win and bill princeton
an ltag is a set of trees elementary lrees which have at least one terminal symbol on its frontier called the anchor
NUM selecting the leftmost np as the lexical site for the argument since precedence with the verb is maintained by this choice
tag g a cooked o j lcb rcb hn o beans fi dried
for example in fig NUM the tree corresponding to eats cookies and drinks beer would be obtained by
the second activity concerns dialogues on logic games based on pictures or puzzle pieces
cumulative error builds up when an incorrect hypothesis is chosen and incorporated into the discourse context causing future predictions based on discourse context to be inaccurate
our sohttion was to modity the discourse processor s constraint processing mechanism making it possible to bring more domain knowledge to bear on the disambiguation task
moreover since in this case incorrect disambiguation does not adversely affect translation quality it ramies sense to handle this ambiguity in a purely non context based manner
n the face of cumulative error both of the two discourse combination approaches suffer fl om performance degradation though to a different extent
in this paper we concentrate on the first two issues which are imperative to integrate a traditional plan based discourse processor into the disambiguation module of a whole system
we gange ambiguities in terms of differences between members of the set of ilts produced by the parse r for the sail e source sentence
these three lloll context based scores will be referred to later when we discuss comt ining non eontext i ased l redict ions with context based ones
once the information from the graded constraints and the focusing scores is awdlable the challenging problem of combining these context based predictions with tile non context based ones arises
their purpose was to eliminate provably wrong inferences and it this way to give the focusing heuristics a higher likelihood of selecting the torte c
the grapher and an underlying application therefore can behaw in a way that the grapher is not only a way to visual st the data of t he application but also providc s a real interface i etween user and af plication
for each type of a modifier of a polish verb the type of the corresponding modifier of the english equivalent is given in the dictionary
the translation algorithm is non modular its only results are the phrasal structure and the surface form of the english expression corresponding to the polish input
the algorithm makes it possible to transfer special verbal constructions called t constructions and t shifted constructions where t denotes a type of a modifier
the method replace symbol by parameter may be used when right hand sides of productions for different symbols start with the same sequence of symbol s
this means that the process of searching a phrase is executed word by word in contrast to the letter by letter search in the automaton of single words
the research plans for near future include consulting a large corpus of computer texts in order to create a bilingual dictionary which will enable the translation of a wide range of polish computational texts into english
the t shifted constructions are used in analyzing sentences in which an object of the polish expression should be transferred into the subject of the english expression e.g. the polish sentence
if verb modifiers in the clause fulfil none of the types given in the dictionary for the predicate then default values for the english verb equivalent and the english modifier types are taken
english equivalents of polish inflected forms are not derived
its alphabet coincides with the polish orthographic alphabet
a surprising result is the importance of low count events ignoring events which occur less than NUM times in training data reduces performance to NUM NUM
each sub tuple predicts noun or verb attachment with a weight indicating its strength of prediction the weights are trained to maximize the likelihood of training data
firstly while we have shown the importance of low count events some kind of smoothing 1nay improve peribrmance further this needs to be investigated
a transformation is a rule which makes an attachment decision depending on up to NUM elements of the v nl p n2 quadruple
the accuracy figure for a particular tuple is obtained by modifying the algorithm in section NUM NUM to use only information from that tuple at the appropriate stage
the figure below shows the results for the method on the NUM test sentences also giving the total count and accuracy at each of the backed off stages
we constructed NUM sets of coded texts from the corpus by varying the threshold value of agreement from NUM NUM to NUM NUM
note that the method here gives a rise to NUM possible divisions and an equal number of corresponding decision tree models
imagine for instance that nine subjects are asked to extract three most important sentences from a text with ten sentences
recall is the ratio of cases assigned correctly to the yes category to the total yes cases
the reliability of human judgements is evaluated by the kappa statistic a reliability metric standardly used in behavioral sciences
the values of p a and p e are then combined to give the kappa coefficient k
the value is the ratio of the number of sentences preceding to the total number of sentences in the text
text type this attribute is categorical and identifies the type of a text to which a given sentence belongs
we begin in section NUM NUM with a review of the general theory of statistical translation describing in some detail the models employed in candide
instead of attempting to handle all possible grammatical errors the grammar checker identifies certain specific types of grammatical mistakes that appear more regularly than others in the present domain of application
in our example tree of figure NUM both noun phrases have exactly one subtree np4 nl z NUM the verb phrase has NUM subtrees vp3 NUM and the sentence has NUM sl NUM
at the moment the lexicon has about 18k nouns NUM NUM k adjectives NUM NUM k transitive verbs 2k intransitive verbs among other categories
NUM it is also noteworthy that the results are much better on bod s data than on the minimally edited data crossing brackets rates of NUM and NUM on bod s data versus NUM on minimally edited data
if the simple method is used then no new non terminals are introduced using this method it is not possible to recover the n ary branching structure from the resulting parse tree and significant overgeneration occurs
the user interface of the system includes a direct interaction with the prolog interpreter as well as an internet interface
an english grammar checker as a writing aid for students of english as a second language
in contrast the method proposed here uses part of speech trigrams
each method involves a training phase and a test phase
table NUM summarizes the overall performance of all methods discussed
it can be seen that trigrams and bayes each have their strong points
let jr be the set of features that match a particular target occurrence
where the system alternately suggested replacing it s with its and vice versa
the breakdown columns give the percentage of examples under each condition
the improvement in performance of tribayes over its components is verified experimentally
suppose for a moment that we were applying a naive bayesian approach
lit our case the linguistic unit is defined to be p for paragraph NUM the output of the th being for example one can refer to the tag p in order to define this structure being the linguistic unit to be processed
p NUM predicate logic is based on the syntax of the standard predicate logic but proposes a new dynamic interpretation of the quantifiers and connectives which allows the binding of variables within and outside their scope depending on the interpretation of the corresponding expressions of the natural language
the fact that the existential quantifier in dpl is interpreted as a quantifier which can bind outside of its syntactic scope allows to say that we provide a compositional treatment of the utterance the second sentence being interpreted as it comes without referring to some metalinguistical representation or process
since the existential quantifier is interpreted as being able to quantify outside its scope also in combination with the conjunction and the sequencing of sentences the information concerning the possible antecedent is going to be passed on to following sentences which could be subsequently uttered
not every quantifier or connective has the dynamic property of binding outside of its scope the universal quantifier for example can bind within its scope but not outside of it NUM every man walks in the park
other segmentation models such as part of speech trigram and word unigrarn can be used in the same manner
introduction standard symbolic machine learning techniques have been successfully applied to a number of tasks in natural language processing nlp
the retrieved case can either be used directly or after one or more modifications to adapt it to the current problem solving situation
the case retrieval algorithm was modified slightly to prefer cases among the top k NUM cases that match the current word
in summary this paper begins to address the issue of algorithm vs representation for case based learning of linguistic knowledge
in addition the restricted memory bias alone does not state which chunks or features to keep and which to discard
in the sections below we describe the recency bias the restricted memory bias and the subject accessibility bias in turn
for example the cbl system must decide that who is a relative pronoun that refers to the boy
modifications to the instance representation in response to these biases either directly or indirectly change the feature set used to describe all instances
figure NUM exact match of a perfect reranking scheme for the top n parses of section NUM of the wsj
the spatter parser is a history based parser that uses decision tree models to guide the operations of a few tree building procedures
this paper presents a statistical parser for natural language that finds one or more scored syntactic parse trees for a given input sentence
since each cell concerns a specific combination of feat ures this provides a way to estimate probabilities of specific feature combinations from the observed frequencies ms the cell counts can easily be converted to probabilities
contextual probabilities p ti ti NUM t NUM null the probability of observing tag ti given that the two previous tags ti NUM t i NUM occurred
since the model makes an implicit independence assumption between the utterances the corpus probability is calculated by multiplying the utterance s probabilities yielding NUM NUM NUM NUM NUM NUM
the relative frequencies of pairs or triples of groups categories clusters are used as model parameters each group is represented by a state in the model
is that it can recognize that a word the type belongs to more than one category while each occurrence the token is assigned a unique category
thus the introduction of these constraints does not reduce the order of the time complexity but it can reduce the constant factor significantly see section about experiments
the best division into k NUM classes for some model of size n is the creation of classes that all have the same size n k or an approximation if
we must first analyze the original text using a robust grammar that can produce a reliable semantic interpretation of the text
when a computerized call is made to a former prisoner s home that person answers by plugging in the device
another rst relation that is very important for summarization is restatement because restatements are a good indicator of important information
ash says the ultimate goal is to use it to get about forty out of jail early
searching for similar lfs captures important information that is restated many times in the text
the cb is defined as the highest thematically ranked element in the previous utterance that also occurs in the current utterance
after the text has been segmented we need to decide which of the discourse segments are important for the summary
the a and alternating normal and italicized script mark segment breaks in the text as determined by centering theory
basically we can either continue to talk about the same entity or shift to a new center
the passes the procedures they apply and the actions of the procedures are summarized in table NUM and described below
it is less clear whether our main clustering result separates number words into different classes for the same kind of reason in figure NUM class NUM contains NUM number words and class NUM contains NUM
at each section and for each cluster we make an estimate of the preferred classification label for that cluster by finding the most common parts of speech associated with each word in the classification under question
ordinary pos language models offer a two level version of this ideal it would be preferable if we could defocus our predictive machinery to some stages between all word n grams and pos n grams when for example an n gram distribution is not quite representative enough to rely on all word n grams but contains predictively significant divisions that would be lost at the relatively coarse pos level
for the following experiments a formatted version punctuation removed all words decapitalized control characters removed of the one million word brown corpus was used as a source of language data NUM of the corpus was used to generate maximum likelihood probability estimates NUM to estimate frequency dependent interpolation parameters and the remaining NUM as a test set
the resulting classification is then passed to the evaluator which works as follows the first stage involves producing successive sections cutting the tree into distinct clusters from one cluster to as many clusters as there are vocabulary items so that an evaluation score can be generated for each level these evaluations can be plotted against the number of clusters
for example in a corpus of conversations about train timetables where numbers occur in two main situations as ticket prices and as times we might expect to observe a difference between say the numbers from NUM to NUM and numbers up to NUM hour numbers and minute numbers respectively figure NUM lends some support to this speculation
we note that in contrast with a bottom up approach a top down system makes its first decisions about class structure at the root of the hierarchy this constrains the kinds of classification that may be made at lower levels but the first clustering decisions made are based on healthy class frequencies only later do we start noticing the effects of the sparse data problem
as it turns out though the rule is in fact not error proof and causes both errors of omission i.e.
l6xicalsenmntie information as well as clues for contextual semantic and pragmatic processing are typically located in the lexicon adjectives being no exception
the function of the ontology is to supply world knowledge to lexical syntactic and semantic processes ibid
NUM the relations that are extracted from the syntactic structure of a sentence ex subject object goal attribute modifier
ordering the dictionary words in terms of decreasing number of occurrences the top NUM of these words account for NUM of word occurrences
for example by can be a relation of manner by chewing time by noon or place by the door
a collection of conceptual clusters together can lbrm the basis of a lexical knowledge base where each c l
in the following we will discuss the different system utterances one by one
see figure in s mc analyses e.g.
analysis and generation had to be straightforward inverse operations
figure NUM root drs with cvcvc template and
figure NUM traditional kimmo style system archi tecture
figure NUM intersection of lexically consecutive root cv template and voweling
figure NUM intersection of lexically consecutive
the symbol o in figure NUM indicates composition
the resulting single transducer is called the lexicon transducer
rules hand compiled into fsts the intersection of the rules is simulated in code
this means timt input words may be fully voweled or diacriticized i.e.
we then discuss code portability and also data portability and we describe the method we have used for a french lexicon showing that portability leads to a more natural computational lexicon
l he similarity t etween two lel ltis or sets o terms can be defined as any of vector similarity liieaslires
here can he multiple interpretations in segnienthig a word due to the ambiguity of fiinction words as illustrated in the following examp e
for tllis r iisott we mol tcd inverse o ulnent fre luency hi liie cxi erinmllls
indices that are carefully chosen to represent a document will bring about the improvement of retrieval performance in accuracy and time efficiency
the index terrns that include simple nouns prodnced as a result of compound noun analysis are weighted which finishes the indexing
a NUM tokenizing and eolni ound noun analysis tokenizing aims at recognizing simple and compound nouns froni a text and reporting them as the the final index terms
the iileasllrl ln hlt of relative infortualion of the two distril utions corresponding to the two tertns rives the distauce between the distrit utions
the potential of a candidate index is often judged on the basis of its discriminating power over a docmnent set as well as its linguistic significance in the document
the lexicon must be uncompiled and compiled back when porting from mac to pc but the whole process does not take more than a dozen minutes
this leads to a second issue i.e. a need for further investigation into the causes of the generally poorer performance in the trec NUM adhoe task
the university of massachusetts examined the issue of dealing with terms having a high fiequency in documents which is also related to document length
the modularity of illico is also a great asset because it makes it easy to define various exercises about language with different levels of difficulty by using linguistic modules lexicon and grammar with broad or restricted coverage by allowing or not the guided mode we obtain a lot of different exercises among which one can choose the one which is suitable for each user s capacities
it can be integrated into a more comprehensive nlp system to recover missing information for purposes of text and dialogue understanding
by contrast the bare adverb beautifully is an adjunct daughter of a vp headed by an empty verb in NUM
it will be a useful component for source analysis in machine translation text understanding systems and discourse interpretation systems
in a se oik xl erinlent kb msisi s of NUM NUM NUM vs v l ai terns ext racl ed
let be the prior probability pprior
figure NUM percentage of collision sets vs number of collid
NUM cluster the collocational data according to semantic categories
NUM repeat step NUM NUM on the remaining i.e.
all these approaches need extensive collections of positive examples i.e.
tables NUM and NUM summarize the results of the experiment
a testset of NUM NUM hand corrected collision sets was built
sense disambiguation acquisition of subcategorization frames
ppdisambiguation has been significantly improved by this a cooperative approach
in each phase the corresponding recall and precision have been measured
c 3k s contain multiple concepts interrelated through multil le semantic relations together forming a semantic duster represented by a con
a l tries to tind the most likely analysis i y using the widence conta ined in a knowhxlge ba se of linguistic da ta automa t ically e xtra ted
this is achieved by applying do NUM test a x like statistic
the algorithm also poses the advantage of producing a tagged corpus for word sense disambiguation
fhis is evident from the slightly higher hit rate based on simple dictionary lookup
the algorithnfs pertbrmance was then tested on the two sets of reside and outside data
table NUM shows the connections that are considered in each iteration of the sensealign algorithm
morphological analysis part of speech tagging ktioms identification are performed for the two languages involved
ocr is a major bottleneck for information retrieval systems in terms of speed
the system computes frequencies of word shape tokens to generate a document profile
we obtained a set of word shape tokens from them
however we must pay attention to its computational expense
consequently the number of characters to process became small
however experts do n t always agree on the judgements
most texts are rich in multi word expressions that can not be properly understood let alone be processed in an nlp system ff they are not recognized as complex lexical units
the set mapped to only NUM words without the suffix NUM
similarly we obtained NUM NUM distinct word shape tokens from them
this time one word shape token mapped to NUM NUM words
for example instead of explicitly writing the complicated re above we define a word order macro wovltrg that may be used for all german verbal mwls having no additional idiommxternal arguments wov1arg
therefore the restricted lexical and syntactic variability of mwls and their idiosyncratic peculiarities need to be expressed in the computational lexicon in order to be able to recognize the full range of their occurrences
merging without a constraint continues until only three states remain the initial and the final state plus one proper state
the second experiment uses the bigram model with NUM NUM states as its starting point and imposes no constraints on the merges
as the following experiment will show the exact points of introducing discarding constraints is not important for the resulting model
it has NUM NUM states which correspond to the NUM NUM words plus an initial and a final state
after a number of merges have been performed the constraint is discarded and a weaker one is used instead
for the trivial model and u pairwise different utterances the probability is p simtri NUM u
this paper investigates a fifth method for estimating natural language models combining the advantages of the methods mentioned above
this choice of a starting point excludes a lot of solutions which are allowed when starting with the maximum likelihood model
for this purpose the maximum likelihood markov model is chosen i.e. a model that exactly matches the corpus
if sense selection is instead performed when syntactic processing is completed on the assumption that the words involved do not differ syntactically then there will only be two parses and three lexical disambiguation decisions
the way the boolean encoding works has to allow for elements to be conjoined one at a time but it can not require that all the elements are present simultaneously for this very reason
this means that any y categories following this one will be recorded in the store of the x daughter and will have to be consistent with the constraints recorded on this y daughter s right feature
although there is only a single entry for send it will on either a left corner or head driven approach to parsing initiate parsing hypotheses for each distinct vp rule whose head unifies with it
a simple though rather artificial illustration of this phenomenon might be a treatment of the semantics of prepositions that regarded them as ambiguous between different senses depending on which type of np they combined with
thus nothing would prevent us from successfully analyzing a sentence like john sent mary out mary a letter to mary a letter with too many complements or john sent out with too few
clearly when the mother of this rule is unified with the corresponding daughter of rule NUM the effect will be to extend the list of daughters of rule NUM by adding the value of next
the system makes use of statistical information the mutual information scores to make quick and reliable guesses of the locations of these words
it is important to note that such frequencies are not meant to indicate some kind of goodness measure of alternative word boundary interpretations
in this run for example the nodes word and chunk become activated at cycle NUM due to activation spreading from the character node
the role of a breaker is to identify erroneous linguistic structures and set them to dormant restoring any dormant competing structure when necessary
for reference the complete algorithm converted to work with logarithms as it was implemented is presented below let a t fl 7r and p be the hmm parameters after the above transformation and normalization e.g. alj k f
where d is a vector containing the e minimum distance values that correspond to the e state sequences qe lcb qt rcb t NUM t e NUM e which are returned as the best most probable state sequences
this system gave an average score of more than NUM correctly transcribed words overall success in the first four candidates for most of the seven languages it was tested on dutch english french german greek italian and spanish
the computational linguistics volume NUM number NUM experiments on the name corpora resulted in lower scores than the corresponding experiments on the hellenic general corpora reaching NUM NUM for exp NUM NUM NUM for exp NUM and NUM NUM for exp NUM for four output candidates
in the above if we substitute the values of the greek ptgc system n NUM m NUM t NUM e NUM for the symbols we can see that we need no less than NUM NUM NUM bytes for storing these data
the legends of these figures have the form cc n where cc is a two letter code for the corpus domain e1 e2 ne n1 od as described in the beginning of this section and n is either NUM for a first order model or NUM for a second order model
this implies that without any modification the algorithm can be used in the reverse order i.e. for a grapheme to phoneme conversion system widely used in text to speech or speech synthesis systems by just interchanging the phonemic with the graphemic data of the training procedure
the main difference in the success rate table NUM is due to the size of the training corpora the training especially with the street names was inadequate and to the fact that names are usually spelled or pronounced in a more arbitrary way than other words
the following consideration is the basis of the multiple output conversion algorithm let qe lcb ql t q2 t qe t rcb be the globally e best hidden state sequences that end at state qt si at a given time t
for this problem we have already proposed a substitutional light method NUM in kakari uke analysis
first it determines bunsetsu fcatures a for each bunsctsu according to its word constituents
in the initialisation step the feature vectors are classified into NUM classes using a soft vector quantization technique
even the question of a bust positioning oi the units within the spoken corpus is still widely debated
prosody generation assigns the sequence of allophones with some of their prosodic parameters pitch l requency duration
a slovenian iqgure NUM wawqbrm above and spectral below representation of the diphone ac
monly adopted as a compromise between the size of the unit inventory and tile quality of synthetic speech
finally diphone boundaries are determined and pitch markers are assigned to voiced sections of the speech signal
hidden markov model phone segmentation to solve the segmentation problem methods for stochastic modelling of speech are used
table i gives the slovenian phones and their corresponding submodels as they are used li r logatom segmentation
displays boston to denver flights which flights are available on tuesday
figure NUM a sample semantic frame
figure NUM a sample parse tree
this expression can be simplified by introducing two independence assumptions NUM
the discourse history is then updated and the post discourse meaning is returned
the authors claimed that NUM NUM of NUM NUM words in the document were correctly aligned
the connection candidates that are inconsistent with the selected connection are removed from the list
therelbre parameters in an model of distortion based on absolute position are highly redundant
we expect that additional biases will be needed to handle new natural language learning tasks but that in general a relatively small set of linguistic biases should be adequate for handling large number of problems in natural language learning
for consensus sf phrases we again found similar patterns for both speaking styles and both label7t tests were used to test for statistical significance of difference in the means of two classes of phrases
the calculation of reliability for sbeg versus non sbeg labeling in effect tests the similarity of linearized segmentations and does not speak to the issue of how similar the labelings are in terms of hierarchical structure
the present study addresses issues of speaking style and segmentation method while exploring in more detail than previous studies the prosodic parameters that characterize initial medial and final utterances in a discourse segment
the speech was subsequently orthographically transcribed with false starts and other speech errors repaired or omitted subjects returned several weeks after their first recording to read aloud from transcriptions of their own directions
df l speech than did do not necessarily sum to the total consensus agreement percentage since a phrase is both segment initial and segment final when it makes up a segment by itself
parallel investigations on prosodic acoustic cues to discourse structure have investigated the contributions of features such as pitch range pausal duration amplitude speaking rate and intonational contour to signaling topic change
we term these the consensus labeled phrases and compare their features to those of all phrases not in the relevant class i.e. non consensus labeled phrases and consensus labeled phrases of the other types
nal utterances in both prominence and rhythmic properties
NUM corpus a preliminary experiment for obtaining abstract triples as a basis of features of synsets was conducted
table NUM lists the top five noun synsets in the fiat probability groupings of NUM and NUM synsets
table NUM shows the average synset node depth and the distribution of synset node depth of word net1 NUM
this means that the content of each abstracted triple can not be treated as generally or universally true
these methods difl er most significantly in the way they characterize contexts and the similarity of contexts
msca makes the sitnilarit y the satile because they have the sante nearest eotmnon abstraction co
we can use for example wordnet or the edr concept dictionary as a hierarchical conceptual thesaurus
heuristics since the current implementation adepts the brute force approach almost all massively generated deep triples are fake triples
it uses a semantic knowledge base where concepts are annotated wit disli qlli sbiw
distribution based methods can acquire concepts b sed on recurring patterns of words but not on recurring patterns of concepts
n NUM cons m cons n
table NUM comparison of build and check to oper
a standard approach in statistical modeling to avoid the problem of overfitting the training data is to employ cross validation techniques
table NUM shows some randomly chosen noun de noun phrases extracted from this test suite along with p interchangelx the probability assigned by the model to inversion
it includes a six word window of french words three to the left of yj and three to the right of yj
in certain cases mwls can even contradict normal syntactic rules as with by and large or g von haus aus originally lit
we suggest to describe their syntactic restrictions and their idiosyncratic peculiarities with local grammar rules which at the same time allow to express in a general way regularities valid for a whole class of mwls
if the feature does not lead to an increase in likelihood of the withheld sample of data the feature is discarded
for instance in f perdre la tdte to lose one s mind lit to lose the head the noun can be substituted by la boule lit
additional morphologicalfeatures and which does not contain the feature ca in any position define v v letter k y es
the procedure places the rule pronunciation on the queue for later recursive rule application and continues trying to apply phonological rules to the rule pronunciation
the second input is the training data i.e. a set of examples for which the class and feature values as in figure NUM are specified
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
for both t NUM and t NUM extremely few of the NUM training examples are classified as boundary NUM and NUM examples respectively
similarly although our spoken corpus was manually transcribed this could have been automated using speech recognition although this would introduce further sources of error
many of the other rules such as the reduced vowel or reduced liquid rules only apply about NUM of the time
in a tagged lexicon each surface pronunciation is annotated with the names of the phonological rules that applied to produce it
however as a consequence it is more difficult to extract generalizations across classes of phonemes to which rules can apply
furthermore our formalism allows the use of a bigger set of operators such as contains not and etc
the viterbi algorithm is a dynamic programming search which works by computing for each phone at each frame the most likely string of phones ending in that phone
since the timit pronunciations were from a completely different data collection effort with a very different corpus and speakers the closeness of the probabilities is quite encouraging
finally by knowing which rules were used to generate each surface form we can compute a count for each rule
finally we analyze the probability differences between rule use in male versus female speech and suggest that the differences are caused by differing average rates of speech
this allows for feedback fiom the users during the development phase
we have also shown that previous results were partially due to an unlikely choice of test data and partially due to the heavy cleaning of the data which reduced the difficulty of the task
these results are significant since the dop model has perhaps the best reported parsing accuracy previously the full dop model had not been replicated due to the difficulty and computational complexity of the existing algorithms
we show that bod s results are at least partially due to an extremely fortuitous choice of test data and partially due to using cleaner data than other researchers
we can however find an upper bound on average case performance as well as an upper bound on the probability that any particular level of performance could be achieved
for instance if the maximum probability parse had probability NUM NUM then he would need to sample at least NUM times to be reasonably sure of finding that parse
we assume that the number of ambiguities in a sentence will increase linearly with sentence length if a five word sentence has on average one ambiguity then a ten word sentence will have two etc
the probability that a potential constituent occurs in the correct parse tree p x ws wtls wl wn will be called g s t x
then for trees such as the probability of the tree is b b a NUM the other six cases follow trivially with similar reasoning
the n ary productions can be parsed in a straightforward manner by converting them to binary branching form however there are at least three different ways to convert them as illustrated in table NUM
for the frequencies of types occurring at least once in the sample the average of the sample and good turing adjusted frequencies is a useful heuristic
hence the probability that after sampling n tokens the next token represents an unseen type is less than v n NUM n
hence it is often ignored in the hope that violations of the randomness assumption will not seriously affect the accuracy of quantitative measures and estimates
increasing the number of measurement points increases the degrees of freedom along with the deviance and the optimal value of the p parameter remains virtually unchanged
in max havelaar a number of words in heid such as waarheid truth and vrijheid freedom are underdispersed key words
note that for de telegraaf du k does not capture the downward curvature of d k as well as it should for large k
we have seen that intra textual and inter textual cohesion lead to a significant difference between the expected and observed vocabulary size for a wide range of sample sizes
it is shown that this bias does not arise due to sentence bound syntactic constraints but that it is a direct consequence of topic cohesion in discourse
distinguish between accepting and non accepting states as there can be no ambiguity about which path is taken through the states
the transducer is deterministic that is there is only one arc leaving a given state for each input symbol
the cost of insertions and deletions was arbitrarily set at NUM roughly one quarter the maximum possible substitution cost
promising results from another field of linguistic learning syntactic partof speech induction suggest that an empiricist approach may be feasible
that is a transduction may fail because the model has no transition specified from a given state for some phone
adding phonological feature biases to such a model could improve its generalization performance just as it improved ostia
can automatic procedures be used for the construction of lexical representations
what do the representations at each of these levels look like like and how would they be constructed
how long does it take to build a structured lexicon with the relevant pieces
to overcome this problem several methods to predict the global structure of long sentences have been proposed
even subjects or other obligatory elements of clauses are omitted very often when they aye indicated by contexts
the sem ntic indicators are the modalities that a wide range of parts of speech have
first we present the encapsulation power of japanese function words which are classified into six levels
conjunctive particles adverbs and even plain forms of verbs can have modmity in the japanese btnguage
corn sequently the pause length and location can be more efficiently controlled with the ldg conjunction levels
it can presume the global sentence structure through lexical information without any analysis in the deep structm e
phdn forms of verbs are the present or past tense forms of verbs without any modal auxiliary verbs
plain forms of verbs in a relative clause which modify a nominal phrase do not have such modmity
the second reason for which kleene is used is to get a flat structure where there is no evidence for recursion
however a flat list of the occurrences of c is built up as the value of kval on the topmost kleene category
the full range of such combinations as is well known can lead to very bad time and space behavior in processing
intuitively we identify each possible value for f with the position between arguments in bv by
then we can write things like stem be stem be have stem have do etc
a frequently occurring case is the following a particular word w has multiple possible realizations of some property p1
depend on the context in context c1 we find p1 and more generally in ci we will find pi
furthermore some of the techniques described can lead in the worst case to overwhelmingly large structures and consequent processing inefficiency
the intent of declarations like this is to ensure that an np or a verb always has these and only these feature specifications
similar studies have shown that this sensitivity appears to be cross linguistic
in our example if the exophoric module runs first the endophoric module ends up only pronominalizing implant in the last clause
necessarily some subtasks such as content delimitation exophoric and endophoric choice then play a less prominent role
one way to achieve this is to see sp modules as treetransformation engines viewing spl and pre spl expressions as trees2
for illustration compare the following two tsls of varying degrees of abstraction for sentences NUM to NUM above a more abstract tsl expression
supplant x cause rolel y function rolel agent x cause actor y
should one of the alternatives later turn out to be incompatible with some module s decision that alternative simply dies and is removed from the blackboard
to determine this the sentence structuring module evaluates whether the predications in the pre spl are to be communicated as a sequence or as a composite complex event
the authors would like to thank the national science concil of the republic of china for financial support of this manuscript under contract no nsc NUM NUM
from this rough character alignment words are aligned using an em algorithm for model NUM in a fashion quite similar to the method presented by brown
while this paper has specifically addressed only english chinese corpora the linguistic issues that motivated the algorithm are quite general and are to a great degree language independent
for intensional adjectives compare a former famous president with a famous former president this ordering may well be incorrect
f structures are a mixture of mostly syntactic information grammatical flmctions with some semantic predicateargument information encoded via the values of
NUM how to f structure a qlf the reverse mapping from qlfs to lfg f structures ignores information conveyed by resolved recta variables in qlf e.g.
the alignment decision however may depend on the previous matches to the extent that the results from dynamic programming inay not be sufficiently accurate
to cot e with the data sparseness problem caused by considering all possible phrases we represent phrases by the tag sequences of their component words
l he table NUM shows the degree of mismatch between english words and korean words that are analyzed by our atttomatic pos tagger and tnorphological analyzer
the proposed method to accomplish korean english aligmnent takes phrases as an alignment unit that is a departure from the existing methods taking words as the unit
when we selected NUM sentence pairs randomly and manually tested aligned results we obtained NUM NUM precision at the phrase level and NUM NUM precision of bilingual dictionary induced from the alignment
by using the notation elk the ree stitnation forntula ofp elk can be induced as equation NUM using m ntethod
an early ttemt t to align asian and ndol uropean l mguage pairs is found from tim work by wu and xia 199d
from a computational point of view the resuiting algorithm h r pronominalization can be sketched as follows
the cf list is ranked according to the default ranking strategy clause theme actor benefic
in order to save computation time however we approximated the weighted sum of called the vitcrbi reestimation
it is designed to be more accurate than the viterbi rcestimation and more efficient than the generafized forward backward algorithm
transliteration of pencil is registered in the dictionary and some university names are registered in the dictionary such as y NUM b stanford university and r NUM cambridge university
if all word hypotheses are not registered in tile dictionary and the threshold NUM is NUM NUM we regard kpq introduction liq linguistics language and q study as tile new words
assume that the ith input sentence is the character sequence kpq which means introduction to linguistics and its best three word segmentation hypotheses are as shown in the relative probabilities of the word segmentation p o hypotheses corresponding to p od ill equation NUM
for example table NUM contains an excerpt from training data while table NUM contains the features generated while scanning ha t3 in which the current word is about and table NUM contains features generated while scanning h4 t4 in which the current word well heeled occurs NUM times in training data and is therefore classified as rare
b generate tags for wi given s i NUM j as previous tag context and append each tag to s i NUM j to make a new sequence c j j NUM repeat from b ifj g NUM find n highest probability sequences generated by above loop and set sij NUM j n accordingly
many of the studies discussed in the preceding subsection take this approach
threshold they concluded that their coding scheme and instructions required improvement
as will be seen in section NUM NUM NUM
na then boundary elseif global pro
negative values indicate the degree to which observed disagreements differ from chance
the hypothetical subjects assign boundaries randomly but with no repetition
the first product of this analysis is a preliminary list of frame elements fen from this domain such as for instance those shown in table NUM
for purposes of this discussion the frame elements are identified here using single letter abbreviations and the structure of an feg is shown as being merely a bracketed list
thus buy a house with a NUM year mortgage involves a different frame from buying a candy bar and entails a slightly different interpretation of the payment element
a similar problem in using labels from frame semantic descriptions in the tagging of corpus lines is due to the fact that separate parts of any single sentence can evoke different semantic frames
s corpus tagging for a sentence like sentence NUM NUM susan took out a huge mortgage to buy that new house
a similar issue arises in cases of anaphora we may or may not resolve the anaphora s referent in the annotations depending on practical considerations of time and effort involved
that is a possible of phrase was omitted from that sentence because its content had been previously mentioned or could otherwise be assumed to be known to both conversation participants
a more compact left anchored grammar can typically be produced by eliminating infinite ambiguity and empty rules without making the other changes necessary to put the input in chomsky normal form
why restrict ourselves to the finite state case
why approach this problem with rule sequences
consider the case of volkswagen of america inc
def phraser labej none phrase is currently unlabelled
none golkswagen lnone of org america inc org
we cut offrule acquisition after the iooth rule
the clearest such example is proper name identification
in each case the underlying method is identical
second word of phrase test last resp
we would like our model to accord with these statistics
in particular advanced processing involving conceptual structuring logical forms etc is still beyond reach computationally
as shown in figure NUM for each predicate both file semantic restriction on a noun including its modifier and file restrictions on case marking particles including its modifier are described for each valency element
figure NUM shows an example of this type of japanese sentence mmlysis for the sentcncc kare wa watashi ni kate no imouto we shoukai shita he theeduced his sister to me
this conversion allows us to correctly bind each modifier to its appropriate valency element becanse predicates in type NUM cases have both a subjective case with ga and an objective case with wo
the input sentence has already undergone morphological analysis and dependency mmlysis i.e. it has been tdready determhled whirl nouns modi y the adjective predicate wilh what sort of poslpositional p nticle expressions
an adverbial particle can correspond to more than one case marking pataicle for example wa is a possible proxy for ga wo hi and so on
NUM proposed method for analyzing japanese double subject construction in order to overcome lhe problems described in the previous section this section proposes a method for analyzing a japanese double subject conslmction having m adjective predicate
example NUM shows that time expression NUM gatsu wa in jmle acts as an adverbial phrase in the sentence NUM gatsu wa ame ga tot it has much rain in june
judgment of doume subject construction if the input sentence contains two modifiers that have adverbial particle wa mid case tam king particle ga it is determined as a double subject constnmtion
in our system the database text is first processed with a fast syntactic parser
the procedure consists of the following two loops
however this is not always the case
we assign a class name for each word
table NUM patterns for extracting eastern asian
our algorithm consists of the following two major steps
this function can be realized by simple pattern matching
table NUM likelihood scores of characters of euro
the next section describes the problem
NUM NUM ambiguity in determhllng character coding system
there are for instance numerous rules that encode specific conventional speech acts e.g. that s g o o d is a confirm o k a y is a confirm acknowledge let s go to chicago is a suggest and so on
this is why this use of case postposition deha with a different semantic restriction is supposed to occupy the same slot nom with the major case postposition ga
each verb subcategorization frame is coded in what we call s block that is placed between m block that contains the very surface level information and c block that is supposed to contain language independent purely conceptual information
all the other subcategorization frames for direct passive indirect passive dative passive and possibility are generated with slightly different variations of alternative surface case markers
these replacement or addition do not alter the basic event structure of the sentences NUM NUM a b f but sometimes just add ambiguities to the syntactic structure as is shown in e.g.
NUM NUM c a simple natural solution to correctly recognize the scope of complex modality features is to recursively apply the pernmtations of surface case set as is described in the following section
NUM NUM that violates the unique case principle shows that ni and he for the verb ageru have to share the same slot in the subcategorization frame
since our approach here is rather empirical so any guidelines that help the lexicon to be uniform in quality we take advantage of other literature that aimed at some exhaustive listing of interesting cases
the alternative case postposition deha also complies with the unique case principle NUM that prohibits other case elements from filling the same slot as nom that is already filled by x deha
NUM NUM a ok x ga y wo z ni age m
in the following section are described the major linguistic requirements of the architecture the case elements of which are free of word ordering and can increase in number when their voice is converted
by contrast in maht systems dictionaries and glossaries are intended for human access only and in almost all advanced mt systems dictionaries but not glossaries can only be accessed and updated by a lexicologist with special tralnin g
in addition to being shorter the new topics were written by the same group of people that did the relevance judgments see next section
it was expected that this type of model would be particularly affected by the shorter topics and experiments were run trying several methods of topic expansion
the conferences were run as workshops that provided a forum for participating groups to discuss their system results on the retrieval tasks done using the tip ster trec collection
the adhoc topics used in trec NUM topics NUM NUM are not only much shorter but also are missing the complex structure of the earlier topics
west publishing used their production system to see how far it differed from the research systems and therefore did not want to use more radical topic expansion methods
used in document ranking but only minimal topic expansion was used with that expansion based on pre constructed general purpose synonym classes for abbreviations and other exact synonyms
how does the notion of level of agreement or data reliability in a behavioral scientist s sense relate to the performance of automatic abstracting
the experiments used NUM texts from three different text categories NUM for each category column editorial and news report
figure NUM the result of chunk detection
figure NUM the result after second pass
ations of a shift reduce parser NUM NUM third pass
the cost function for substitutions was equal to the number of features changed between the two segments
g grouping annotations into sets a ensuring that each part of the application is marked and handled at the proper level of security classification
finally consider one of the most studied predictions of the stack model cases where a pronoun has an antecedent in a prior focus space
thus even when a segment is clearly closed if a new topic has not been initiated the popped entities should still be available
the notion of a cache in combination with main memory as is standard in computational architectures is a good basis for a computational model of human attentional capacity in processing discourse
as a potential alternative to the stack model the cache model appears to be unable to handle return pops since a previous state of the cache can not be popped to
in the stack model any of the focus spaces on the stack can be returned to and the antecedent for a pronoun can be in any of these focus spaces
interpreting this alternate version requires the same inference namely that having all your investments in six month certificates constitutes the negatively evaluated condition of having all your eggs in one basket
since return pops are a primary motivation for the stack model i will re examine all of the naturally occurring return pops that i was able to find in the literature
itowever the rhetorical structure of the text providing information on the semantic links between utterances helps understanding how the content presentation progresses
thus it appears that part of bod s extraordinary performance can be explained by the fact that his data is much cleaner than the data used by other researchers
NUM frames have many properties of stereotyped scenarios situations in which speakers expect certain events to occur and states to obtain
table NUM gives examples of such fegs including fegs with only one member paired with sentences whose constituents instantiate them
our justification for distinguishing them is based on the results of corpus research and on comparison of the elements of this frame with those of other related frames
whether we choose to tag more than what we need for our analysis will depend on the extent to which the process becomes automated and the resources available
this list is ordered according to the likelihood for the elements of being the primary focus of the following discourse
figure NUM illustrates a fine grained distinction of semantic features whose combination specify how a referring expression can be built
pronominalizable then pronominal use a pronoun else nominal build an anaphoric expression
whenever a new utterance is processed the corresponding cf and cb are pushed on the top of the two stacks
the global algorithm implemented has been derived from the network of choices presented above as emerging from the corpus analysis
e is a concept NUM eing defined through a listing of its components use a definite singular noun phrase
figure NUM how semantic features combine to identify the entity in the context user model
the first element in the list is called the preferred center cp u
when an anaphora occurs but a pronoun can not be used a nominal anaphoric expression is built
trigrams may then choose differently when the words are tagged as nouns versus verbs whereas baseline makes the same choice in all cases
more precisely given an observed word sequence o from the speech recognizer speechpp finds the most likely original word sequence by finding the sequence s that maximizes prob ols prob s where prob s is the probability that the user would utter sequence s and prob ols is the probability that the sr produces the sequence o when s was actually spoken
while it is possible to claim the send could be used to indicate the illocutionary force of the following fragment and that a container might even be involved the fact that the parser separated out the speech act indicates there may have been other fragments lost
from the parser we get three acts i a confirm acknowledge okay NUM a tell involving mostly uninterpretable words let s send contain NUM a tell act that mentions a route from detroit to washington the discourse manager sets up its initial conversation state and passes the act to reference for identification of particular objects and then hands the acts to the verbal reasoner
the best sequence of speech acts to cover this input consists of three acts NUM a confirm acknowledge okay NUM a tell with content to take the last train now i take the last train NUM a request to go from albany go from albany note that the to is at the end of the utterance is simply ignored as it is uninterpretable
sometimes the suggestions led to an infinite loop as with the sentence NUM be sure it s out when you leave
to put tribayes on an equal footing we added a postprocessing step in which it uses thresholds to decide whether to suppress its suggestions
on the easy numeric expressions performancc is ahnost perfect precision appears poor for percentiles but this is due to an artifact of the testing procedure
the corc of the rule consists of clauses that test thc lexical context around a candidatc phrase NUM or that test lcxcmcs spanned by NUM
this measure is conservative in the sense that its value is closer to precision p or recall r depending on which is lower
NUM machine crafted rules to evaluate the performance of our learning algorithm we attempted to reproduce substantially the same environment as is used for the hand crafted rules
the patching process as a whole is itself preceded by an initial labeling phase that provides an approximate labeling as a starting point for rule application
about so me of the things that tyt jcally follow from asserting tltat some r lationshi i hohts
i make the following assumptions the aspect of the core verb specifies a relationship between all instant and all event type
telic events have results which are charat terised by propositions which become true at the ii t point of the
then there is nothing in mp sinlple to lead us to infe r the existence of more than one such state of affairs
the combination of have and a t ast participle i will call this the pmuq q wt
as far as th urrent t at er is concerned this is a free ehoic
ambiguities of segmentation into paragraphs may occur in written texts if for example there is a separation by a new line character only without line feed or paragraph
we intend first to focus on prototypical or core uses of the words
the following example is like the famous one time flies like an arrow linguist s examples are often derided but they really appear in texts and dialogues
the functions p and may be calculated explicitly using simple calculus
i a fragment v presents an ambiguity of multiplicity n n NUM in an utterance u if it has n different proper representations which are part of n or more proper representations of u
NUM according to the preceding table this corresponds to a structural consistency of NUM for each component which seems impossible to achieve by strictly automatic means in practical applications involving general users
prioritizing enables adept to process both categories of collections concurrently
based on the source a mapping template is selected
there is one primary mapping template for each data source received by adept
both the security and audit logs can be viewed via the am gui
for each problem sgml tag dp generates diagnostic information
a process begins by accessing the first document in its collection
after a successfully evaluation adept may be made operational
it completes the document processing identifying any remaining errors
the di identifies and separates the rose feed stream into documents
these figures are estimated to increase by twelve percent per month
together with their associated probability scores are passed to the discourse model
most recent frame in this list which we call me
lcb b rcb bodypart his foot healed
c two surnames together like ji
f the sports section all items of news concern sports
foreign proper nouns may be transformed in part by transliteration and translation
this paper proposes various strategies to identify and classify chinese proper nouns
the first is usually adopted and the second is never used
most names are two characters and some rare ones are one character
b ambiguity the abbreviated organization names may be ambiguous
structures of organization names are more complex than those of personal names
of the word sequence first class given the tag class of service npr
constraints are not always inherited however
moreover the ease with which this profile can be discerned in the translated text is assumed to be related to the readability or understandability of the text as a whole
this can then be compared to the connectivity profile of the original text and the degree of correspondence between the two would be a measure for the quality of the translation
a set of essential conjuncts was extracted for english and japanese and a computer interface was designed to support the task of inserting the most fitting conjuncts between sentence pairs
quantifiability given translations of varying quality the degree of correspondence in the connectivity profiles must be shown to correspond to the quality of the translation
we assume that conjuncts which form a closed class can be divided into a limited number of categories that are meaningful in terms of expressing the semantic relationship between sentences
those of c and NUM come ont a bit h w lint the combined mean for c d suggests that this may be partly due to the size of the sample
this approach assumes that the number and order of sentences are invariant in translation luckily for mt systems this is almost always true
this approach hinges on the hope that straight linguistic knowledge comes more naturally to people and is less susceptible to person to person differences than contrived meaning categories
we will get back to the design problem later but with respect to the definition problem our solution was to simply hide the definitions
moreover the target of each relation is taken to be the previous sentence i.e. sentence i NUM see ss NUM for further discussion
model merging starts with a very special model which then is generalized
null the system is intented to be evaluated through the clinical and cognitive evolution of the children first by medical personnel who will use standard evaluation methods and also by the families who will be able to daily test the appropriateness of the system for their children
while computational linguists will certainly have to play an important role in providing linguistic resources grammars lexicon and processing tools it is not clear yet how to decide on the adequacy of the tools browser editors
also there are good chances that within this context new problems arise while old solution turn out not to be good at all in which case the following two questions arise what is the nature of these new problems and in what terms do these new problems have to be rethought
in order to get a clearer picture of these problems and in order to draw the community s attention to the fact that there is a real need and potential for integrating nlp technologies in call systems we propose a panel discussion between specialists in the concerned disciplines linguistics artificial intelligence psychology language teaching
it is both a challenge and a chance to bring nlp unlike 17s intelligent teaching systems cai computer assisted instruction cbi computer based instruction and icai intelligent computer aided instruction which use language or communicating domain specific knowledge call has langage learning as its primary goal
we have to look at the constraints of the system for which they have been designed man
what can nlp based systems teach us about language acquisition linguistic theory and natural language processing in general
what effect can a domain like call or the involved disciplines have on the development of nlp technology
additionally the merged model was much smaller than the bigram model
if such objects exist in the context the system can produce definite descriptions which must agree with the description of these objects
the overall accuracy is high the NUM data was able to predict itself with an accuracy of NUM NUM while the NUM data predicted itself with an accuracy of NUM NUM
research in explanation planning addresses the inverse problem automatically creating this structure by selecting facts from a knowledge base and subsequently using these facts to produce text
we introduce the two panel evaluation methodology and describe how knight s performance was assessed with this methodology in the most extensive empirical evaluation conducted on an explanation system
to perform these tasks successfully an explanation planner must have access to discourse knowledge which informs its decisions about the content and organization of textual explanations
to minimize the effect of factors that might make it difficult for judges to compare knight s explanations with those of domain experts we took three precautions
however this study would be an evaluation of how it behaves in the face of highly incomplete knowledge rather than a fair head to head comparison with knowledgeable experts
knight executes the compute inclusion algorithm with the given verbosity of high which returns true i.e. the information associated with the topic should be included
the fd skeleton processor first determines if each of the essential descriptors is present if any of these tests fail it will note the deficiency and abort
these patterns with hyphens separating the items form keys to two hash tables one records attachments to nps while the other records attachments to vps
the relation taxonomy provides a useful organizing structure for encoding information about second order relations i.e. relations among all of the first order relations
dialogs the rows labeled by default give the portion of the total success rate last row accounted for by kankei s default guess
the rudimentary set of noun word classes used in this project is composed of city and commodity classes and a train class including cars and engines
n is the total number of sentences in the text
a character coding system specifies the mapping between characters and numbers
an approach to speech generation that starts from communication context and maps this to intonational features is the only approach that provides the intonational control needed in dialogue systems to produce speech that human hearers would find acceptable
further from the data that we have collected so far we observe the dialogue move guides the selection of speech function e.g. request corresponds to speech function question whereas offer maps to command
finally since the system is incapable of handing over say a ticket this request can not be realized as an offer let me give you a ticket to your destination
this recursive representation of a dialogue enables cor to account for mixed initiative dialogues where both information seeker and informa ion knower can employ for instance retraction correction and clarification tactics
the linguistic generation resources of german have been enhanced by a systemic functionally NUM NUM NUM motivated grammar of speech that includes knowledge about intonational patterns NUM NUM
since the stratum of context is extra linguistic locating the dialogue model which has originally not been designed to be a model of linguistic dialogue but of retrieval dialogue in general here is a straightforward step
for the current example we suggest the following alternatives null 12we assume that the system has an abstract internal specification of its information needs and that it keeps a record of the information it has already received
second we apply a top down approach we determine how this knowledge can be obtained from tile dialogue model and dialogue history i.e. from the extra linguistic context and thereby verify the applicability of our overall model
finally if the system has not at all understood what the user said it could indicate this by using a clarifying wh question with tone NUM interrogative wh type wh tonic clarifying
choices in the systems of tonality and tonicity lead to an information constituent structure independent of the grammatical constituency whereas choices in tone result in the assignment of a tone contour for each identified tone group in an utterance
i have introduced a cognitively oriented approach for modelling a phenomenon within the processes of information structuring namely the informational status of discourse entities
this value is expressed by different means in different languages for marking verbalized entities in a felicitous way i.e. so that the hearer gets the intended reference
my approach constitutes a cognitively oriented and highly context dependent model for computing the informational status embedded in a concept tospeech model of language production
to use the above model a new version of the viterbi algorithm should be employed one which can recursively calculate the intermediate values of the probability measure d using the second order hmm
the application of these methods however in the reverse process i.e. in phoneme to grapheme conversion ptgc creates serious problems especially in inflectionally rich languages
in the following sections we describe two approaches to recovering such a distribution followed by a description of two baseline metrics
determining when such expressions are discourse anaphoric is part of the task this information is generally not known to the system a priori
in this paper we consider the problem of assigning a probability distribution to alternative sets of coreference relationships among entity descriptions
when just considering the pairwise feature sets these two cases are not distinguished so the resulting probability will be mixed
this work was supported by the defense advanced research projects agency under contract number 4099scl001 e systems inc prime contractor
for instance the probability for the coreference configuration a b c is initially computed to be NUM
this transformation offers two advantages first a four byte integer representation is used for each number instead of a ten byte floating point representation without any loss of accuracy thus reducing memory requirements
rentzepopoulos and kokkinakis phoneme to grapheme using hmm NUM description of the system before the presentation of the proposed system a brief overview of the theory used and the issues addressed in its application are given
the meaning of this algorithm is the following if a pair of phonemes is written as either a single grapheme or a pair of graphemes then this pair is considered a single state
in the second case a few suggestions the execution speed of the system is substantially higher than in rule based or dictionary based systems due to the small number of suggestions per word
the text corpora used for the training and assessment of the system were gathered and transcribed to their phonemic form during the eec esprit NUM project NUM linguistic analysis of the european languages
from this list only two represent existing orthographically correct words i c speak and c apples
depending on the language and the errors of the recognizer this number may be very large rendering the disambiguation of the words by a subsequent language model a time consuming and unreliable task
the system as tested uses a maximum number of NUM states per word a constant that has not yet been surpassed by any word in all the languages in which it was tested
this notion of substitution differs from the traditional ltag substitution operation in the following way in ltag substitution always the root node of the tree being substituted is identified with the substitution site
whet loot nodes undergo contraction the algorithm has to ensure that both the foo nodes share the subtree pushed under them e.g. NUM NUM and NUM
in the conjoin operation however the node substituting into the conjunction tree is given by an algorithm which we shall call findroot that takes into account the contraction sets of the two trees
we use the standard notion of coordination shown in fig NUM which maps two constituents of like type but with different interpretations into a constituent of the same
placing the np nodes at addresses NUM and NUM NUM of the tree a cooked into the contraction set gives us a cooked tl NUM
derived structure or perhaps elementary structures
findroot a cooked lcb NUM NUM rcb will return the root node i.e. corresponding to the s 4in this paper we do not consider coordination of unlike categories e.g.
to count syntactic categories requires linguistic theory to identify precisely what the syntactic category is empirical research to identify the features that indicate where it is present and a computer program to automatically identify occurrences
where corpora are larger words will tend to be more frequent so for the same level of corpus similarity or homogeneity and the same number of degrees of freedom x will be larger
one possibility is to treat a corpus as a single text with chunks specified as first NUM NUM words next NUM NUM words etc the strategy adopted in the experiments described below
sinclair has postulated every distinct sense of a word is associated with a distinction in form NUM we take this one step further and postulate no linguistic distinction without a word frequency distinction any dii erence in the linguistic character of two corpora will leave its trace in a di erence between their word frequency lists
word frequency lists are cheap and easy to generate so a measure based on them would be of use as a quick guide in many circumstances for example to judge how a newly available corpus related to existing resources or how easy it might be to port an nlp system designed to work with one text type to work with another
as one statistics textbook puts it none of the null hypotheses we have considered with respect to goodness of fit can be ezactl true so if we increase the sample size and hence the value of x NUM we would ultimately reach the point when all null hypotheses would be rejected
in brief the method for the homogeneity case is as follows divide the corpus into two halves by randomly placing texts in one of two subcorpora produce a word frequency list for each subcorpus calculate the x NUM statistic for the rtifference between the two subcorpora normalise iterate
the second is that it is very similar to the question how similar are two corpora
the prospects for two independent teams arriving at the same syntactic construction frequency list for the same corpus are slim
it should be noted that a tipster module may in fact comprise several modules from the point of view of a particular application
holding the weights constant for all old features in the field we choose the best weight fl forf how fl is chosen will be discussed shortly yielding a new distribution qfi
in the network a node represents a concept
clas fier quantity aspecttlal figure NUM
they are responsible for identifying and constructing monosyllabic words
many codelets are independent and they run in parallel
this method scans a sentence from left to right
gan palmer and lua a statistically emergent approach
global ambiguity can only be resolved with discourse knowledge
the pair having the highest score is grouped together
the same procedure is applied to each part recursively
this syntactic rule works in sentence NUM
which tries to autonmticatly utilize t rolmri ics of topic continuity
now each step of our sublanguage component will be described in detail
inevitably this evaluation is affected by the performance of the base system
we tried this becmlse sri actually provided us with NUM n best hypotheses
improvements will no doubt be possible through better adjustment of the parameter settings
used a silnple technique whit h is conunoil in information retrieval research
also we thank for useful discussions and suggestions prof grishman and slava katz
scores of the n best hypothes s gen rnted
note that none of their language models take long range dependencies into account
as we will try to put all this information together into one large graph we must first find what information the various temporary graphs have in common and then join them around this common knowledge
certainly if we euvisage applications trying to understand children s stories or help in child education a corpora of texts for children would be a good source of information to extend our lkb
knowledge structures called concel t lustering knowledge raphs cckgs are introduced along with a process for their construction from a machine readable dictionary
the join operation allows us to bring new conccpts into a graph by finding relations with ex null isting concepts as well as bringing new relations between existing concepts
often though a localist approaeh is adopted whereby the words are kept in alphabetical order with some representation of their definitions in the form of a template or feature structure
NUM that process would be done for the whole dictionary we would obtain an l l ii divided into multiple clusters of words each represented by a cck
this is the main reason for calling our graphs temporary as we assume a conceptual graph the ultimate goal of our translation process should contain a restricted set of well defined and nonambiguous semantic relations
each jckg will start as a conceptual graph representation of a trigger word and will expaud following a search algorit hm to incorporate related words and ibrm a c oncept cn s er
the ideas explored using this dictionary can be extended to other dictionaries as well but the task might becorne more complex as the defilfitions in adult s dictionaries are not as clear and usage oriented
city in sleek NUM will send them to you ill i riorio
lack of precision in the choice of vocabulary NUM NUM and NUM NUM
given that the tests used only NUM letters of each type one might question their representativity
after the sixth cycle the average quality scores showed thai the results wottld be sufficiently representative
the sentences or paragraphs thus produced are thcl clbre concatenations o1 predefined and illsertcd texts
no member of the jury knew which technique had been used for producing each of the letters
the first three criteria were considered as eliminatory and were marked NUM or i
that between the atmmmtic and semi automatic letters is considerable NUM NUM out of NUM
therefore similarity measures based on cooccurrences and similarity estimation based on shared contexts must not be used in place of each other
a and b in fig NUM threshold of NUM bring out a concrete and a more theoretical use of etude
however through cliques and edge labels the syclade structured and documented map of the words helps to capture the word meaning level
if it is not it is eliminated
table NUM identification results of transliterated personal names
however there are still several difficult problems
third the keyword may be omitted completely
that is it is a single character word
tlus problem is interesting and worthy of resolving
for exan ple hj company in fj three companies is not a keyword due to the critical parts of speech
when the criterion is loosed a little i.e. chinese personal nmnes and transliterated personal names are regarded as a category the performance s NUM NUM NUM NUM
and of course some conceptual categories such as color are outside the inventory entirely
however the present study so far can fault a number of the assumptions in this proposal as it stands
this work is based on a central feature of all known languages that they have two distinct systems of elements
the contents of this study the main goal of this study is to outline the fundamental conceptual structuring system of language
to illustrate one organizing principle of this sort pertains to cognitive topology
further even within acceptable categories such as that of number not all member notions can be specified
one system is the open class or lexical system comprised of elements that are great in number and readily augmented
the concepts expressed by closed class forms are critical to this study they constitute the core structuring system of language
this closed class form refers schematically to location at points of a volume of space that is defined by a curved plane
both of these sentences can refer to the same situation a set of houses located with respect to a valley
in our thim expet iment we use the full sensealign to align the testing data
because the number of character shape codes is small and they are defined by simple graphical features their recognition from images is inexpensive
fable NUM shows the processing thne for the u ansformarion of all images on a sparcstation NUM sun microsystems
in this paper we consider the number of connected components vertical location and deep concavity as graphical features to classify characters
in the next section we describe the definition of character shape codes and word shape tokens and their generation from document images
we show in this paper that our technique can categorize as accurately as the conventional ocr based approach while it can process much faster
some characters touched each other in lower quality images and were treated as a single character in the process of word shape token generation
similarly word shape tokens from all NUM NUM words with suffix ing mapped to only NUM words without the suffix NUM
when images were in higher quality n NUM NUM there was little correlation between the accuracy and the size
we show below that given the loosely structured task the probability of the observed distribution depicted in figure NUM is extremely low hence highly significant
today we are much better off
the second discourse feature of interest is that the usage of a wide range of lexicogrammatical devices seems to constrain or be constrained by this more abstract structure
NUM NUM NUM NUM every document detection component shall be able to serve as part of a document routing function that compares new documents from a specified source to potentially very large numbers of profiles from many users
NUM NUM NUM the architecture shall support the concept of using detection to filter documents as the input to the extraction process however the extraction process may operate independently from the detection process if required by the application
the architecture shall provide for the use of a common term expansion dictionary that can be used to look up equivalent terms variation terms synonym terms or abbreviation expansions to support various document processing tasks in different applications
while much of the scope of reference a tei is directed to embedding the associated tagging information during document creation in order to support publication needs it also provides supporting concepts that can be used to dynamically mark documents
the effect of this relation is that the reader recognizes the situation presented in the satellite as a cause of the volitional action presented in the nucleus
to answer the question empirically one could code a corpus for its intentional relations and attempt to identify linguistic cues that correlate with distinctions among the relations
by incorporating the nucleus satellite distinction into the definitions of rst informational relations these relations include an implicit analysis of intentional structure
while the core s position in the g s linguistic structure is most likely an unembedded utterance it is also possible that the core could be an embedded segment
the question of whether or not coreless segments actually occur however is best answered by corpus analysis rather than theorizing
the volitional result relation is nearly identical except that the cause of the action is the nucleus and the result is the satellite
the correspondence between g s dominance and rst nuclearity helps to clarify the relationship between ils and informational structure the structure determined by underlying semantic domain relations
recall that segment purposes like the utterance intentions discussed by grice have the property that they are intended to achieve their effect in part from being recognized
the issue of what structure is determined by semantic domain relations in the discourse and how this structure might be related to the intentional structure is discussed
we use the term intentional linguistic structure or ils as a theory neutral way of referring to the structure of a discourse determined by the speaker s intentions
one can see from these tables the important differences between the languages on which the experiments were performed
note also that the system is symmetric between the two forms of a natural language graphemic and phonemic
the algorithm as described previously has many disadvantages for a ptgc system from the implementation point of view
in all tables and charts some symbols have been used to designate the different parameters of the experiments
for this reason the natural logarithms base e NUM NUM were chosen instead of decimal logarithms
the multilingual aspects of the algorithm and experimental results for seven languages are also given in this section
the differences in performance between the languages and the types of domains and models used are discussed in the following section
in all known representation systems it is possible to define proper representations extracted from the usual representations and ambiguity free
but p qx t p qi t vi e NUM e
the importance crucial important not important negligible expresses the impact of solving the ambiguity in the context of the intended task
then come its obligatory labels scope then status importance and type in any order and its other labels
NUM these differences are frequently queryspecific not just domain specific which makes muc style extraction impractical NUM the role that a concept plays in a query can affect its usefullness in retrieval concepts found in focus appear to be radically more discriminating than those found in background roles
why does rst need two relations to capture this
the dominance of intentions directly determines embedding of segments
NUM NUM can intentional and informational structure differ in rst
this effect is an explicit claim about ils
final answers to these questions require further research
toward a synthesis of two accounts of discourse structure
further research is required to resolve this question
ils is something g s makes explicit claims about
the verb sense distinctions we make may sometimes be less detailed than those appearing in most dictionaries since as many researchers have noted dictionary sense distinctions are often overprecise and incorporate pragmatic and world knowledge that do not properly speaking inhere in the word itself
the basic approach taken during the development of the dictionary was to avoid a particular linguistic theory and to allow for adoptability to various applications
technique since NUM the mail department el lea redoule a l uropcan mail order colnpany has been using a semi automatic reply system referred to below as sa consisting of a nutnbel of predelined attd fill in the blank sentences or paragraphs which are identified by codes that the writers memorisc
NUM NUM NUM absence of repetition semi automatic system NUM NUM out of NUM atttomatic hybrid system NUM NUM out NUM NUM human written letters NUM NUM out of NUM differences ideal human letters vs automatic letters NUM NUM automatic letters vs sa letters NUM NUM ideal human letters vs sa letters NUM NUM
the following example shows the typical problem o1 ps ctition in the semi automatic letters
dear si i have received your lettel which i have n gd with great attention
l ant q kaid that NUM can not give you an exact delivery date and sincerely apologise for this dek y
however the differences between the hmnan written letters and those produced by the automatic hybrid system ue relatively slight
the overall system is composed of two inain modules thc i ccision module and the generation module
comme vous en avez dtd informdc lots de i enrcgistremcnt de votre commando ellcs n dtaient pas disponibles
dear mac m ant very sorry hat you have 1tot received lhe white sports shoes
this would appear to be mainly or reasons related to commercial communication rather than computational linguistics
the above approach con ectly determines die input sentence s valency structure which allows the maddne translation system to produce more accurate output
the workshop is intended for researchers in computational linguistics artificial intelligence psycholinguistics or other fields who have been working in lexical semantics and large scale lexical knowledge acquisition
such linguistic frameworks as lfg and hpsg have also used the concept albeit in a different sense and for a different purpose
whereas the former has been regarded as a topical issue for quite some time the latter is only now receiving its due attention
two main issues present themselves a treatment of lexical ambiguity and b lexical rules as a conceptual tool for controlled proliferation of entries
we are mainly interested in examining the following trade offs the coverage vs the depth of existing semantic lexicons vs the effort involved in building them
however the ability to ignore function words is of great benellt when working with speech recognition output in which such words are often mistaken
the analysis of spontaneous speech requires dealing with problenls such as speech disfluencies looser notions of grammaticality and the lack of clearly marked sentence boundaries
a newly developed end to end evaluation procedure allows us to assess the overall performance of the system using each of the translations methods separately or both combined
the evmuation of transcribed inl ut allows us to assess how well our translation modnles wouhl unction with perfect speech recognition
in this paper we described the design of the two translation modules used in the janus system outlined their strengths and weaknesses and described our etforts to combine the two approaches
file utterances are broken clown into sentences for evaluation in order to give more weight to longer utterances and so that utterances containing both in and out of domain sentences can be iudged
our current system is designed to translate spontaneous dialogues in the scheduling domain with english spanish and german as both source and target languages
elements words or tokens in a pattern may be specified as ol tional or repeating as in a kleene star mechanisln
to cope with high levels of ambiguity the parser includes a statis ical disambiguation module in which probabilities are attached directly to the actions in the lr parsing table
as already described both the glr parser and tile phoenix parser were specifically designed to handle tile problems associated with analyzing spontaneous speech llowever each of the ap
recognition rates had been improved there for read speech
our current work which led to intarc NUM NUM
inerementality is required for all modules
modules communicate explicitly with one another via messages sent over bidirectional channels
fhere is only one thread of control active at any time
only if semantic analysis fails it requests further edges from the unpacker
now ice is the architectural framework of the vm research prototype
e actual is the last vertex added to tpsto in an operation
e eat points to the left hand side of the grammar rule
these topic averages are then combined averaged across all topics in the appropriate set to create the non interpolated average precision for that set
the overview of the results discusses the effectiveness of the systems and analyzes some of the similarities and differences in the approaches that were taken
this is a valid sampling technique since all the systems used ranked retrieval methods with those documents most likely to be relevant returned first
sample trec NUM topic nura number NUM desc what are the prospects of the quebec separatists achieving independence from the rest of canada
this did not happen in earlier trecs because the problem seemed less important than for example discoveting automatic query expansion methods in trec NUM
work has continued at comell in improving their radical new matching algorithm and further information can be found in NUM
this trend continued in trec NUM with a major drop particularly for the routing task that reflects increased accuracy in rejecting nonrelevant documents
this is a remarkably high level of agreement in relevance assessment and probably is due to the general lack of ambiguity in the topics
the factors section is included to allow easier automatic query building by listing specffic items from the narrative that constrain the documents that are relevant
in reestimating the word n gram probabilities we introduce two modifications to the normal reestimation procedure
this is because a fairly large amount of segmented japanese corpus were available in our experiments
the overestimation of the vocabulary is substantially reduced
the left adjunction rules are triggered by states of the form a ec i j
they can be looked at as recognizing a subtree that is required to be substituted as opposed to a subtree that may be substituted
rules NUM and NUM encode the fact that one can skip over nodes labeled with c and foot nodes without having to match anything
every use of t in a complete derivation in g has to be associated with a substitution of some u e i for
in the case of a cfg this means that it must not be the case that x x for any non terminal x
therefore the parser can be changed so that a completion rule is triggered at most once for any possible first chart state and k
to start with the use of auxiliary trees in an ltig can allow it to be exponentially smaller than the equivalent gnf
for example everywhere in the procedure the word right can be replaced by left and vice versa
the ability to resolve co references provides a sound basis for all forms of link analysis NUM
part of speech of the nominal attachment site
briefly this procedure works ms follows
is the word in all upper case
the results are shown in figure NUM
core extraction is thus used as a step towards abstracting ztway fl om actual words in the direction of a more sema ntica llygrounded
both inherent semanti l rol erdes of words as embodied by taxonomical relationships and word listri i utiona l
put another way these two models assume more than we actually know about the expert s decision making process
to address the general problem we apply the method of lagrange multipliers from the theory of constrained optimization
we call the model described by equations NUM and NUM the basic translation model
but now we face a question left open in section NUM what does uniform mean
the first task is to determine a set of statistics that captures the behavior of a random process
predictions of the noun de noun interchange model on phrases selected from a corpus unseen during the training process
we observed that this model was a member of an exponential family with one adjustable parameter for each constraint
NUM the translation result has a different character string than the proofread translation result with unregistered words
in this paper we describe the process of english japanese translation as one possible application of this method
in this paper we proposed a new method of machine translation using inductive learning with genetic algorithms
the system substitutes the words in the word translation rules for the variables in the sentence translation rules
the use of capabilities which are operating system or environment dependent must be clearly identified and modularized so as to isolate them from transportable components and modules
for lower quality images n NUM the former was significantly lower than the latter
for an unbiased comparative experiments between the two approaches we chose rela null tively specific topics
it should also maintain the dictionary by collecting new words in the target domain
in figure NUM the string eniac is successfully tokenized as an unknown word
we decompose it into the product of word length probability and word spelling probability
since we did not use any tokenizers numerals tend to be divided arbitrarily
in the second sentence of figure NUM the system extracted NUM
to count the expected word frequencies we used the top NUM word segmentation hypotheses
here unknown words are those that are not registered in the system dictionary
the discourse processor also updates a calendar which keeps track of what the speakers haw said about their schedules
one strategy that we have investigated is to use the l hoeifix module as a back up to the NUM lt lcb module
the parse result of glr is translated whenever it is judged by the parse quality heuristic to be good
our experience has indicated that a large proportion of bad translations arise from the translation of small parsable fragments within out of domain phrases
this representation is usually less detailed than tile corresponding glr ilff representation and thus often resuits in a somewhat less accurate translation
the interlingua meaning representation of an input utterance is derived directly from the NUM arse tree constructed by the parse r
janus is a multi lingual speech to speech translation system designed to facilitate communication between two parties engaged in a spontaneous conversation in a limited domain
the relevant concepts however are extracted and strung together they provide a general meaning representation of what the speaker actually said
when the input is only slightly ungrammatical and contains relatively minor distluencies ilr produces precise and detailed ih s that result in high quality translations
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
this paper discusses a case study that examined how lexical semantic techniques could be used to build scoring systems based on small data sets
based on the above mentioned limitations of lcss the use of such representation for scoring systems does not seem compatible with our response classification problem
we have not yet tested our method with other conditions
since there are at most n states this stage of the algorithm is o nfk2
as we saw above the unaugmented ostia algorithm often outputs long clumps of segments when seeing a single input phone
the alignment information previously calculated between input and output strings is used again in determining which arcs have the same behavior
a when a computerized call is made to a former prisoner s home phone that person answers by plugging in the device
the most salient entity the center of attention at a particular utterance is called the backward looking center cb
but a new application of the technology is about to be tried out in massachusetts to ease crowded jail conditions
a the summary above just shows the relevant portions of the original text in the original order selected for the summary
a the content of the summary is selected by picking the two segments with the most fre null quent cb the inmate s prisoner
another heuristic used is to select restatements among the propositions for the summary since restatement is a good indicator of important information
for example the second sentence below can be left out of the summary because it is an elaboration on the same topic
first those segments that are about the most frequent centers of attention are selected and then these segments are pruned by recognizing non critical elaborations among the propositions
good authors often restate the thesis often at the beginning and at the end of the text to ensure that the point of the text gets across
as the reviewer also points out this is a problem that is shared by e.g. probabilistic context free parsers which tend to pick trees with fewer nodes
finally the statistical method fails to correctly group hanzi in cases where the individual hanzi comprising the name are listed in the dictionary as being relatively high frequency single hanzi words
thus in a two hanzi word like zhongl guo2 middle country china there are two syllables and at the same time two morphemes
however there will remain a large number of words that are not readily adduced to any productive pattern and that would simply have to be added to the dictionary
making the reasonable assumption that similar information is relevant for solving these problems in chinese it follows that a prerequisite for intonation boundary assignment and prominence assignment is word segmentation
lexical knowledge based approaches that include statistical information generally presume that one starts with all possible segmentations of a sentence and picks the best segmentation from the set of possible segmentations using a probabilistic or cost based scoring mechanism
according to equation NUM at low temperatures it is increasingly difficult for a new structure of lesser strength to win in competition against existing structures of greater strength
the effect of equation NUM is similar to equation NUM to maximize differences in strength values at low temperatures and to minimize differences at high temperatures
figure NUM for simplicity we have omitted relations between characters in figure NUM it is observed that the system has not yet constructed the agent and theme relations
it can also be observed that overall there is a gradual shift in the types of operations executed from being charactercentered initially to word centered and then to chunk centered
descriptions of chunk objects may also include these two descriptions except that here these two descriptions are derived from the category and the sense of the word that is the governor
the building of linguistic structures e.g. word and chunk objects descriptions of objects relations between objects is carried out by a large number of agents known as codelets
in section NUM we introduce the problem of ambiguous chinese word boundary perception and follow in section NUM with a summary of the current practices in chinese word identification
the direction of a part of link for instance the link from the character node to the word node is interpreted as the character is part of the word
both alternations xudshdng hu6 student live 2a and NUM xu sh nghu6 learn life are acceptable
an example for each category is shown in NUM to NUM NUM throughout this paper we follow the guidelines on chinese word segmentation adopted in china
unlike english japanese can not rely upon orthographic clues like capitalization to identify proper nouns
in addition to absolute dates such as identified as well
time expressions were also straightforward in their manner of representation
while there are clearly many places in which our current system requires further work it does set a new standard for spoken dialogue systems
we have attempted to build a fully functional system in the simplest domain possible and focused on the problems that most significantly degraded overall performance
since these experiments were performed we have enhanced the channel model by relaxing the constraint that replacement errors be aligned on a word by word basis
robustness is achieved by a combination of statistical error post correction syntactically and semantically driven robust parsing and extensive use of the dialogue context
while not present in the output the presence of unaccounted words will lower the parser s confidence score that it assigns to the interpretation
if fragmentary information is supplied the problem solver attempts to incorporate the fragment into what it knows about the current state of the plan
seven different subjects had a task where the goals were not met and each of the five tasks was left unaccomplished at least once
some of these systems support system initiated questions to elicit missing information in the template but that is the extent of the mixed initiative interaction
it is then instantiated by domain specific reasoning algorithms to perform the actual searches constraint checking and intention recognition for a specific application
in addition there is no other engine at detroit so this is not plausible as a focus shift to a different engine
provide adequate translation for these collocations
tile ba sic tool used in categorical data analysis is the contingency table sometimes called the crossclassified table of counts
first features that could be used to guess the pus of a word were determined by examining the training portion of a text corpus
NUM the architecture shall support the use of dictionaries in machine readable form e.g.
let pi be a sentence set comprising the ith japanese sentence and its possible english correspondences as depicted in figure NUM
note here that pis overlap each other and w NUM may be double counted in the contingency matrix
they found character or word length based approaches were not appropriate due to the structural difference of the two languages
however none of them have their own entry in the bilingual dictionary which would strongly obstruct the dictionary method
although these word correspondences are very effective for sentence alignment task they are unsatisfactory when regarded as a bilingual dictionary
by repeating this anchor setting process with threshold reduction sentence correspondences are gradually determined from confident pairs to nonconfident pairs
in this phase the most important point is that each set of possible sentence correspondences should include the correct correspondence
section NUM reports experimental results for various kinds of japanese english texts including newspaper editorials scientific papers and critiques on economics
null the system performs well and is robust for various lengths especially short and various genres of texts
for example english spanish english french english german english japanese japanese french japanese chinese and other dictionaries are now commercially available
although none of the other features are derived using classification knowledge of any other potential boundary sites note that global pro
this differs from a significance test of the null hypothesis e.g. our use of cochran s q where observed data is compared to random distribution
in principle a is computed from the same type of matrix shown in table NUM and can be applied to multivalued variables that are quantitative or qualitative
our method aims at abstracting away from the absolute differences across multiple subjects per narrative n NUM to derive a statistically significant set of segment boundaries
step 2a consider the ak rooted initial trees that fail to satisfy ft
so NUM rather than NUM because there are two cells in the contingency table for each degree of freedom
this includes NUM sentence boundaries if two separate sen null tences are to be produced the spl is split into one per sentence and built up sequentially
in our example the condition marker in NUM NUM is lexicalized as if the disjunction marker as or and the conjunction marker as and
one of the choices the exophoric lexical choice module makes while generating NUM is the replacement of the fragment d1 in the condition part by if replacement is needed
to see the difference between the tsl and spl consider the pl expression for the first sentence in NUM NUM overall planning process
NUM explicit implicit condition can be com null municated explicitly by discourse means and or lexical means such as the verb necessitate or implicitly obtainable via inference
in best first probabilistic chart parsing a probabilistic measure is used
more accurate probability estimates should be attainable using lexical information
figure NUM nonzero length edges for NUM of probability mass
we will refer to this figure of merit as straight ft
they do not actually incorporate this figure into a best first parser
we gathered statistics for each sentence length from NUM to NUM
figure NUM average cpu time for NUM of probability mass
we propose and evaluate several figures of merit for best first parsing
in our experiments we use only tag sequences for parsing
in this paper i will be exploring techniques for automatically summarising texts concentrating on selecting the content of the summary from a parsed semantic representation of the original text
a the wristband can be removed only by breaking its clasp and if that s done the inmate immediately is returned to jail
null thus i propose the following heuristic for pruning the segments in the summary the same topic as defined above in the summary
rosenblatt expects electronic surveillance in parole situations to become more wide spread and he thinks eventually people will get used to the idea
the heuristics for content selection actually operate on lfs the selected lfs will then be sent to a generator which can plan a more coherent summary than what is produced above
NUM a whenever a computer randomly calls them from jail the former prisoner plugs in to let corrections officials know they re in the right place at the right time
concept grammar rules are built by mapping concepts from the lexicon onto the concept structure patterns present in a set of training responses
there is a movement in testing to augment the conventional multiple choice items i.e. test questions with short answer free response items
our current prototype was designed to classify responses according to a set of training responses which had been hand scored by test developers in a multiple category rubric they had developed
as previously mentioned there was insufficient lexico syntactic patterning to use a contextual word use method and domain specific word use could not be derived from real world knowledge sources
for this item type examinees are reliant on real world knowledge with regard to item topic and responses are based on an examinees own ability to draw inferences
our results are encouraging and support the hypothesis that a lexical semantic approach can be usefully integrated into a system for scoring the free response item described in this paper
in this study the manual creation of the lexicon and the concept grammar rules for this data set took two people approximately one week or NUM hours
given our small data set our assumption was that a lexical semantic approach which employed domain specific language and concept grammars with concept structure patterns would facilitate reliable scoring
for example an item referred to as the police item describes a situation in which the number of police being killed has reduced over a NUM year period
for this reason we can not predict whether the client will accommodate to the interpreter or vice versa
encouraging those natural inclinations in real human machine systems will have a better chance of success than imposing artificial restrictions
however again the greater difficulties in communication in the machine interpreted setting might make up for a lack of concern with social standing resulting in a rate of accommodation comparable to that in the human interpreted setting
included in this requirement is the specification of application program interfaces api
we expect that clients will accommodate to the machine to some extent that clients word choice will be affected by their perception of what works or what the machine knows
the architecture shall accept as input template schema with empty slot relationships and treated as formatted information
NUM NUM NUM NUM NUM NUM verification method demonstration
the architecture shall make filled templates available to the user interface for viewing editing and disposition
in such cases labels shall not be separable from the process or data item
NUM NUM NUM NUM verification method inspection and demonstration
or about NUM of the interhuman agreement
nonstochastic lexical knowledge based approaches have been much more numerous
the present proposal falls into the last group
this is not ideal for some applications however
we have not to date explored these various options
particular instances of relations are associated with goodness scores
here i is the positional index of the text string but the position is specified in terms of bytes
such cases occur where a proper prefix of the pattern string has high occurrence frequency in the text string e.g.
after searching it is necessary to clear
another approach uses the different characters in p as the reduced alphabet er which is much smaller than NUM
the basic idea is to map the NUM byte characters to single byte characters and then use existing algorithms
when the pattern string is found in t the position of the first matched character in t is returned
for example the bm algorithm moves to position i NUM lipid for matching in table NUM
state transition ruble NUM is used for updating the values of the current row in NUM
in chinese string searching this will happen for technical terms that have a high frequency prefix constituent
the table contains actual entries for the french source word premier from 7best lexicons that were induced from NUM pairs of training sentences using different filter cascades
the filter based framework together with the fully automatic evaluation method allows easy investigation o the relative efficacy of cascades of each of the subsets of these four filters
bible is a family of algorithms based on the observation that translation pairs NUM tend to appear in corresponding sentences in an aligned bilingual text corpus a bitext
the kth cumulative hit rate for a source word s is the fraction of test sentences containing s whose translations contain one of the k best translations of s in the lexicon
given a test set of aligned sentences a better translation lexicon will contain a higher fraction of the source word target word pairs in those sentences
the greater the overlap between the vocabulary of the test bitext and the vocabulary of the lexicon being evaluated the more confidence can be placed in the bible score
besides comparing different lexicons on different scales bible can be used to compare different parts of one lexicon that has been partitione d using some characteristic of its entries
merging an mrbd with an n best translation lexicon induced using the mrbd filter will result in an mrbd with more entries that are relevant to the sublanguage of the training bitext
the knowledge sources investigated here are part of speech information machine readable bilingual dictionaries mrbds cognate heuristics and word alignment heuristics
comparing the above structures with the complete analysis of the sentence in they were not identified because the system has come to a stop at cycle NUM after an instance of answer codelet was executed
we believe it is easier to correct a poor plan than having to keep trying to explain a perfect one particularly in the face of recognition problems
pictures are a medium for language and allow a child to enter a world by playing
the aim of this activity is the verbalisation of the action
produce text related to the scenario or game components
all this work is carried through a common language dialogue
so the user does n t become discouraged by fruitless attemps
a grammar has been developed which describes the highest level of difficulty
their underlying meaning using a simple and logical graphic formalism
the distinguished index of the noun phrase call it p is identified with the variable y in the rule but this variable is not associated with the syntactic category s on the left hand side of the rule
we conclude that as a matter of principle no edge should be constructed if the result of doing so would be to make internal an index occurring in part of the input semantics that the new phrase does not subsume
we know that it will not generally be possible to reduce logical expressions to a canonical form but this does not mean that we should expect our generator to be compromised or even greatly delayed by trivial distinctions
this view has the apparent disadvantage of putting insignificant differences in the syntax of a logical forms such as the relative order of the arguments to symmetric operators on the same footing as more significant facts about them
NUM a b b c c this represents a phrase of category a that requires a phrase of category c on the right for its completion
the key property of this scheme is that active and inactive edges interact by virtue of indices that they share and by letting vertices correspond to indices we collect together sets of edges that could interact
we illustrate the modified procedure with the sentence NUM whose semantics we will take to be NUM the grammar rules NUM NUM and the lexical entries in NUM
a prima facie argument for the utility of these particular words for expressing i can be made simply by noting that modulo appropriate instantiation of the variables the semantics of each of these words subsumes NUM
the entries in NUM with their variables suitably instantiated become the initial entries of an agenda and we begin to move them to the chart in accordance with the algorithm schema say in the order given
neither the chart nor any other special data structure is required to capture the fact that a new phrase may be constructible out of any given pair and in either order if they meet certain syntactic and semantic criteria
the second pass takes the output of the first pass and uses chunk to determine the flat phrase chunks of the sentence where a phrase is flat if and only if it is a constituent whose children consist solely of pos tags
nip components are integrated in the architecture
translations are produced using a gbmt engine
the temple project has developed an open multi lingual
since in practice it only does a constant amount of work to advance each step in a derivation and since derivation lengths are roughly proportional to the advance d x v insert d x h extract h d completed d
suthers set forth a set of views which can be used to select coherent subsets of domain knowledge structural functional causal constraint and process
although neither of these studies employed human judges to critique text quality the rigor with which they were conducted has significantly raised the standards for evaluating generation systems
as computational linguists have known for many years formally characterizing texts is a very difficult time consuming and error prone process
we have designed an architecture for explanation generation and implemented a full scale explanation generator knight NUM based upon this architecture
in our work we focused on two types of texts that occur in many domains process descriptions and object descriptions
NUM although we will not discuss the details of the edps here it is instructive to examine their structure and function
edge has facilities for managing conversations so users may interrupt the system to ask questions and edge can either answer the question immediately or postpone its response
it is our hypothesis that because edps are designed for ease of creation and modification the second and third steps will be considerably more difficult than the first
in an ablation study different aspects of the system are ablated removed or degraded and the effects of the ablation on the explanations are noted
candidates surrounding the caesura mark a chinese specific punctuation mark are treated in the similar way
c cic2c3 and c1c2 are in the cache and c NUM c2c3 is correct
there are NUM NUM transliterated personal names including NUM NUM male s names and NUM NUM female s names
such a syllabic order is rare in chinese but is not special for a transliterated string
because of the unrestrictive length of transliterated names how to identify their boundary is a problem
in other words the syllabic orders of transliterated strings and general chinese strings are not similar
in general the maximum entropy framework puts no limitations on the kinds of features in the model no special estimation technique is required to combine features that encode different kinds of contextual predicates like punctuation and cons NUM NUM NUM
after checking the result we find that some unreasonable word association comes from the training corpus
the name part of an organization may be forlned by single characters or words
an average of NUM NUM correct groups have been found in the testset again demonstrating a great level of ambiguity in the source data
conclusions as a conclusion we may claim that corpus driven lexical learning should result from the interaction of cooperating inductive processes triggered by several knowledge sources
the improvement is quantified by means of the number of saved bits necessary to describe the correct decisions when moving from prior to posterior probability
the predicates for tag chunk build and check are used to scan the derivations of the trees in the corpus to form the training samples c tcmusk u ld and tcmeck respectively
identifying the coding system and language of on line documents on the internet
the following two subsections describe the above two steps in detail
the final result is easily obtained by combining results of the asian and the european parts
in a communications network characters are represented as numbers or a sequence of numbers
kanagawa NUM NUM japan kikui nttnly i sl
each character in a character set has a unique identification number
a fundamental solution is to develop international standards for an internationalized coding system and language representation
most of these processes assume that documents are correctly decoded and the language is known
the spatter decision trees use predicates on word classes created with a statistical clustering technique whereas the maximum entropy parser uses predicates that contain merely the words themselves and thus lacks the need for a typically expensive word clustering procedure
furthermore the search heuristic returns several scored parses for a sentence and this paper shows that a scheme to pick the best parse from the NUM highest scoring parses could yield a dramatically higher accuracy of NUM precision and recall
the first sentence is across a turn by speaker a namely we did get the wedge cut out by building some kind of a cradle for it
i mean sorry excuse me as shown in example NUM through it is possible that editing terms occur outside the rr
within the verb group weak and more common verbs will appear in the given portion whereas strong verbs that carry content will appear in the new portion
we tried to alleviate this problem by trying to provide the context for the current lattice by selecting the most likely pair of words from the previous lattice using pair occurrence frequency
one problem with this approch is that since the standard switchboard wer is about NUM about NUM of the time we were providing incorrect context using these lattices
partial words are also not marked specially though in the transcripts they end in a they appear directly to the left of the interruption point
initial experiments shows no overall improvement in word error rate however the model was able to identify both previously identified and new backchannel utterances in the test data
a more complex algorithm that finds the main verb group and uses the last verb in the verb group rather than the last verb in the sentence would remedy this
the automatic tagger inserts only the syntactic part of the tag
the job of the treebattkers is l o local
the second stage is the insertion of sgml like mark up
i ase l oil a ni sl
beyond skeleton parsing producing a comprehensive large scale general english treebank with full grammatical analysis
range of dialects and regional wtrieties of cur rent american english
for example there are subtle differences in some steps of quantifier storage according to the formalism being used similarly differences even in lambda reduction for intensional logic it is natural to interleave the step of operator caneellation between reductions
if the correct sentence structure is acquired for each input sentence prosodic information can be accurately calculated
all the pp cases from the brown curl us and NUM NUM of the wsj cases were reserved ms training data
rules NUM and NUM merely move from state to state without changing the span i j
this is accomplished by associating with each feature a weight that indicates the importance of the feature in determining case similarity and by using a weighted nearest neighbor case retrieval algorithm NUM set the weight wl associated with each feature f in the normalized feature set3
it may be that this built in encoding of the bias is adequate or that like the restricted memory bias additional modifications to the baseline representation are required before the subject accessibility bias can have a positive effect on the learning algorithm s ability to find relative pronoun antecedents
finally we argue that the linguistic bias approach to feature set selection offers new possibilities for case based learning of natural language it simplifies the process of instance representation design and in theory obviates the need for separate instance representations for each linguistic knowledge acquisition task
our current approach assumes that the expert knowledge of computational linguists is easier to apply at the level of linguistic bias selection than at the feature set selection level so at the very least this expert knowledge can be used to seed the bias selection algorithm
samples of text that contained different proportions of unknown words were tagged using the three different methods for handling unknown words described above
we expected the merged representation to perform rather well because the combined recency bias representation worked well on its own and because the restricted memory rm bias essentially discards features that are distant from the relative pronoun and rarely included in the antecedent
figure NUM shows the performance of the two types of nmdels with fenture sets that ranged from a single feature to nine features
to ensure that the case retrieval algorithm focuses on features that are present rather than missing from the problem case we also modify the original case retrieval algorithm to award full credit for matches on features present in the problem case and to allow partial credit for matches on missing features
previous restricted memory studies e.g. short term memory studies do not state explicitly what the memory limit should be it varies from five to nine depending on the cognitive task and depending on the size and type of the chunks that have to be remembered
an example surge input with the corresponding sentence is given in fig NUM
it has also been used for teaching natural language generation at several academic institutions
the goal of a re usable realization component is to encapsulate the domainindependent part of this third task
null we first define the task of a stand alone syntactic realization component within the overall generation process
the verb group grammar decompo es in three major systems tense polarity and modality
the task of the realization component is to map such skeletal tree onto a natural language sentence
we employ yj rather than the more intuitive rcb to avoid confusion with the feature function notation
another model obeying this constraint predicts pendant with a probability of NUM NUM and with a probability of NUM NUM
berger della pietra and della pietra a maximum entropy approach figure NUM four different scenarios in constrained optimization
null the past few decades have witnessed significant progress toward increasing the predictive capacity of statistical models of natural language
we consider a random process that produces an output value y a member of a finite set NUM
intuitively the principle is simple model all that is known and assume nothing about that which is unknown
obviously we would like a condition which applies exactly when all the useful features have been selected
thus a model p y x is by definition just an element of v
in other words the architecture specifies an internal structure to which documents being input into tipster modules should conform in order to ensure optimal processing
analytical tools such as link analysis tools timelines or other displays showing document clustering are considered part of the user interface or the application
however if it does provide any of these capabilities in order to be compliant it must follow the tipster standards for user application interfaces
even if no modules have yet been built which serve his purposes the developer may be able to find help in the tipster interface control document
a vendor s product may be determined to be tipster compliant with the use of a tacad independent of actually being part of a tipster application
this document is a compilation of all the text processing needs which are envisioned to be required by the u s government during the life of the tipster project
the architecture will provide a vehicle for delivering tipster text document detection and data extraction methods to today s analysts in nsa cia and dia
for tipster purposes it is useful to distinguish between different forms of a document on the basis of the types of processing performed on it
NUM NUM NUM NUM markup of documents a document markup will be defined as information which has been added to a document before it becomes a form NUM document
tray at the university of colorado boulder who prepared the text on the basis of the hendricks house edition
figure NUM illustrates how a ro of entries in NUM l
for example nj which means conference can be regarded as a word but in the phrase NUM quot j lp pi s quot deg abe
sometimes patterns which are words can match with text where the matched string of the text is not functioning as a word
the last state is distinguished because it has no success transitions where as the other has one or each state
the program in figure NUM determines in line NUM whether the current input character is a single or two byte character
if the converted value is negative then NUM is the first byte of a NUM byte character
a perfect hash function section NUM ho converts the NUM byte characters into an index off
thus because subsequential transducers are an inefficient model of these sorts of rules representing them leads to an explosion in the number of states of the machine and an inability to represent certain generalizations
so our results suggest that assuming in the system some very general and fundamental properties of phonological knowledge whether innate or previously learned and learning others empirically may provide a basis for future learning models
nativist models suggest that learning in a complex domain like natural language requires that the learning mechanism either have some previous knowledge about language or some learning bias that helps direct the formation of correct generalizations
many models for example assume that much of the prior knowledge that children bring to bear in learning language is not linguistic at all but derives from constraints imposed by our general cognitive architecture
our intuition was that these more general transducers would correctly classify stressed vowels together as environments for flapping and similarly solve other problems caused by gaps in training data
a decision about an object is reached by descending the tree at each node taking the branch indicated by the object s value for the property at that node
after calculating alignment information for each input output pair all output symbols determined to have arisen from substitutions that is all output segments other than those arising from insertions are rewritten in variable notation
just as the generalized arcs can now specify one of their output symbols as being the current input symbol with certain phonological features changed they are now able to reference previous word final stop devoicing with variables
the basic intuition of two level phonology is that a rule that rewrites an underlying string as a surface string can be implemented as a transducer that reads from an underlying tape and writes to a surface tape
in the paper we will propose a method for identifying summary extracts in a way that allows objective justification
it has been left unclear just how high level of agreement among subjects needs to be achieved before reliably using data
precision is the ratio of cases assigned correctly to the yes category to the total cases assigned to the yes category
a single test material consists of three extraction problems each with a text from a different category
if one changes an arbitrary node label in a dag admitted by g one does not necessarily obtain a new dag admitted by g hence gibbs sampling is not applicable
then the probability that any two raters agree on the jth category by chance would be p
to obtain an initial distribution we associate a weight with each rule the weights for rules with a common left hand side summing to one
as a result the best weight we can choose for b is NUM which is equivalent to not having the feature b at all
we might consider approximating qnew fi by ignoring the normalization factor and assuming that all features have the same weight as feature i
the acceptance decision is made as follows if p y q y then y is overrepresented among the proposals
in the resulting structures the probability of choosing a particular word is constrained simultaneously by the syntactic tree in which it appears and the choices of words at the n preceding positions
then the sum over l g that is implicit in qfl f can be expanded out and solving for fl is simply a matter of arithmetic
since ij NUM x NUM x we arrive at the expression on the left hand side of equation NUM
we can convert the nondeterministic derivations discussed at the beginning of section NUM into stochastic derivations by choosing rule x i with probability i when expanding a node labeled x
it is here possible to spot the source and the target of the metaphor using the syntactic properties of the comparison
in its current version stk allows us to tokenize tag and search for lexical markers on large corpora
a prototype stk partially implementing the model is currently under development within an incremental approach
the analysis we made showed the existence of textual clues in relation with metaphoric contexts and analogies e.g.
a prototype implementing this model is currently under development within an incremental approach allowing step by step evaluations NUM
thereby detecting a metaphor meant detecting an anomaly in the meaning representation issued from such a classical analysis
NUM all content words of two or more occurrences are extracted
tables NUM and NUM list samples of translation pairs extracted from the experiments
the subsequent steps handle only those extracted word sequences
table NUM numbers of words identified
the similarity measure is an extension of dice coefficient
table NUM comparison of mutual information and dice coefficient
all the japanese and english sentences are aligned and morphologically analyzed NUM
though they assume unaligned japanese english parallel corpora alignment is performed beforehand
NUM NUM NUM NUM NUM NUM NUM NUM
in crossover two translation examples which have common parts are selected
experiments were carried out with and without genetic algorithms
one point crossovers are performed producing two translation examples
many studies have been carried out on machine translation
this process also uses genetic algorithm
figure NUM shows examples of crossover
these thresholds were determined by preliminary experiment
the onjunct mean of b is very high it is not clear why
the cross linguistic category mean for a b is significantly lower than that of a c and a i
evaluator independence given a text in one language different evaluators should produce the same connectivity profile
our first experiments japanese only concerned the conjunct determining dialogs
1t is toubtflfl however that the approach will give a useful indication of translation quality
distribution means were computed both for categories and for conjuncts
with these in place several sets of experiments were performed
the sizes of the subject groups are given in table NUM between parentheses
a prototype was implemented on a macintosh computer using hypercard
we try finding words in the dictionary containing many concepts identical to the ones already present in the cckg but perhaps interacting through different relations allowing us to create additional links within the set of concepts present in the cckg
NUM nucleus satellite salience in the case of condition salience can be shifted by change of the order of the condition and effect arguments
by keeping the preposition itself within the temporary graph we delay the ambiguity resolution process until we have gathered more information and we even hopefully avoid the decision process as the ambiguity might later be resolved by the integration process itself
as a result translation of collocations can not be done on a word by word basis and some representation of collocations in both languages is needed if the system is to translate fluently
finally a quick look at a bilingual dictionary even for two widely studied languages such as english and french shows that correspondences between collocations in two languages are largely unexplored
second it speeds up the execution of the algorithm as all fractions ri s decrease and the overall number of candidate translations is reduced
with our current constant dice threshold td NUM NUM we may miss a valid translation as long as the corpus contains at least NUM sentences
this measure is very sensitive to the marginal probabilities relative frequencies of the l s in the two variables tending to give higher values as these probabilities decrease
although the two measures are similar in that they compare the joint probability p x NUM y NUM with the marginal probabilities they have different asymptotic behaviors
finally the sixth and seventh columns give the similarity scores for today and each french word pair computed according to the dice measure or specific mutual information in bits respectively
NUM since our intent in this paper is to evaluate champollion we tried not to introduce errors into the training data for this purpose we kept only the NUM NUM alignments
hui can still appear on its own but aujourd is not a french word so champollion s french tokenizer erroneously considered the apostrophe character as a word separator in this case
thus if a grammar is to be strongly lexicalized it must be only finitely ambiguous
in the following we give a constructive proof of the fact that tig strongly lexicalizes cfg
positions are represented by placing a dot in the production for the corresponding NUM NUM NUM layer
for example the fourth position reached in figure NUM is represented as s ae b
figure NUM illustrates the simultaneous adjunction of one left and one right auxiliary tree on a node
a prediction should be made if any of these anchors is the next element of the input
both of the changes above depend critically on the fact that there are no left auxiliary trees
these rules reflect facts about the grammar and the traversal that do not depend on the input
in this case nominator consults a small authority file which contains information on about NUM special name words and their relevant lexical features
since it is usually the case that a right hand operator has stronger scope over a left hand one we evaluate strings containing operators from right to left
we then estimate the most likely part of speech category and label this cluster accordingly
this simultaneously fails to differentiate some linguistically important contexts and unnecessarily fractures others
the next section contains a presentation of a top down automatic word classification algorithm
and NUM b the cheese in the sandwiches quickly
we constructed a frequency dependent interpolated unigram and bigram model as a baseline
the perplexity for this system was NUM a NUM improvement
some n grams occur very frequently so word based probability estimates can be used
ms t deg maxms t NUM t
we then replaced the maximum likelihood bigram component with the smoothed bigram estimate
one easily calculated feature is the frequency of the previously processed word
days of the week male personal name body part noun and auxiliary
for example the transducer of figure NUM will fail completely upon seeing any symbol other than er or end of string after a t
of course this transducer is only trained on three samples but the same problem occurs with transducers trained on large corpora
this new distribution of output symbols along the arcs of the initial tree transducer no longer guarantees the onwardness of the transducer
due to the shallow nature of the rules in question the exact parameters used to calculate alignment are not very significant
the improved algorithm induced a flapping transducer with the minimum number of states NUM with as few as NUM NUM samples
as is seen in table NUM our algorithm successfully induces a transducer of minimum size given NUM NUM or more sample transductions
for clarity of explication set subtraction notation is used to show which vowels do not cause transitions between states NUM and NUM
designing a self constructive code for a highly flexional language like polish would have been a complex task
reaching a terminal state of the automaton is equivalent to finding a polish inflected form in the dictionary
as an example we show the creation of the spl expressions for the sentences NUM to NUM
the temple architecture is capable of handling a large number of character codesets through the use of the multilingual text library developed at crl which includes a multilingual string library a multilingual widget library use fig example to develop the mulfilingual lexical editor and the multilingual tipster editor for documents
one important outcome of the temple project is the development of an architecture to support the reuse of nlp tools and resources tools that are acquired from an external source such as morphological analyzers generators or taggers can be integrated in the system with a minimum of programming effort
the project has provided valuable results and insights for the development of a flexible multuingual platform for natural language processing
japanese and russian and an english morphological generator penman NUM
analysts and translators can edit the raw translation using a multilingual editor figure2
architecture the temple prototype includes a gbmt engine that provides an automatic translation for each language pair
the goal is to support rapid development of machine translation functionalities in a very short time with limited resources
the targeted languages are those for which natural language processing and human resources are scarce or difficult to obtain
the translator can then use the glossary editor figure NUM to edit any entry flagged as incomplete
certain applications require that the output of an information extraction system be probabilistic so that a downstream system can reliably use the output with possibly contradictory information from other sources
a positive value was learned for the case in which template t was created from a definite expression and s was perhaps transitively the preferred referent according to the coreference module
for training the maximum entropy model only the sets of characteristics of context for pairwise coreference are relevant the number of such sets differed between the two approaches as discussed below
the problem solver handles all speech acts that appear to be requests to constrain extend or change the current plan
upon making this feature active the ai s for all active features are re trained so that the constraints are all met simultaneously
the first component consists of a series of phases that recognize domain relevant patterns in the text and create templates representing event and entity descriptions from them
the list of coreference configurations for our example passage are repeated below we will refer to these configura null tions by their corresponding numbers
in practice coreference sets that are significantly larger than the one we have considered here can lead to an explosive number of possible coreference configurations
in the first split the training set contained NUM messages giving rise to NUM coreference sets and the test set contained NUM messages giving rise to NUM coreference sets
even if an utterance was completely uninterpretable the parser would still produce output a tell act with no content
of the NUM subjects NUM selected speech as the input medium for the final task and NUM selected keyboard input
this was done to avoid confounding effects
the generality of the approach is evident fi om the thct that the coverage and precision for the ovtside test are comparable with those of the inside test
classical approaches to the metaphor in nlu revealed multiple underlying processes
we thus propose an object oriented model for representing these clues
it is evaluated under grace NUM protocol for corpus oriented tools assigning grammatical categories
we proposed an object oriented model to represent these clues and their multiple properties
metaphor is a frequently used figure of speech reflecting common cognitive processes
the latter can be easily retrieved using stk avoiding lexical ambiguities
indeed they can be found using the results of syntactic parsing
in our point of view all the previous approaches were founded
a corpus based analysis shows the existence of surface regularities related to metaphors
excessive repetition a diflcrence of NUM NUM out of NUM ill relalion io human writing and of NUM NUM ira rehrtion to tire automatic system
w do our ulmost to satisjs our customer v but are dependent ott the delivery times imposed on us by certain suppliers
you shouhl ahvcmy have received this parcel ther n e would you please send me a cheque ih paymeht of the merchandise that we have vellt lo yell
the human letters are obviously the best but the dil rence between the automatic and semi automatic letters is very great NUM NUM out of NUM
it can be seen that the quality of the letters generated by the pilot systeln using alethgen was lar superior to that of the senti automatic system using predetinexl paragraphs
human writing the best characteristics of the human letters were absence of repetition and proximity personalisation which were both given scores of NUM NUM out of NUM
je suis ddsolde de no pouvoir vous donner une date prdcise de livraison croyez bien clue je regrette vivcment ce retard
below are several examples o1 letters produced using the semi automatic ill in4hc blmlks system and the automatic linguistic and template hybrid system
the third technique used was human writing in ideal conditions one of la redoute s best writers wrote the letters with no time constraints
rcstant a w lre enli sre disposition je vous pric de or tire chcr m msieur en rues sentiments ddvouds
the pause locations and durations transcribed by chafe see section NUM NUM NUM were omitted but otherwise all lexical and nonlexical articulations were retained
note that a majority of subjects agreed on only NUM of the NUM possible boundary sites corresponding to the segmentation illustrated in figure NUM
researchers have begun to investigate the ability of humans to agree with one another on segmentation and to propose methodologies for quantifying their findings
we now turn to the second question addressed in the segmentation study how to abstract a set of empirically justified boundaries from the data
we then partition cochran s q to determine the lowest value on the x axis in figure NUM at which agreements on boundaries become statistically significant
the underlying case representation only has to change when new knowledge sources become available to the nlp system in which the cbl system is embedded
results indicate that incorporation of the subject accessibility bias never improves performance of the learning algorithm although dips in performance are never statistically significant
in fact three of the rm recency representations now outperform the original baseline representation shown in boldface at the NUM significance level
we expect that this bias will have a positive impact on performance only when it is combined with linguistic biases that provide feature relevancy information
a separate instance representation need not be designed each time we want to apply the learning algorithm to a new problem in natural language understanding
features s human s ppl location pvevl syntactic type comma class s the antecedent is the subject
similarly the hardliners receives the attribute np2 because it is a noun phrase two positions to the left of who
NUM the last row in table NUM shows the performance of the baseline representation when this built in bias is removed by discarding the last constituent features
here we expect that very different manipulations of the baseline case representation will be needed to implement the linguistic biases presented in this paper
in future work we plan to investigate the use of the approach for feature selection in conjunction with other standard machine learning algorithms
values range from NUM to NUM with NUM representing that there are no more agreements observed in the data than would happen by chance
that is there can be many ways a given sentence could be derived from the stsg
in general the number of subtrees will be very large typically exponential in sentence length
introduction the data oriented parsing dop model has a short interesting and controversial history
the parse tree has probability of being generated by the trivial derivation containing a single tree
in this section we examine the empirical runtime of our algorithm and analyze bod s
this will be complicated by the fact that sufficient details of bod s implementation are not available
the parser must also determine the best parse from among tit diflzrent parsable subsets of an input
the set of semantic concept tokens for the scheduling domain was initially developed from a set of NUM example english dialogues
a recent focus has been on developing a detailed end to end evaluation procedure to measure the performance and effectiveness of the system
we describe this procedure in the latter part of the paper and present our most recent spanish to english performance evaluation results
grammatical constraints are introduced at the phrase level as opposed to the sentence level and regulate semantic categories
another recent focus has been on developing a detailed end to end evaluation procedure to measure the performance and effectiveness of the system
figure NUM shows the results of combining the two translation methods using the simple method described in the previous section
we art in the process of investigating some more sophisticated methods for combining the two translation at proaehes
our evaluations have confirmed that the glr approach provides more accurate translations while the phoenix approach is more robust
the glr parse quality judgement is used to determine whether to output the glr translation or the phoenix translation
with the generalization a blc in e
at about this point after NUM NUM merges we discard the constraint
the effect of further retaining the constraint is shown by the thin lines
the original procedure is improved by introducing constraints and a different initial model
now states are merged successively except for the start and end state
the experiments show the advantage of model merging over the standard bigram approach
the method first extracts meaningful collocations in the source language english in advance by the xtract system
kitamura matsumoto use the same measure to calculate word similarity in their work of extraction of translation patterns
a french word f is considered to be translated into english word ej that gives the maximum mutual information
NUM for each pair of bilingual word sequences the following similarity value sim w r
figure NUM shows the flow of the process to find the correspondences of japanese and english word sequences
two states are selected and removed and a new merged state is added
this paper proposes a method of finding correspondences of arbitrary length word sequences in aligned parallel corpora of japanese and english
the settings of the experiments are as follows the maximum length of the extracted word sequences is set at NUM
since frequent co occurrence suggests higher plausibility of correspondence we set a similarity measure that takes co occurrence frequencies into consideration
as a result most parsers can not be easily used in applications and it is difficult to develop a practical parser which can be easily integrated into many applications
similar approaches i NUM were used for segmentation or preliminary morphological analysis about NUM years ago using the transitionpoint between types of ehaxaeter sets to cue word segmentation
such as nonn verb noun verb NUM adjective l
1bunsetsu NUM i is a kind of phrasal unit i n japanese consistiug of one content word
flow of qjp s syntactic analysis under these approaches qjp s syntactic aataiyser processes words sequence in three steps figure NUM each following its own set of rules
simple treatment of structural ambiguities null structurm ambiguities are usually dealt with either by generating all possible structures or by selecting the more preferable ones ba sed on some scoring scheme
second it extracts all possible kakari uke bunsetsu pairs marked by o in b based on specific combinations of bunsctsu features for each bunsetsu pair
qjp performs two types of analysis NUM morphological analysis to segment a sentence into part of speech tagged morphemes and words NUM syntactic analysis to place words into bunsetsuldependency structure
in english in processing a sentence of an agglutinative language like japanese in which divisiono are not placed between words morphological analysis is the first harrier
a high scoring cluster is one whose members are classified similarly in the tagged lob corpus
our system scores higher than the finch system at all levels the hughes system scores better than ours over the
in these experiments we use maximum likelihood probability estimates based on a training corpus
parameter setting for the smoothed bigram takes less than a day on a sparc ipc
both authors thank the oxford text archive and british telecom for use of their corpora
this is a main motivation for the multilevel class based language models we shall introduce later
we therefore expect the topmost classifications to be less constrained and hopefully more accurate
one such problem concerns the way that n grams partition the space of possible word contexts
the system has been compared directly and indirectly with other recent word classification systems
in fact most previous work in sense disambiguation has tended to use different sets of polysemous words different sense inventories different evaluation metrics and different test corpora
thus the evaluation tool of tsnlp has been extended by a certain class of semantic information non ambiguous temporal expressions
what characteristics should common test suites exhibit
how should sense inventories be defined so as not to be biased in favor of certain disambiguation methods such as those based on selectional restriction topic codes hierarchical ontologies or aligned multilingual corpora
using the most probable derivation criterion one simply finds the most probable way that a sentence could be produced
furthermore for the first time the dop model is compared on the same data to a competing model
even in this case there is only about a NUM NUM chance of getting the test set bod describes
bod shows that the most probable parse yields better performance than the most probable derivation on the exact match criterion
following our earlier work we use a robotic framework
an overview of these concepts and related ones is given in section NUM NUM
finally in section NUM we discuss related work and the most pressing unsolved problems
the computer program based on the theory learns a natural language from examples which are commands occurring in mechanical assembly tasks e.g. go to the screw pick up a nut put the black screw into the round hole
in section NUM we describe some empirical results especially the comprehension grammars generated from learning the languages
our approach to machine learning of language combines psychological linguistic and logical concepts
we believe that the five central features of our approach 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 are either new or are new in their present combination
therefore there is a one to one mapping between trees in t and t
the transformation specified by this lemma closes over substitution of t and then discards t
the transformation specified by this lemma closes over substitution into and then discards t
we assume without loss of generality that g does not contain any useless production
this reduces the completion rules to a time complexity of o igin3
there are several ways that the efficiency of the tig parser can be improved
rule NUM supports the bottom up recognition of the adjunction of a left auxiliary tree
it does this top down only if an appropriate prefix string has been found
figure NUM depicts the earley style tig parsing algorithm as a set of inference rules
figure NUM shows five elementary trees that might appear in a tig for english
figure NUM shows an example of computing precision and recall for the sentence ta NUM NUM c j fi which means rockefeller laboratory is an academic laboratory founded by an american millionaire rockefeller
of all csi step by step esl with lower mcpi values as globally derived from all the corpus are filtered out the mcp1 values are then redistributed among the remaining esl s
introduction this paper presents techniques that can be used to analyze the formulation of a probabilistic classifter
he reports that in one hundred highest ranking correspondences ninety of them were correct
are typically viewed as representing high reliability see section NUM NUM
in section NUM we give a brief overview of related work
the following paragraphs describe the roles and responsibilities of each project element involved in the cm function
cm requirements processes and practices are conveyed to all tipster team members for compliance
if major modifications are necessary the se cm may submit an entirely new rfc
class ii changes are within the scope of authority of the erb for disposition
the representative must have full authority to act on behalf of the missing member
each board member is allowed to state his her official position on the proposed change
the ccb provides a central point of coordination of changes to the baseline architecture
specifically the interfaces must be as described in the tipster interface control document
the erb will be comprised of the se cm and the developing contractor s representatives
NUM documentation detailing any as built discrepancy or deviations from the tipster architecture
at any point in the development process the user can choose to produce the partially built spl plan or generate through penman the english realization of the current structure
for example the word were is an auxiliary to the word produced from the concept crea ti ve ma terial a ctio n because of tense requirements
in order to facilitate the growth of natural language generation research systems should be able to handle more complex input but at the same time should be more accessible to non experts
such a facility should have both theoretical knowledge of the allowable form and content of the input specifications and the practical ability to ensure that only syntactically correct specifications are entered
the template also gives the user access to a number of different tools and resources including the actual spl that gets constructed and the generator s output from the constructed spl
the sentence bank can be searched for candidate plans that can then be used in the creation of new sentence plans specific to the domain of interest
for example the template for a verbal process will display the roles relevant to this type of process sayer addressee and saying
a lot of computation time can be saved by choosing an initial model with fewer states
a class of models that can serve as the initial model as well are n gram models
but it can be further retained until no further merge under this constraint is possible
model merging is a technique for inducing model parameters for markov models from a text corpus
the merged model assigns a lower perplexity to the test set and uses considerably fewer states
the initial model of the original model merging procedure is the maximum likelihood or trivial model
pour pourer volitional for a sentence to classify as a particular alterna tion a legal linking must exist between an event and the subcategorization
once a sentence passes the linking test and classifies as a particular alternation a rule associated with the alternation classifies it as a specimizalion of the concept
that is given a set of syntactically analyzed sentences that exemplify the syntactic alternations allowed and disallowed for that verb the classifter will provide appropriate linguistic hypotheses
world knowledge is used in the definitions of nouns and verbs in the lexicon and describes high level entities such as events and animate and inanimate objects
the mapping from the gfs to the appropriate argument structure is similar to lexical rules in the lfg syntactic theory except that here i semantically type the arguments
in general a corpus of tagged sentences is inadequate since it rarely includes negative examples and is not guaranteed to exhibit the full range of alternations
while i have focused on a lexical research tool an area i will explore in future work is how classification could be used in grammar writing
such sentences exemplify an alternation that belongs to the alternation pattern of their verb NUM i will call this the alternation type of the test sentence
a side effect of the alternation classification is that the event classifies as a specialized event and indicates which sense of the verb is used in the sentence
the syntactic ana ysis uses no semantic information only part of speech and other syntactic information
characteristics of written japanese a japanese sentence has no spaces between words figure NUM
se entation of words by hor ological klalyser i NUM
not using of semantic inibrmations most methods for analyzing japanese use c e patterns with semantic features for preference selections
it is an effective parser for many general purpose applications despite of a dictionalt size of only NUM thousand words
if the application user corrects any alternative kakari uke pairs the most likely set is re calculated using retaining possible kakari uke pairs
then using the allocation rules part of speech candidates are assigned based on the sequence s character set and length
the candidates au e disambiguated by checking connection with the the following morphemes based on the connection table between morphenm parts of speech
we also take this approach because it is intuitive understandable and easily implemented
to represent the relevance space in a sentence specification i initially provided a relevant entities field which listed those ideational entities which were relevant for expression
now we might wish to express a sentence to the effect that a dog destroyed mark s house which ignores mark s ownership of the dog
while salutory moves do not rather speech function the speech function is the speaker s indication of what they want the hearer to do with the utterance
the information basically tells that mary left a party because john arrived at the party tell is a lisp macro form used to assert knowledge into the kb
the first is sometimes termed terminological knowledge knowledge about terms and their relations the second assertional knowledge knowledge about actual entities and their relations
one does not need to specify features which are systemically implied e.g. specifying propose is equivalent to specifying and move speech act negotiatory propose
this integration allows economies of generation not possible where content used for text planning and content used for sentence generation are represented distinctly
according to grosz focus is that part of the knowledge base relevant at a given point of a dialog
the participants in an interaction each possess a certain amount of information some of which is shared and some unshared
quality i pdeglar quality role information a specification of the roles of the entity and of the entities which fill these roles
in particular two sets of rules are required to account for advp and adjp constituents in cgf since only the latter is subject to agreement
a more realistic definition of local lexical specialization is the concentration of the tokens of a given word within a particular text slice
any new code or capabilities for the tipster application must be developed in accordance with the tipster architecture
instead we implemented the structure of the vp as a verbal complex of the auxiliary and the past participle which is sister to the complements
this representation gives jff an appropriate morphological decomposition preserving information that would be lost by simply listing as an unanalyzed form
we can model this probability straightforwardly enough with a probabilistic version of the grammar just given which would assign probabilities to the individual rules
the high NUM tone of would not normally neutralize in this fashion if it were functioning as a word on its own
examples will usually be accompanied by a translation plus a morpheme by morpheme gloss given in parentheses whenever the translation does not adequately serve this purpose
as can be seen gr and this pared down statistical method perform quite similarly though the statistical method is still slightly better
nonetheless the results of the comparison with human judges demonstrates that there is mileage being gained by incorporating models of these types of words
since foreign names can be of any length and since their original pronunciation is effectively unlimited the identification of such names is tricky
for each pair of judges consider one judge as the standard computing the recall of the other s judgments relative to this standard
our system does not currently make use of titles but it would be straightforward to do so within the finite state framework that we propose
however some caveats are in order in comparing this method or any method with other approaches to segmentation reported in the literature
in that cruse it leaves behind a stranded quantifier except in the e use of rule d where that quantifier is null
specific subject indexing topics that have been tested are advertising expenditure lntranet job interview and mutual fund
the guiding principle for building the gui component of our prototype system is to automate the manual process of capturing information content out of large document collections
the intention is to make the resulted corpora cover a much greater variety of topics or domain subjects than the focused dataset
various existing and workable term extraction tools are either statistically driven or linguistically oriented or some hybrid of the two
it helps spot significant terms which would normally be missed and objectively examine the significance level of certain fuzzy and ambiguous terms
if significant changes in the values of certain statistical variables are detected associated terms are selected from the focused sample and included in the final generated lists
the goal of the visualization is to present the key term lists in such a way that a high percentage of the hierarchy is visible with minimal scrolling
the design of the document browser is intended to provide an easy to learn interface for the management and manipulation of the document collection
when a term is selected from the tree a corresponding term lookup is conducted on the document collection to locate the selected term within the currently displayed document
each corpus was divided into NUM equally large text slices
f NUM NUM NUM NUM p NUM
a series of diagnostic plots is shown in figure NUM
denote the number of new underdispersed types for text slice k
first consider the effect of nonrandomness on the frequency distributions of morphological categories
for vu k f NUM NUM NUM NUM p
for the higher frequency ranks the estimates are fairly reliable
we shall not justify the reasons for our choice of gpsg but will simply point out that gpsg is a formal theory which is unification based and hence well suited for computational linguistics
now we expand the second a node
NUM NUM av grammars and the erf method
we can obtain the globally best weights by iterating
in that case we must use random sampling
in particular for types that have not been seen in the training corpus and for which we therefore have no direct estimate of the word specific prior probabilities we would like to know whether the hapax based or overall mle provides a better estimate
NUM NUM a paired t test on the ratios no inf no pl versus eo no inf eo no pl reveals a highly significant difference t9 NUM NUM
since the hapaxes of a particular morphological process mostly consist of non idiosyncratic formations from that process it makes sense that the distribution of a property among the hapaxes is the least contaminated estimate available for the distribution of that property among the unseen cases
consider a moved car a tried approach an asked question an appeared ad but contrast an oft tried approach a frequently asked question a recently appeared ad where an adverbial modifier renders the examples felicitous
it consists of modules for speech signal feature extraction hard or soft vector quantization and beam search driven viterbi training and recognition
tile different phases of the text to speech transformation are performed by separate independent modules operating sequentially as shown in figure NUM
this happened due to the exaggerated eagerness of the speaker trying to pronounce the meaningless logatoms in a correct and clear way
phone i model silence silence table l list of phones and their corresponding submodels used for slovenian iogatom segmentation
a standard bigram model b constrained model merging first experiment c model merging starting this is exactly what we find in the experiment
there is a large initial part of merges that do not change the model s perplexity w r t the test part and that do not influence the final optimal model
the probability never increases because the trivial model is the maximum likelihood model i.e. it maximizes the probability of the corpus given the model
an important difference to the second method with automatically derived categories is that with the manual definition a word can belong to more than one category
in these experiments we examine the proper granularity of abstracted concepts
ifsm differs from the path length model because it is sensitive to depth
relation based methods include both depth based and path based measures of similarity
relation b used sin itarity measure and distribution based similarity measure
these choices are not simply iml lemelltational but imply ditferent similarity judgements
each synset of deep triples is abstracted based on the flat probability grouping method
this level seems to be too general for describing the features of objects
table NUM synsets i y flat probal illty grouping metho NUM
we have adol te t the weighted laeeard
these models are smaller by one or more orders of magnitude than the trivial model and therefore could speed up the derivation of a model significantly
NUM clearly the divergence is significant for almost all of the first NUM measurement points
this simple fact is difficult to take into account in statistical models of word frequency distributions
for different numbers of text chunks virtually the same high frequency words appear to be underdispersed
first consider figure NUM which summarizes a number of diagnostic functions for alice in wonderland
basically parameters of these segmentation models are estimated by computing the relative frequencies of the corresponding events in the segmented training corpus
we call p w t the segmentation model although it is usually called tagging model in english tagger research
let us give another example that shows the effect of summing tlle expected word unigram counts over all the sentences in the corpus
in the second sentence in figure NUM the system divided NUM into NUM and NUM
table NUM shows the new word extraction accuracies for a variety of expected word frequency thresholds NUM with and without reestimation
the third word model consisted of the spelling model trained using all words and the length model trained using low frequency words
in this experiment we randomly selected NUM of the sentences in the edr corpus for training the word segmentation program
before we start we briefly explain the difficulties of japanese morphological analysis especially when the input sentence includes unknown words
here wi denotes a combination of orthography formally denoted by wi and part of speech ti for simplicity
let NUM denote the minimum expected word frequency that we use to classify a given word hypothesis w as a word
errors correlated with one of two kinds of the information used in the np algorithm identification of clauses ficus and of inferential links
however when either pause or cue is combined with the more global np as in pause np and cue np we see that performance improves
this was loosened to include referential links between an np referent and referents mentioned in or inferable from any part of the previous utterance
for a new word hypothesis w a i key score such that a w a i l key score exists for all e in vi l inactive in or vi l activein
NUM our initial algorithm does not take the duration of the pause into account pause duration is considered in the algorithms presented in section NUM NUM NUM
in one cycle a ba kwar search is performed to the beginning of the utterance or to some designated time point in the past con stitnting a starting point for grammatical analysis
np operates on the principle that if an np in the current ficu provides a referential link to the current segment the current segment continues
for x bigram grammar and prosody tpsoutside x a outside x i lnside x NUM tans x a NUM lps lnsidc x a lnside x NUM NUM 1nside x NUM NUM rans x a NUM i or x acoustic e outside x a oulside x l from
each potential boundary site in our corpus is coded for features representing the three different sources of linguistic information of interest prosody before sentence final contour sentence final contour
in particular the way in which c4 NUM minimizes error rate is not an effective strategy when the distribution of the classes is highly skewed
in a system like intarc NUM NUM the analysis tree is of much higher importance than the recovered string for the goal of speech translation an adequate semantic representation for a string with word errors is more important than a good string with a wrong reading
metaphoric uses of cure and heal tend to take direct objects which are target domain analogs of disease and wound respectively
in frame semantics we take the view that word meanings are best understood in reference to the conceptual structures which support and motivate them
although the mapping between word shape tokens and words is one to many they are a rich source of information for content characterization
evidence is found in NUM lcb NUM lcb
lie a mbiguities is illusl ra teel
morphological processing NUM NUM word boundary ambiguity
a gradual refinement model for a robust thai morphological analyzer
fig NUM shows an overview of the system
NUM NUM semantic based pruning and implicit spelling correction
means a word in the right
three types of implicit spelling error
NUM three nontrivial problems of thai
these proposed constituents are stored in a data structure called the keylist
another goal is to understand better what our users need from their retrieval systems and demonstrate an ability to provide retrieval improvements which are meaningful to them
he continues to lead these two groups through the period of revisions to the architecture as a result of its use in tipster demonstration projects and elsewhere
the nlp module is used to identify appropriate multiword phrases for use in indexing and in processing the user s natural language search requests
the results of this work which incorporates much that has been learned from other tipster participants using this method increase our understanding of the role of syntax in information extraction and will be used to further our progress toward the goal of an extraction system which can be configured for a task by the user
one goal of the experiments is to contribute to the on going tipster work in the use of combined detection and extraction systems
the nyu system has been modified to be tipster architecture compliant and has participated in trec3 and trec NUM under the tipster contract
the following packages are available from crl a tipster compliant document manager and user documentafious a tipster document manager validation suite a graphical user interface toolkit to support development of multi lingual tipster applications and english name recognition software and data
wi about wi i stories wi NUM the wi l well heeled wi NUM communities ti i nns ti 2ti NUM dt nns
unlike sdt the maxent training procedure does not recursively split the data and hence does not suffer from unreliable counts due to data fragmentation
p tiw p w p tl p t21tl x h p tilti lti NUM p wilti i NUM
the rare word features in table NUM which look at the word spellings will apply to both rare words and unknown words in test data
the search is described below NUM generate tags for wl find top n set 81j NUM j n accordingly
table NUM shows the results of an experiment in which specialized features are constructed for difficult words and are added to the baseline feature set
this model also can be interpreted under the maximum entropy formalism in which the goal is to maximize the entropy of a distribution subject to certain constraints
here difficult words are those that are mistagged a certain way at least NUM times when the training set is tagged with the baseline model
and the observed feature expectation is and where is hi ti denotes the observed probability of hi ti in the training data
considering just a single negative instance say the jonses read thick this book do what is are the misplacenmnt s and where do they occur
each of the rules at level NUM are neither more general nor more specific than each other but are more general than the most specific rules at the bottorn
analogously the instance selection rules serve to transform the high level hypotheses rules to a representation useflfl for guiding the search in the instance space
we have described a program that learns the lp rules of an id lp logic grammar in a form that can be directly utilized by that grammar
tile basic idea is that ill all representation languages for the rule space there is a partial ordering of expressions according to their generality
the size of the lp rules space will deperid upon the size of the specific id grammar whose lp rnles are to be learned
this may be a problem in our task and perhaps in many other linguistic tasks since our assessments of grammaticm ungrammaticm word order are in some cases far from definite yes no s
the leart ing method assumes a set of positive and negative examples and its aim is to induce a rule which covers all the positive examples and none of the counterexamples
the NUM set however does not so it has to be uiilliinally generalized to cover it obtaining g set de t nuui
so fie lllost sl eci i illsta l es rettlaill lllcovere l
in january the training texts were released along with NUM sample unannotated training texts to the participating sites
the original task guidelines were modified so that the core guidelines were language independent with language specific rules appended
the program compares the human generated answer key and the system generated responses to produce a score report for each system
the f measure is used to compute a single score in which recall and precision have equal weight in computation
papers from each of the sites then briefly provide technical descriptions of their systems and participation in met
the f measures in table NUM for the human performance baseline were also preduced by the automated scoring program
analysis revealed that inter analyst variation on the task is quite low and that analysts performed this task accurately
first of all multilingual named entity extraction is a technology that is clearly ready for application as the score ranges indicate in appeared to encourage experimentation which is evidenced in the technical discussion of the snmmary site papers NUM NUM NUM
the accuracy results are the f measures obtained by comparing each analyst s answer key against the final answer key
on the other hand average mutual information depends only on the distribution of x and y and not on the actual values of the random variables
as a concrete example consider a corpus of NUM matched sentences where each of the word groups associated with x and y appears five times
the simple way to do this is to build the models as sets of atoms and then test the expression to see if it holds of each one
given a feature with values in some set of atoms or product of sets of atoms any boolean combination of these can be represented by a term
the specific formats for the fills is also included
in this way the components shall be interchangeable
the easiest way to implement this use of threading is by defining and using macros such as those given earlier for illustration
this can have a further advantage in that now the same technique can be used to eliminate disjunction for words where there is sense ambiguity but no syntactic ambiguity
that will be exactly the same number of parsing hypotheses as we would have had with the original gpsg treatment and so there is no obvious advantage here
more generally put this specification on that category of the rule introducing the conditional constraints which contains the feature specification figuring in the consequents of the conditional constraints
the language consists of sequences of a verb vabcd vbcd or vbd followed by the things it is subcategorized for in any order e.g.
notice that the number of arguments in the term that we build for one of these boolean expressions depends on the size of the sets of atomic values involved
when an interpolated trigram language model uses smoothed bigram estimates test set perplexity reduced by approximately NUM NUM compared to a similar system with maximum likelihood bigram estimates and NUM compared to the weighted average language model
therefore we conjecture that interactors in this setting will not be concerned with social standing
one reason for this apparent improvement may be that their baseline model constructed as it is out of much more training data is already better than our equivalent baseline so that they find improvements harder to achieve
however it has been widely demonstrated that they do not
figure NUM percent of first use of words in common for agent and client in each setting
human human accommodation was higher than coincidental overlap but lower than both of the interpreted settings
since the segmentation corresponds to the sequence of words that has the lowest summed unigram cost the segmenter under discussion here is a zeroth order model
sproat shih gale and chang word segmentation for chinese jtangl o o adv NUM NUM o o
we have shown that at least given independent human judgments this is not the case and that therefore such simplistic measures should be mistrusted
tm in what follows we will discuss all cases from this set where our performance on names differs from that of wang li and chang
a greedy algorithm or maximum matching algorithm gr proceed through the sentence taking the longest match with a dictionary entry at each point
sproat shih gale and chang word segmentation for chinese logical rules and personal names the transitive closure of the resulting machine is then computed
the interdependence between j or and is not captured by our model but this could easily be remedied
NUM chinese speakers may object to this form since the suffix meno pl is usually restricted to attaching to terms denoting human beings
the simplest version of the maximum matching algorithm effectively deals with ambiguity by ignoring it since the method is guaranteed to produce only one segmentation
the tipster cm process imposes two control gates pdr and foc on the tipster application development lifecycle as shown in figure NUM NUM
construction and visualization of key term hierarchies
the system consists of two components
NUM NUM graphic user interface gui
often data structures are organized linearly by some metric
they all target frequently co occurring words in running text
different applications aim at different types of key terms
document store provides the interface to document collections
the left area of the gui component see figures NUM and NUM is devoted to selecting retrieving and operating on the key terms generated by the preprocessing component of the prototype system
the preliminary results indicate that the system can provide internal specialized library developers as well as subject indexing domain experts with an ideal automated toolkit to select and examine significant terms from a sample dataset
second a graphic user interface gui is established that provides the domain expert or the user with an interactive environment to visualize the key term hierarchy in the context of the original dataset
it is recognized that there may be some reduction in capability when common knowledge items are used in an application instead of unique customized knowledge items however the history of other technical areas indicates that common items and sharing has merit and possible pay off
specific algorithms even those which use items in the persistent knowledge repository are not considered part of the repository but instead are included in the particular instance of an implemented requirement however the structure of the particular knowledge item is an architectural item
the first two stages are likely to introduce theoretical disagreements and the last two errors
table NUM NUM is a list of major events milestones and specifies the cm activities conducted and the planned dates for initiation and completion of each event milestone
netowl tm server is a powerful text analysis software product developed by isoquest inc
with netowl it is no longer necessary to scroll through massive amounts of text to find exacdy what is useful
netowl is the total application built on the nametag core engine
it organizes analyzes and summarizes data extracted by nametag tm and any full text search engine
netowl tags each desired document or web page by person organization location relationship and description giving you a browsable back of the book index
it then presents the data for either searching or browsing
this allows for targeting exactly key information that is sought
it supports business intelligence by providing fast and easy access to information stored on local intranets and the global internet
nametag is a data extraction and indexing tool that finds proper names and other defined entities within an input text stream
it searches for the best classifier attribute in a given table
table NUM optimally generalized single attribute table for take
filled templates shall appear as annotations with links to relevant text spans in the source document
in this paper we present a new dtla that can rationally handle the structured attributes
figure NUM shows an example decision tree representing acase frame for the verb take
the error rate also decreased from NUM NUM dtla to NUM NUM lasa NUM
the purity threshold for halting the tree generation was experimentally set at NUM NUM for both algorithms
in the process of tree generation the algorithm generalizes each attribute optimally using a given thesaurus
NUM NUM NUM NUM clue NUM punctuation marks personal names usually appear at the head or the tail of a sentence
because this type of personal names is rare in the testing newspaper corpus the variation is not large
even so title is still a useful clue it can help determine the boundary of a name
three newspaper corpora total size is about NUM NUM million words are used to train the word association
where ci is a character in the input sentence p ci is the probability of ci to be a surname or a name ci is the frequency of ci to be a surname or a name ci is the frequency of ci to contain in tile other words
the reasons why the recall is not good enough are some transliterated personal names e.g. c oi j and d lcb look like chinese personal names and the identification of chinese personal names is done before that of transliterated personal names
NUM NUM NUM NUM clue NUM cache a personal name may appear lnore than once in a paragraph this phenomenon is useful durmg identification we use cache to store the identified candidates and reset cache when next paragraph is considered there are four cases shown below when cache is used a cic2c3 and c1c2c4 are in the cache and c i c2 is correct
figure NUM coordination of eats cookies and drinks beer
the corer feature falls into this category
the subscripting on noun phrases indicates coreference
we refer to this algorithm as np
cue phrases and referential noun phrases
for read and NUM for spontaneous speech
normalized the data before averaging across narratives
unsurprisingly np performs most like humans
what lexicai levels are required in a lexicon
null what are the interdependencies between these levels
because they are language specific semantic tagsets are not constructed based on the same methodologies as general universal conceptual ontologies
can a parallel be drawn with the tagging of corresponding lexical units of other languages you know
should the levels be kept as separate layers and related explicitly or should they be combined into one layer and be related implicitly
thus maxent has at least one advantage over each of the reviewed pos tagging techniques
by extracting those articles tagged by maryann in the treebank v NUM cdrom
consistently tagged words should have roughly the same tag distribution as the article numbers vary
the count of NUM was chosen by subjective inspection of words in the training data
such features can be designed for those words which are especially problematic for the model
the single annotator development set is the portion of the development set which has also been annotated by maryann
table NUM shows the change in error rates on the development set for the frequently occurring difficult words
the specific word and tag context available to a feature is given in the following definition of a history hi
in the future we plan to design more flexible techniques that would work from a loosely aligned corpus see section NUM
to achieve efficient processing of the corpus database champollion is implemented in two phases the preparation phase and the actual translation phase
it is not the meaning of the words lay and set that determines the use of one or the other in the full phrase
this tool has been used in several projects at columbia university and has been distributed to a number of research and commercial sites worldwide
collocations are notoriously difficult for non native speakers to translate primarily because they are opaque and can not be translated on a word by word basis
for example mr speaker is the proper way to refer to the speaker of the house in the canadian parliament when speaking english
smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons table NUM dice versus specific mutual information scores for the english word today
other groups of words in le cause similar problems including to take steps to provi2 these corpora had little noise
on the other hand the compound provisions of the charter is very commonly used as a whole in a much more rigid way
a word about the i1 i p parser generator
indeed because of the noise and nondeterminism inherent to linguistic data we feel strongly that stochastic algorithms for language induction are much more likely to be a fruitful research direction
in contrast the nondeterministic two level style transducer shown in figure NUM has two possible arcs leaving state NUM upon seeing a t one with t as output and one with dx
calculating information content of a given feature can be done in o k time because k is an upper bound on the number of possible outcomes of the decision tree
by initializing the decision tree inducer with a set of phonological features they essentially gave it a priori knowledge about the kind of phonological generalizations that the system might be expected to learn
these representational differences between the two formalisms lead to different ways of handling certain classes of phonological rules particularly those that depend on the context to the right of the affected symbol
in the latter case the transducer is restored to its configuration before the merger causing the original conflict and the algorithm proceeds by attempting to merge the next pair of states
note that the algorithm assumed that zr ziue2 where zi and z2 arc the one byte e.g.
a word compounding part determines a word fl om morphemes using word constituent rules based not only on inflections but also on compounds or derivations such as those shown above
most words written in kanji or katakana are content words such as nouns verb noun and stems of verbs or adjectives
such as auxiliary verbs ii j j and postpositional particles tljj and carrlng one concept
this is especially true when some initial hand tagging of a corpus is required for predicting lexical priors for very low frequer cy morphologically ambiguous types most of which would not occur in any given corpus one should concentrate on tagging a good representative sample of the hapax legomena rather than extensively tagging words of all frequency ranges
many j l aamse syntactic analyses are ba ed on orthodox bunsets depcndency analysis called kakari ui e a anmysis
the japanese language has few syntactic indicators to show the segments of sentences but is rich in semantic indicators which suggest sentence structure
ilowever it has not claritied the level of tile encapsulation powers of japanese flint tion words or tile relation between modality and level
tile japanese language has few syntactic indicators for dividing sentences into phrases or clauses unlike english with its relative pronouns and subordinate conjunctions
one of the most critical features of japanese is that the difference between a phra se and a clause is not cleat
we assign a conjunction level to each conjunctive particle and reduce the number of possible syntactic structures of japanese long sentences
it is also possible to consider the pause length inserted after each clause in relation to the lex ical information in ldg
the level of conjunctive particles which indicates the structure of the japanese long sentences is the most important feature of ldg
null ldg is effective in reducing the syntactic ambiguities and it him alre my been applied to a machine translation system
there are six levels of modality in conjunctive particles and there are four types of modality in predicates as mentioned above
in this section we apply the level to another linguistic phenomenml in order to onfirm the validity of this model
as case marking particle ga normally indicates the subjective case binding ga to the subjective case leads to incorrect mmlysis if only surface spelling is considered
in this paper we use these terms interehangeably
the ramified tree is hard for humans to understand
by applying tiffs melhod to jap mese senlence malysis in japanese to english machine mslalion systems tl m slalion accuracy can be improved because tiffs melhod eml analyze correclly the double subject construction
proposition NUM a traversal graph is a dag
cat to satisfy the complete cover
table NUM shows the percentage of each evaluation item
some elaboration in the penalty function might be required
we used NUM english japanese sentence pairs
the effectiveness of oracle filters based on mrbds will depend o11 the extent to which the vocabulary of the mrbd intersects with the vocabulary of the training text
the cognate filter allows the fourth item a cognate to percolate up to second place and makes room for two party in sixth place
the part of speech filter realizes that premier can only be an adjective in french whereas in the english hansards it is mostly used as a noun
the decision procedure used to select lexicon entries from the multiset of candidate translation pairs is a variation of the method presented in gal91a
the results in figure NUM indicate that the filters described in this paper can be used to improve the performance of lexical transfer models by more than NUM
all the confidence intervals were narrower than one percentage point at NUM pairs of training sentences and narrower than half of one percentage point at NUM pairs
it is based on the idea that word pairs that are good translations of each other are likely to be the same parts of speech in their respective languages
figure NUM word alignment filter partitioning loci marked d are translation pairs found in the mrbd while those marked c are cognates
each such partition reduces the number of candidate translations from each sentence pair by approximately a factor of two an excellent noise filter for the decision procedure
the word alignment filter removes from consideration candidate translation pairs like ont mentioned which would cross the partition created by aussi also
in order to get the right entry namely as it is possible for the alert user of gi osseiblhk to override the decision of the
the first part is to gel all the keys of words starting with a particular string from the first in dex
this key is then used in the second part where for every file in the corpus two extra index files are generated
the agent our government does n t actually physically take anything rather it has begun the process of enforcement through small concrete actions
in our example the second word langues appears in most cases one word before officielles to form the compound langues officielles
its most important function however is to remove from consideration words that appear too few times for our statistical methods to be meaningful
we then ran champollion on each of these sets using three separate database corpora of varying size also taken from the hansards corpus
in this paper we describe the statistical measures used the algorithm and the implementation of champollion presenting our results and evaluation
does not require that berlin be visible
lace provides for the instantiation of simulated ground vehicles such as tank units and sam missile carriers and includes a number of routines for onroad navigation including a route planner for computing paths between towns
our focus is on the use of statistical methods for the translation of multiword expressions such as collocations which are often idiomatic in nature
because of the large size of the database over twelve thousand objects and the need to reduce the search space during information retrieval we elected to constrain the domain of nl discourse to only those objects currently visible in the map display representing about one eighth of the entire database at any one time
interlace is a multimodal interface to the air force s lace land air combat simulation system containing an extensive real world cartographic database of central germany
the presentation will provide a brief overview and demonstration of the components that comprise the microsoft nlp technology including the syntactic parser logical form component and lexical knowledge base mindnet
speaker our government has demonstrated its support for these important principles by taking steps to enforce the provisions of the charter more vigorously
unlike koalas lace came to us with no graphical interface although all the hooks were in place to implement a map display with animation of simulated objects
the tank understands both cardinal northeast and relative directions left and in ambiguous situations chooses the stretch that bears the most closely in the specified direction
we take this approach rather than developing new fully integrated systems from scratch to facilitate the development of navy demonstrations of nl technology applied to their own tools as well as to explore the relative strengths and weaknesses of graphical vs nl interfaces and how the two can be used in combination to yield more powerful interaction techniques
eucalyptus and interlace are two projects integrating spoken natural language interaction with the already developed graphical user interfaces of currently existing military simulation systems
NUM interactions must be considered explicitly between new edges and all edges currently in the chart because no indexing is used to identify the existing edges that could interact with a given new one
however in these cases the semantic material remaining to be expressed contains predicates that refer to this internal index say tall p and young p
the procedure will be reminiscent of left corner parsing
because the proper nouns are usually segmented into single characters they will interfere with one another during identification and classification
the variation of a character is measured by the inverse of the frequency of the character to be the other words
however the unrestrictive length of transliterated names and homophones in chinese result in the need of very large training corpus
if the identified results are not classified the average precision is NUM NUM and the average recall is NUM NUM
the perfornmnce evahmtion criterion is very strict not only are the proper nouns identified but also suitable features are assigned
however the resolution of unknown words i.e. those words not in the dictionaries form the bottleneck
we not only tell if an unknown word is a proper noun but also assign it a suitable semantic feature
especially in the token analysis there is some evidence for positive autocorrelation at lag NUM and for a negative polarity at time lags NUM and NUM
if any portion of a document matches a portion of a query that has a priority attribute then the document is assigned that priority
typical factual information to be extracted includes but is not limited to entities objects events entity relationships and event relationships
tools and enhancements to assist in applying the architecture to new tasks applications and languages should be identified and where possible developed
the architecture shall recognize the importance of appropriate response times particularly in the interactive mode and not impede implementations from meeting accepted standards
note that in the sequel mppwg mps mpp denotes the decision problem corresponding to the problem of computing the mpp mps mpp from a word graph word graph sentence respectively
in the reduction the terminals of the constructed stsg are new symbols vii NUM i m and NUM j NUM instead of t and f that becomc non terminals
a similar problem arises with NUM allan is living in bray
rather that we do n t think about the time they take
we have to be carefifl here
NUM mary eats a peach for her lunch
if this happens then the principle has no force
conclusion that such an activity hal pens on a regular basis
consider the following pair of sentences NUM allan lives in bray
we now turn to the simple aspect
responding requests utterance d is a request in response to the destination that the user informed
a promise move is always in response to a request move and the relevant partial structure is
also the method we have applied here to develop our model has been solely qualitative
this allows us to always have an illocutionary force that can be refined as more of the utterance is processed
this is knowledge about the type of discourse or genre
the structure of the model remains constant across domains but the actual details of constructing plans remain domain specific
discussing a sample dialogue section NUM NUM we will then apply the model developed
viewing the output as a sequence of speech acts has significant impact on the form and style of the grammar
as indicated in the partial structure above an assert move can follow a promise act
these one byte characters and the standard one byte characters e.g.
arc copied across to compute NUM
let p be a proper representation of u q is a minimal underspecifiedpart of p if it does not contain any strictly smaller underspecified part q
for speech and language processing lattices bearing such trees are also used which means at least NUM levels at which a representation may be ambiguous
as far as utteranceqevel ambiguities are concerned let us stress again that we consider only those which should be produced by a state of the art analyzer constrained by the task
but in a standard f structure one can not force tile presence of an attribute so that a necessary attribute may be missing ref NUM
about NUM pages of dialogues gathered at atr have been labeled monolingual dialogues in japanese and english and bilingual woz dialogues NUM
in the case of an indeterminacy of semantic relation deep case e.g. on some argument of a predicate q would correspond to a whole phrase v
this format is independent of the exact kind of output produced by any implemented analyzer essentially because ambiguities are described with a view to generate human oriented questions
this question is not as trivial as it seems because it is often not clear what we exactly mean by the representation of an utterance
i the an ambiguity kernel of a u v pi p2 pm pl p2 pn is the scope of a and its proper representations
the architecture shall provide for the use of a common stemming library that can support various document processing tasks in different applications
first of all current language teaching methods tend to emphasize oral language skills especially in second language instruction
taught by age old methods many learners only have an inkling of the meaning of important grammatical concepts
learners will be deprived of knowledge that in fact is essential to solving writing problems
more serious is the ensuing lack of interest in the improvement of grammar instruction methods
teaching first and second language writing skills is very laborintensive because teachers need to mark large numbers of test papers
it is in the area of written language instruction that nlp technology can find a wealth of extremely valuable applications
the negative attitude toward grammar is strengthened by the generally disappointing outcomes of grammar based language teaching methods
equally important are writing skills orthography formulating well formed sentences composing clear and well organized texts
box NUM NUM rb leiden the netherlands kempen rulfsw leidenuniv nl
indeed present day call systems hardly employ natural language processing nlp techniques
currentcats keeps track of the category of the morpheme which occures in the current partition
the recursive coerce predicate ensures that no partition is violated by an obligatory rule
the base case of the predicate is given in listing NUM NUM and the recursive case in listing NUM
this paper presents a generalised two level implementation which can handle linear and non linear morphological operations
surface alphabet tl alphabet NUM cl c2 c3 v
additionally the interpreter facilitates the incremental compilation of rules by simply allowing the user to toggle rules on and off
semhe NUM fulfils this requirement it is a generalised multi tape two level system which is being used in developing non linear grammars
table NUM summarizes our results for simultaneous part of speech and semantic class i.e. word sense tagging NUM details regarding the experiments are included as part of table NUM
given as input a baseline instance representation comprised of both relevant and irrelevant attributes the method modifies the representation in response to any of a number of predefined linguistic biases
in summary this section presented a linguistic bias approach to feature set selection and applied it to the problem of finding the antecedent of the relative pronoun who
in our treatment a weight is assigned to each entry in the cache
this results ill a considerable computational simplification of the model but as we shall see below
certain types of pp objects favor at null tachment to the verbal or nominal site
p hlel x p hlej x NUM
in actual texts there are often more than two possible attachment sites for a pp
one may easily understand that the traversal graph will be a dag since formula NUM allows an arc between two nodes to be spanned only in the direction that increases the index number of the leaf
does the word include a nunlber
yet from among these innumerable possible subcategoties of entity names a few would seem likely to emerge as more well motivated than the rest
step NUM chart the distribution of ne data table NUM following the tex0 provides a summary of the test results
so the following observations are at best an interpretative error analysis informed by knowledge of the language and of likely user expectations
the resulting inventory of subtypes can be thought of as an hypothesis that the ne data is describable in a certain interpretative yet systematic way
the workshops were repeated at NUM month intervals for the duration of phase i selected researchers from other arpa human language technology hlt programs were also invited
proposals were solicited in june of NUM and eventually three contractors were selected to investigate different approaches to detection and another three were selected for extraction research
there has been improved recall higher recall of relevant documents and improved precision the user reads fewer useless documents in fmding the ones he wants
we also wish to thank wiley harris and sharon kaufmann for their suggestions and assistance in assembling and organizing all of the materials presented in this summary book for
a successful partnership the continued close cooperation of multiple government organizations in formulating and implementing the tipster program has been a major ingredient in its success
the contractors described their specific approaches to detection and extraction and laid the groundwork for the future sharing of ideas and of software and data resources
this architecture was developed as part of phase ii r d through the cooperative efforts of multiple contractors coordinated by an independent systems engineering configuration management contractor
this procedure was costly in terms of labor commitment and the training required and there was wide variance in the accuracy and consistency of the database content
the pre existing state of the art the two language technologies which arpa initially hoped to advance through the tipster program were document detection and information extraction
based on a variety of lessons learned and encouraged by the results of tipster phase i a four part program began to informally take shape
does the word include a period
touretzky elvgren and wheeler s approach seems quite promising our use of decision trees to generalize each state is a similar use of phonological feature information to form generalizations
in our reimplementation of this lnethod
in these cases we would argue that it is still appropriate to use the kappa statistic in a variation which looks only at pairings of agreement with the expert opinion rather than at all possible pairs of coders
taken in isolation is very similar to the semantics of the corresponding patr statement both assert equality of values associated with the two paths
NUM NUM experimental results three attachment sites
with a slow system which can analyze only a few sentences per minute it is possible to perform only one or at best two runs per day over the full training corpus severely limiting debugging
basically a noun group consists of a noun and its left modifiers the first five paragraphs the yellow brick road such groupings can generally be identified from syntactic information alone
3it also specifies that the attributes of these three constituents are to be bound to the variables person at verb at and company at and that the procedure when run is to be invoked when this pattern is matched
these include in particular patterns to recognize the scenario events such as smith became president of general motors smith retired as president of general motors and smith succeeded jones as president of general motors
with a limited development time four weeks for this muc we were able to develop a system which obtained a recall of NUM and a precision of NUM with a combined f measure of NUM NUM on the test corpus
in the case of muc NUM the task the scenario was to identify instances of executives being hired or fired from corporations3 most of the stages of processing are performed one sentence at a time
during this time while experimenting with many aspects of system design we have retained a basic approach in which information extraction involves a phase of full syntactic analysis followed by a semantic analysis of the syntactic structure NUM
v te lkmnd that this particular error is one if the mne for which there is no c orreet candidate in the n best hypotheses
in recent weeks hyundai corporation and fujitsu limited announced plans for memory chip plants in oregon at projected costs of over one billion dollars each in recent weeks continental version
in particular the number of errors for the base system and the minimmn number of errors obtainable by choosing the n best hypotheses with minimmn error are important
tile system we will describe here is an incremental adal tation system which uses only the inlbrmation the syst em has acquired from tile previous utterances
also the do tnnent dequen y in sublanguage articles has to be at least NUM times the word dequency in the large corpus
the larger this s ore of a word tile more strongly the word is related to the sublmlguage we found through the tirior discourse
however we can imagine that if their language score can be computed with high confidence for a particular word then our model should have
there exist some hypotheses which have billion at the right spot the 47th ean lidate is the top candidate which has the word
as you can see a mistake in sri s hypothesis membership instead of memory and chip was replaced by the correct wor ts
frontier nodes labeled with are referred to as empty
however the following differs from the definition of tag
NUM NUM comparison of the ltig gnf and rosenkrantz procedures
this procedure is referred to below as the gnf procedure
this is an essential difference and can not be avoided
this is necessary to remove infinite ambiguity and empty rules
empty auxiliary trees are both and cause infinite ambiguity
schabes and waters tree insertion grammar figure NUM left adjunction
the tree t can be eliminated by applying lemma NUM
this can lead to the creation of new empty trees
the use of passages seemed to have httle effect
of these NUM used automatic construction of queries
this sampie is then shown to the human assessors
task NUM was to construct the best query possible
this is represented by q1 in the diagram
null inqi02 university of massachusetts at amherst
figure NUM best trec NUM automatic adhoc results
figure NUM best trec NUM manual adhoc results
eleven groups took part in this track in trec NUM
there were also five new tasks called tracks
for co nparison the accuracy of lh ill s unsupervised english tagger was NUM NUM using NUM NUM word penn treel ank texts
one could derive such biases fronl a corpus as discussed in merialdo 199d but it unfortunately requires a tagged cort us
q he main task of the discourse processor was to relate that representation to the context i.e. to the plan tree
the discourse processor must be able to weigh these various predictions in of der to determine which ones to believe in specific circumstances
however our results also indicate that we have not solved the whole problem of combining non context and context based predictions for disambiguation
the basic assumption of our disambigua tion approach is that the context based attd non context based scores provide different perspectives on the disambiguation task
as mentioned in the introduction for a plan based disconrse processor to deal with ambiguities three issues need to be addressed NUM
as we mentioned e arlier the disaanbiguation task benelits from both non ontexl and context l sed methods
we present two types of performance statistics on the testing set without cumulative error testing without ce and with cumulative error testing with ce
in generm we base our context based predictions for disambiguation on turn taking information the stage of negotiation and the speakers cmendar information
both combination methods the genetic programming approach and the neural net approach were trained on a set of NUM spanish scheduling dialogues
we only say that the standpoint we chose is simple and umchine tractable md works well lbr our purpose
this subsection describes the most important part of our method the pattern matchers and heuristics on unregistered word treatment
an augmented bottom up cfg parser chooses the minimum cost tree for the given word sequence
the system is freely available on the internet
the solution adopted was to use parameterised modularisation
the teaching tool allows users to work step by step through derivations of semantic representations and to compare the properties of various semantic formalisms such as intensional logic drt and situation semantics
syntax semantics mai plng rule to rule
for example clicking on app NUM NUM
our current plans a re to test t l system with a wide lass of users to discover areas requiring extension or modification
we would especially like to thank ih bin ooper mass me poe sio and steven lhdman for eontril utions to the code
the result of the parameterised approach is a system which provides several thousand possible valid combinations of semantic tbrmalism grammar reducer etc using a small amount of code
l his work would not have been i ossible without the encouragement and support of the other men hers of the l ra as project
it specifies the sgml tags i.e. pubdate whether the tag is required and any associated field names triggers within a document which must be used to extract the sgml tag value as well as corresponding format validation and normalization rules
the gui displays NUM the original document NUM the current version of the sgml template for that document NUM the linkages between the two NUM diagnostic information associated with the document and NUM suggestions for fixing the problem tag s
the om s main capabilities include creation of the sgml tagged version of the document null performing special processing when required providing an interface for passing the tagged document to the main rose catcher providing a gui which will allow the rose data administrator to view the original document the final tagged document and the linkages between the two for any document stored in local storage
note how easy it is to find synonyms for the epithet miser and how hard to find synonyms for spendthrift
although segmentation errors may exist this corpus is NUM NUM times larger than ntu balanced corpus so that we can get more reliable word association pairs
from the viewpoint of personal nmne identification it is easy to regard li r j ff as a chinese personal name
if the word preceding the possible keyword is a place name or a personal name then the word forms the name part of an organization
for each candidate which has only two characters we compute the frequency of these two characters to see if it is larger than a threshold
this formula has a drawback i.e. it does not consider the probability of a character to be the other words rather than a surname
in sumnmry if a title appears a special bonus is given to the candidate NUM NUM NUM NUM clue NUM mutual hfformation chinese personal names are not always composed of single characters
table NUM single attribute table for
in the case of the infinitival and related constructions no such relation holds the noun modified by the adjective at the surface level
until many more adjectives have been investigated we avoid introducing overly specific features whose range applicability and definition may be unclear
NUM statements about morle xical entries
structural phenomena like vp ellipsis coordination or topicalization can however still be accounted for ill terms of an apl roi riate emt e hling at c structure cf
there are a lso major reasons however tbr nol adopting this analysis NUM linguistic adequacy NUM unmotivated structural complexity NUM lion parallel analyses for predicationally equivalent sentences
the architecture of lfg assumed here is the traditional architecture described in bresnan NUM NUM as well as the newer advances within lfg dalrynq te et al NUM
the auxiliaries wird will and haben have now only contribute information as to tit overall tense lint do not subcategorize for complements
NUM le lcb ondu teur aura tourn6 le levier the driver will have turned the lever the driver will have turned the lever
the lfg based imph menration t resented here avoids unnessary structural eonq lexity in the representation of auxiliaries by challenging the traditional analysis of auxiliaries as raising verbs
the linguistic studies used for the typologies of col verbs and spatial prepositions have been realized on verbs considered without any adjuncts in their atemporal form and independently of any context on the one hand and on prepositions considered independently of any context on the other
they also take into account the syntactic structure of the sentence we have supposed an x bar syntax with a vp internal subject though this is not essential and the links which exist at the level of diseors between this sentence and the previous and following sentences of the text
this lref implicitly suggested by each col verb can be either the initial location as with partirto leave sortir t0 go out or the path passer traverser to pass through or the final location arriver to arrive entrer to enter of the motion
NUM an external zone of contact required by verbs like atterir for which the final location is neither the lref in contrast with entrer or the outside or proximity outside of the lref in contrast with s approcher o approach
these concepts are NUM a limit of proximity distinguishing an outside of proximity from a far away outside indeed if sortir simply requires to go out of the lref partir in addition forces the mobile to go sufficiently far away from that lref
we claim that natural languages can be considered as a trace of these representations in which it is possible with systematic and detailled linguistic studies to light up the way spatiotemporal properties are represented and on which basic concepts these representations lie
we propose to refine this structure with two new concepts required to distinguish minimal pairs like sortir to go out partir to leave and entr r to ent r atterir to land
the pcfg is equivalent in two senses first it generates the same strings with the same probabilities second using an isomorphism defined below it generates the same trees with the same probabilities although one must sum over several pcfg trees for each stsg tree
aj sc NUM aj a bc l a aj bkc bh aj a bkc bk a aj bci ci aj a bcz cja aj b ci bkcl aj a bkcl bkcl a NUM
experiments with this system revealed three major problems which our current research is addressing
brighton bn1 9qh uk pembroke street cambridge cb2 3qg uk john
however there is a large decrease in accuracy with no training data i.e.
the parser was set up to return only the highest ranked analysis for each sentence
we described an implemented system and evaluated its performance along several different dimensions
figure NUM geig metrics for held back sentences training on varying amounts of data
as the grammar was developed solely with reference to susanne coverage of sec is quite robust
table NUM geig evaluation metrics for test set of NUM held back sentences against the manually disambigated
table NUM geig evaluation metrics for test set of NUM held back sentences against susanne bracketings
unsupervised vs supervised models of syntactic learning several corpus based methods for syntactic ambiguity resolution have been recently presented in the literature
figure NUM d plots the coverage i.e. the number of decided cases over the total number of possible decisions
the ssa grammar in ld has about NUM dcg rules and it generates NUM NUM esl s from the whole corpus
each esl in a collision set has its own mcp1 value that has been globally derived from the corpus
in the test phase eslls are classified as locally correct and wrong according to their relative values of mcpi
the average frequency of right generalized esl s is now NUM NUM in the enea and NUM NUM in the ld
some methods do not require manually validated pp attachments but word collocations are collected from large sets of noise free data
probabilistic backed off or loglinear models rely entirely on noise free data that is correct parse trees or bracketed structures
and one good way for doing so is by measuring ourselves with the full complexities of language
the corpus selected for experimenting the incremental technique is the ld the size of the corpus is about NUM NUM words
it can refer to motion along a line that lies within a medium but it is neutral to the shape of this line as shown by the fact that it can occur equally well in sentences referring to paths of greatly different contours i zig zagged through the woods and i circled through the woods
thus while noun inflections in various languages mark such number distinctions as singular dual plural and paucal no i known inflections indicate such notions as even odd dozen or countable
if however the open class forms are changed as in the sentence a machine stamped the envelopes the structural delineations of the referent scene and of the speech context are the same as before but the new content situates us in say an office building with office equipment postal routines and stationery
now if either of these verbs were to grammaticize so as to become a closed class form such as an auxiliary while retaining its current core meaning it is clear that hate would not undergo this process but that keep might well do so much as the auxiliary form used to has already done
as for overlap i have to date identified eight structural characteristics apparently shared by these two cognitive systems
comparably it is discontinuity neutral as seen in in the bell jar and in the birdcage
in the other direction a closed class reference in certain respects is geometrically less abstract than in mathematical topology
accordingly no process of grammaticization will terminate with a closed class form expressing the concept of hate
there are principles by which closed class forms divide a referent scene and its speech context into entities on the one hand and on the other hand the processes that the entities execute and relations that they bear to each other
but it is magnitudeneutral as shown by the fact that it appears equally well in sentences whose referent scenes different greatly in magnitude the ant crawled across my palm and the bus drove across the country
first an initial set of linguistic features that could be useful for predicting pp attachment was determined
figure NUM results for two attachment sites figure NUM three attachment sites right associa tion and lexical association
on the other hand there may well be good engineering reasons to treat linguistically homogeneous words as belonging to different classes
when we do we get a perplexity of NUM NUM a NUM NUM improvement on standard interpolation which scores NUM NUM
for example if many nouns now belong to class NUM and many verbs to class NUM later subclassifications will not influence the m1 t value
therefore each transformation performed by the algorithm is not irreversible within a level which should allow the algorithm to explore a larger space of possible word classifications
this second calculation is repeated for each word in the vocabulary and we keep a record of the transformation which leads to the highest m t
for example light can occur as a verb and as a noun whereas our classification system currently forces it to reside in a single location
we have built some of these models and carried out experiments that show a NUM drop in test set perplexity compared to a standard interpolated trigram language model
mcmahon and smith improving statistical language models algorithm the next NUM NUM are clustered by our auxiliary algorithm and the remaining NUM NUM words are added to the tree randomly
each node has three kinds of attributes head mod rel and accum cost head has the lexical head of the subtree under np i as its value
but its disadvantage is its huge number of states
computation would not have been feasible without this reduction
figure NUM shows the increase in perplexity during merging
there is no change during the first NUM NUM merges
this is not true in most of the cases
null there are several methods to estimate model parameters
this part consists of NUM NUM words
what happens to the test part
the baseline is also a well known heuristic method to analyze japanese noun phrases combined with c such as a c b c
we introduce an operation called build contraction that takes an elementary tree places a subset from its second projection into a contraction set and assigns the difference of the set in the second projection of the original elementary trec and the contraction set to the second projection of the new elementary tree
if we extend the notion of contraction in the conjoin operation together with the operation of lexical insertion we have the following observations the two trees to be used by the conjoin operation are no longer strictly lexicalized as the label associated with the diamond mark is a preterminal
for example applying conjoin to the trees conj and a eats lcb NUM rcb and c drinks lcb l rcb gives us tile derivation tree and derived structure for the constituent in NUM shown in fig NUM
one of the effects of contraction is that the notion of a derivation tree for the 12fag formalism has to be extended to an acyclic derivation graph NUM simultaneous substitution or adjunction modifies a derivation tree into a graph as can be seen in fig NUM
some of these rules are domain independent but for any given domain we typically implement a number of highpriority domain dependent specializations of the general rules
the metarule for nominalizations as in the company s resumption of talks must appear in the complex phrase recognizer and has the form
tions for an application in which we had to recognize the products made by companies we would want a pattern that would recognize gm manufactures cars
the terminal symbols in the grammar for a phase correspond to the objects produced by the previous phase and their attributes can be accessed and checked
the following is one rule in a grammar for the clause level event recognizer for the labor negotiations domain used in the dry run of muc NUM in april NUM
the rules have a syntactic part expressing the pattern in the form of a regular expression with attribute and other constraints permitted on the terminal symbols
it also recognizes verb groups or verbs together with their auxilliaries and embedded adverbs certain predicate complement constructions are also analyzed as verb groups
indices are associated with each of the arguments of the head s predication and these can be used in the semantics specified for particular pattern
it enabled the easy definition of patterns and their associated semantics and made it possible for a larger set of users to define the patterns
in this paper we present an incremental class based unsupervised method to reduce syntactic ambiguity
transformation steps from the definitions into conceptual graphs then we elaborate on the integration process and finally we close with a discussion
this interaction will be set within a lmrtieular domain and the trigger word should be a key word of the domain to represent
iustead of finding a concept that subsulnes two concepts we will try finding a common subgraph that subsumes the graph representation of both concepts
in the second experiment we measure the recurrence of ambiguous patterns
NUM verification method demonstration and inspection
NUM verification method demonstration and inspection
the requirements herein are derived from several government agencies
for example igure NUM shows a translation from a d lcb s to a formula in predicate logic
this top down modularisation is then followed by some bottom up modularisation in the sense of supplying general utilities which each of the larger modules can use
the final part of the paper describes the grapher which was designed as a stand alone tool which can be used by various applications
menu options include the possibility of canceling intensional operators performing lmnbda reduction applying meaning postulates and rs merging
figure NUM i ranslating i i lcb t to predicate logic li igure NUM initial representation of anna laughs
in this section we show how a user can interactively explore the step by step construction of a semantic representation out of a syntax tree
apart from the routines concerned directly with computational semantics there are also routines designed to aid application developers who want to provide a graphical output tbr semantic representations
the system provides a teaching tool a stand alone extendible grapher and a library of algorithms together with test suites
for example the user can first form an application term aa hmghs a anna and then reduce this at the next step
for example in parsing a parameterised level chooses how to annotate nodes so that the syntax trees only have the relevant inibrmation for the chosen syntax semantics strategy
first segment initial utterances differ from medial and fi8we present results only for text and speech labelings results for text alone were quite similar
with a view toward automatically segmenting a spoken discourse we would like to directly classify phrases of all three discourse categories
the portion of the corpus we report on consists of NUM and NUM intermediate phrases for read and spontaneous speech respectively
the speakers were provided with various maps and could write notes to themselves as well as trace routes on the maps
this coefficient is defined as po p NUM ps where po represents the observed agreement and pe represents the expected agreement
thus application of this somewhat stricter reliability metric confirms that the availability of speech critically influences how listeners perceive discourse structure
it has long been assumed in computational linguistics that discourse structure plays an important role in natural language understanding tasks such as identifying speaker intentions and resolving anaphoric reference
the use of the component nouns of a compound noun as index terms on the other hand may improve the recall performance but can decrease the precision
i igure NUM ilhistrates the different distributions t terms over tile same doclilnellt set suggesting the usefulness of the distributions as the representation of tim terms
p g ig p n l iln p ciici NUM is the probability of tl e function cat
ex tcrtns should be less ilnil orni a n sltare simihu eoii exts with other tertils in a docttlileut
bec llse on ell coiit otlii i jioiiiis e r ryit g n lore sl eeili information become t
lit inany cases there are there than one de olllposition but only a few of thenl are sensible with respect to tim conte xt of the document
the proposed method to evaluate the coinponents of compound nouns is unique in that it delines and uses term representation which explains the superiority of the method to other methods
because of the difficulty in getting new data it was decided to select the new data first and then select topics that matched the data
a detection component is an example of a csc
the topics are used to create a set of queries the actual input to the retrieval system which are then used against the training documents
their basic term weighting formula underwent a major change between trec NUM and trec NUM that combined the trec NUM inquery weighting with the okapi city university weighting
the list below identifies typical analytical methods that may be encountered in any tipster application
therefore we provide an arbitrary number of unification workers running in parallel which are fed unification tasks from the top of an agenda sorted by scores
is lated local controh the control strategies used within the module disregarding any interactions beyond initial input of data and final output of solutions
this kind of communication architecture is hardly new and eonly onts us directly with a large number of unresolved issues in distributed problem solving ef
w key is the name of the lexical entry of w and w score is the acoustic score of w for the frames spanned given by a corresponding hmm acoustic word model
our specific research areas are the construction of parsers for spontaneous speech investigations in the parallelization of parsing and to contribute to the development of a flexible communication architecture with distributed control
we can not measure something like a tree recognition rate or rule accuracy because there is no treebank for our grammar
in contrast to that for semantic analysis a second unpacked chart is used whose edges are provided by an unpacker module which is the interface between the two analysis levels
the chart must be kept as a single data structure in a shared memory processor where concurrent reads are possible and only concurrent writes have to be serialized with synchronisation primitives
for the implementation of the interaction of syntax and semantics we proceed as follows a new turn based ug has been written for which a context sensitive stochastic traiuing is being performed
but at least some cases of ellipsis resolution seem to require reference to this structure
syn loc subcatlh dtrs head dtr i hon
in the second column of results the ordering of the states was simply the order of their creation as the sample transductions were read as input
it takes extra effort to make it possible for a teacher to do so as opposed to a programmer
the core of the vs patterns in NUM is represented by s a lii is whose translation in ia e va ries from rise to come in go tip climb or more pre eisely by the set of both imterent and relational features associa ted
we believe that the language systematic interactions between lexicon syntax and semantics carried out by illico are crucial for the treatment of language and cognitive disorders i.e. can help users to improve their language and cognitive skills
i or training and testing of the tagger we have randomly l icked articles from a large 274mb h norte mexican newspaper corl us and sel arated tlwm into the training and test s ts
fhe best accuracy of the spanish xerox tag ger was NUM NUM for the reduced tag set NUM tags lit can be a part of a last name as it van mahler but also is an inflected form of it
this rule was learned h te in tile learning process when most i su1kjonj pairs had already been reduced llowever as olle call see frolll t le coiltext of the rule it will apply in a large number of eases in a text
for these two experiments he learner was set to have a tight restriction on using context for learning i c the freedom parameter was set to NUM and a loose restriction on context tbr applying the learned rules i.e. l lagfrecdom NUM
one final wrinkle must be noted
it should be trained accordingly that is only on examples where the words have the same part of speech
otherwise the shorter sequence will usually be preferred as shorter sequences tend to have higher probabilities than longer ones
such a rule seems quite unnatural phonologically and makes for an odd spe style context sensitive rewrite rule
as mentioned in section NUM smaller transducers significantly improve the general accuracy of the learning algorithm
in addition no generalizations are made about segments in similar contexts or about long distance dependencies
recent work in the machine learning of phonology includes algorithms for learning both segmental and nonsegmental information
the transducer obtained for the flapping rule after pruning decision trees is shown in figure NUM
our goal in this paper has been to explore the role of prior knowledge in phonological learning
this problem can be solved by pruning the decision trees at each state of the machine
this process continues at the fringe of the decision tree until no more pruning is possible
although it had not been optimized word shape token generation was NUM to NUM times faster than ocr
second we identify the character cells and count the number of connected components in each character cell
in such cases a separate state is necessary for each phone to which the rule applies
the lack of natural language bias causes the transducer to miss correct generalizations and learn incorrect transductions
we would then pick as our answer the wi with the highest p wii t
this section describes the feature extraction procedure
the se cm has five major goals with respect to the rfc process for the tipster architecture
in such cases the se cm will keep the cawg informed of the issue under discussion
the results of any actions taken by the ccb are reported to the architecture committee
additional individuals are invited to participate as appropriate when their technical expertise is required
section NUM NUM has a glossary of all acronyms and abbreviations used in this cm plan
this will allow for the orderly upgrading of installed systems as technology improves in the future
discrepancies will originate from the erb and will primarily be presented by the developing contractor s representatives
the cmm acts as the point of contact for receiving and processing information from external organizations
the structure roles and responsibilities of the configuration management organization are defined in the paragraphs below
at these reviews any discrepancy or deviation from the tipster architecture must be documented and justified explained
then given semantic feature information about the words filling those grammatical functions gfs and information about the possible argument structures for the verb in the sentence and the semantic feature restrictions on these arguments it is possible to find the argument structures appropriate to the input sentence
poura subj agent volitional obj recipient voutional io patient liquid pour2 subj agent volitional obj patient l quid ppo recipient volitional given the semantic type restrictions and the gfs pour1 describes 2a and pourz 2b
the second and third of these three functions can be provided in two steps NUM classifying each alternation for a particular verb according to the type of semantic mapping allowed for the verb and its arguments and NUM either identifying the verb class that has the given pattern of classified alternations or using the pattern to form the definition of a new verb class
and where q is a threshold less than NUM
i inajly further improvements a re sketched a nd other possible a pplic tions of tlm system envisaged
build will now process the tree marked with
for use in the corresponding model
only a s op1 osed i mready exora l e ores
figure NUM shows the result after the second pass
t he retunmd while lie was talking
with a comma figure NUM average pause length diagram
ldg assumes japanese function words have moda ity or proposidonal attitudes md suggest global tructures of japanese hmg sentences in cooperation with modality within mxiliary verbs
jr jap n se eolnl lex or compound selltellces sll rcb rdinate clauses have several dependency levels relative to the main clause
see figure NUM and table NUM
fltilction words located at tile end of the clauses
i answered while i was smiling as lie asked
i answered as lie asked while tie was smiling
input sentences are first analyzed morphologically
comma and words without a comma
anything that the flat constituent structure was originally needed for can be done with this list the extra levels introduced by the recursion being ignored
the various techniques described here are often limited in their applicability applying to only a subset of the problems that one would like to solve
however this route involves a research program of uncertain length and outcome given the known complexity properties of many of the richer descriptive devices
macro transitive verb stem v lcb lex stem subcat np lcb rcb rcb
a better treatment can be obtained by using the fact that it is the np that determines the p semantics and encoding this dependency directly
for example we might want a feature whose value was in the product of the set of letters of the alphabet and positive whole numbers
he gives an encoding of boolean combinations of feature values originally attributed to colmerauer in such a way that satisfiability is checked via unification
the value of the feature next is a variable over the tail of the daughters list in a way reminiscent of many treatments of subcategorisation
is valid and means that the value on c of f which may be only partly specified will be the same on category a
but a rule like this does not make clear what is intended for the values of any features on an adjp not mentioned on the rule
a short query is derived using only one section of a trec NUM topic namely the description field
these include generating simple collocations statistically validated n grams part of speech tagged sequences syntactic structures and even semantic concepts
and unlike the bigram parser the maximum entropy parser can not use head word information besides flat chunks in the right context
the main thrust of this project is to use natural language processing techniques to enhance the effectiveness of full text document retrieval
can we take advantage of interacting lin null guistic constraints from each level for the development of structured lexicons
lastly this paper clearly demonstrates that schemes for reranking the top NUM parses deserve research effort since they could yield vastly better accuracy results
what existing resources should we be using and what aids do we have to transform these resources into appropriate representations
we will also address related questions including how much of what we can tag is context dependent
each unit will be accompanied by examples to make clear the senses we are talking about and by a short summary of the potential tagging problems i see it posing
the similarity between a sentence s and a title t of the text in which it occurs is given by
ollowing i he l riucil h
model NUM NUM english fren h aligmne nt nm lel
table NUM pile content of training corpus en
a timm part of speech tagger is used to tag words beibre aligmnents
this paper snggests a method to align korean english parallel corpus
terms l erger al NUM NUM
the content of training corpus is summarized in table NUM
jllll tllly ommitlct ollcge ht NUM cabinet business board associ tion association airline table NUM siirflk
abstraction of a syuset can be done by divid ing whole synsets into the appropriate number of synset groups and determining a representative of each group to which each member is abstracted
we lav chosel to alltolila tieally gel erate lcb eatures listril utionally by analyzing a large eorl us
l he clistinetiw feature of our apl roach is the rise of the ontology i o e rive features rather than assuming atomic sets of rmtures
path length methods in contrast assert sire c1 c2 sire c2 c3 since the number of links between the concepts is quite different
we leseribe lids geueration process below but we will irst ttlrtl to the e qthlgti tl of similarity based on feat ural analysis
fsm in eontrasl uses the i ol o ogy to derive leseril lions whose omparisotl yields a similarity measure
it would be possible to utilize lexical information in word shape token representation for reducing errors
first we carefully chose ten topic categories with strong boundaries
note that the word based character bigram model is different from the sentence based character bigram model
this resulted in NUM word types being registered in the dictionary of the word segmenter
we redistribute the probability mass of low count sequences to unseen sequences
the string c25 is not registered in the dictionary
let the input japanese character sequence of length n be c cl c2
we will call a pcfg subderivation isomorphic to a stsg tree if the subderivation begins with an external non terminal uses internal non terminals for intermediate steps and ends with external non terminals
a character shape code is a machine readable code which represents a set of graphically similar characters
notice that this parse tree can not even be produced by the grammar each of its constituents is good but it is not necessarily good when considered as a full tree
first for each potential constituent where a constituent is a non terminal a start position and an end position find the probability that that constituent is in the parse
however while bod provided us with his data he did not provide us with the split into test and training that he used as before we used ten random splits
an analysis of bod s data shows that at least some of the difference in performance between his results and ours must be due to an extraordinarily fortuitous choice of test data
thus when using the monte carlo algorithm one is left with the uncomfortable choice of exponentially decreasing the probability of finding the most probable parse or exponentially increasing the runtime
notice that the minimum is usually negative and the maximum is usually positive meaning that on some tests dop did worse than pereira and schabes and on some it did better
two types of simplifications are applied when possible to a given tree NUM NUM plitting each sub tree immediately dominated by the root is extracted and possibly further simplified
s he sub graphs hi which l here is t NUM alh i cl ween every pair of lisl hicl lio 1cs
for this range of corpora a pure symbolic approach which recycles and simplifies analyses produced by robust parsers in order to classify words offers a viable alternative to statistical methods
the prosodic features pitch range pause duration and number of low boundary tones are claimed to increase continuously with boundary strength the proportion of subjects identifying a boundary
we ask subjects to segment discourse using a nonlinguistic criterion in order to avoid circularity when we later investigate the correlation of linguistic devices with segments derived from the segmentation task results
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
the first three phrases in figure NUM correspond directly to three consecutive ficus and each ficu has an np coreferring with an np in the next likewise the 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 tlle major improvement coming from precision
they include the process itself generally surfacing as the verb and its associated participants surfacing as verb arguments
a initially the program will involve only a handful of inmates
the following is a sample text from the penn treebank
predicate argument relations in the original text for the summary
aa most county jail inmates did not commit violent crimes
i use centering theory to roughly segment the text as described in the next section
a the springfield jail it built for NUM people now houses more than NUM
summarization can be seen as a massive application of aggregation algorithms
however most good authors restate phrases rather simply repeating them
thus the most frequent cb can be used to select the important segments in the text
that is why i propose we search for repeated lfs rather than repeated words or phrases
this talk will argue that this progress in parsing unlike the earlier progress in speech recognition depends crucially on the combination of explicit linguistic representation and statistical estimation
tiffs progress in parsing accuracy continues within the last month several new parsers have been reported two developed within the speaker s group at the university of pennsylvania which show distinct improvement over the best parsing results of even a year ago
in model NUM there are four representatives and three samples and some of the representatives saw some of the samples
it is clear from NUM and NUM that this framework undergenerates in comparison with hobbs and shieber s
definition NUM candidate set a variables candidate set is the set of individuals from the model which satisfy the variables restriction
for instance partition 12b gives at least the three sentences 13bi ii iii
NUM ever representative of a company saw both samples of course different scoping and partitioning choices may have generated different quantifiers
processing 22b is straightforward and consists of returning the value lcb sl s2 rcb the set of all samples
this follows from the observation that exactly n meant the same as at least n a at most n
this entails checking the complement of the dependency function to make sure that the quantifier a fails to be consistent with the variable s
similarly for NUM in 12a the appropriate call is q inc NUM NUM qs
o a different collection of quantifiers is checked the monotone decreasing ones o the focus maximum is input rather than the focus minimum
the dots denote the probability mass mp f in the full text n NUM NUM of the words with frequency f in the sample
however the probability mass of the types that do not appear among the first NUM NUM tokens m NUM is much smaller NUM NUM
for both texts no overall topical discourse structure is at issue so that we can obtain a better view of the effects of intra textual cohesion by itself
assume that the trouw data used in the previous section constitute a population of n NUM NUM word tokens from which we sample the first n NUM NUM NUM words
having established that e v n underestimates v n when extrapolating the question is how well the good turing estimates perform
his appearance in the two text slices strengthens the intertextual cohesion of the whole novel but it is only the intra textual cohesion of slice NUM that is raised
interestingly the use of underdispersed words in moby dick is to some extent correlated with the frequency of the word ahab with respect to both types and tokens
considered together this may explain why at the very end of the novel the expected vocabulary slightly underestimates the observed vocabulary size as shown in figure NUM
thus key words are slightly underrepresented in the first part of the novel allowing the largest divergence between the expected and observed vocabulary size to emerge there
a proof of this fact is presented in appendix a
this approach is discussed in schabes and waters 1993c
our procedure relies on the following four lemmas
the ltig procedure converts them into right auxiliary trees
the ltig of figure NUM converted into a cfg
this process must terminate because g is finitely ambiguous
third the gnf procedure changes the trees produced
the ltigs created were smaller than the original cfgs
consider the al rooted initial trees that do not satisfy ft
the parsing accuracy roughly NUM precision and NUM recall surpasses the best previously published results on the wall st journal domain
they devised a dynamic programming method based on the number of corresponding words in a hand crafted bilingual dictionary
in order to find new anchors we combine these statistical word correspondences with the word correspondences in a bilingual dictionary
in order to avoid this we employ t score defined below where m is the number of japanese sentences
NUM w na does not occur in any other english sentence that is a possible translation of jsentencei
let thigh tlow ihigh and izow be two thresholds for t score and two thresholds for mutual information respectively
by using the matching technique we can make the most of the information compiled in bilingual dictionaries
however in many cases there are a few word translations in a set of corresponding sentences
i aekages 17cl r seih very major lcvelolmmllt effort s ii ssi i lcb i lcb u
fhen these keys can be used to search in the second index one index file tbr each corpus for occurrences of the word denoted by these keys
when the average search time grew to severed seconds on a NUM milts unix server it became apparent that some sort of indexing wa s needed
cot l i eke the form of dist lnying iiu ther unknown wor ts in eilj er the dictionary or the examl h s wit lows
although development of the l rototype lcb i ssi l lcb lcb u l is still ongoing these first results look very promising
thus if e.g. corpus search turns up examples with fill her llllkllowll wortls these may l e suhmitted NUM o il ssi ji ibki
for high frequency words one can obtain fairly reliable estimates of the lexical priors by tagging a corpus that gives a good coverage to words of various ranges
the results of the analyses presented in this paper are of potential importance in various applications that require lexical disambiguation and where an estimate of lexical priors is required
in the udb corpus there are NUM NUM infinitive tokens and NUM NUM finite plural tokens so the mle for aanlokken being an infinitive would be NUM NUM
of course this estimate is heavily influenced by the highest frequency words in en as these words contribute many tokens to n en
hence a form such as hebben have is much more likely to be a plural finite form than it is to be an infinitive
for example the written form goroda in russian cyrillic ropo a may either be the nominative plural or the genitive singular of city
no inf and no pl denote the number of tokens in the held out portion that have not been observed in the training set
n inf and n pl are the number of tokens of the infinitives and finite plurals respectively in the training set
NUM among the low frequency verbs including accentuate bottle and incense the predominate types are those in which the past participle usage is preferred
however implementing this flat structure in the gde was found to be highly problematic
with each entry we shall associate token frequencies with the various fegs for each word sense in order to assist nlp programs in picking likely interpretations
we recognize such a naming scheme is inadequate for a large annotation project and certainly the representation of feg structures will have to be more powerful
measure NUM looks at almost exactly the same type of problem as measure NUM the presence or absence of some kind of boundary
a frame element group feg is a list of the fes from a given frame which occur in a phrase or sentence headed by a given word
in contrast to cases where frame elements are missing implied but unmentioned optional etc some examples require that we explicitly recognize i.e.
computational linguistics volume NUM number NUM from the economic and social research council u k to the universities of edinburgh and glasgow
so far we have shown that all four of these measures produce figures that are at best uninterpretable and at worst misleading
now researchers are beginning to require evidence that people besides the authors themselves can understand and reliably make the judgments underlying the research
for example they use this measure for determining the reliability of the existence and category of pitch accents phrase accents and boundary tones
chance of the second coder also choosing that category and NUM of the time the first coder chooses the second category with a NUM
these two responses must be classified in separate categories a better police training general and b types of self defense safety techniques respectively
in future experiments we plan to score an independent set of response data from the same item using the augmented lexicon to test the generalizability of our prototype
the results of this case study illustrate that it is necessary to analyze content of responses based on the mapping between domain specific concepts and the syntactic structure of a response
our hypothesis is that this type of representation would denote the content of a response based on its lexical meanings and their relationship to the syntactic structure of the response
the difference between the previous system and our current prototype is that in the previous system concepts were not represented with regard to structure and the lexicon was domain independent
based on the syntactic parses of these responses and the lexicon a small concept grammar was manually built for each category which characterized responses by concepts and relevant structural information
another consideration in the development of a scoring system is that the data sets that are available to us are relatively small and the responses in these data sets lack lexico syntactic patterning
hnc has also developed a preliminary prototype of an english spanish matchplus system
more complete performance results can be generated when additional funding becomes available
the arpa and intelligence community sponsored tipster program has risen to this challenge
all these applications have demonstrated the tremendous versatility of the context vector technique
the basic context vector methodology has been implemented in a system called matchplus which serves as a standalone information retrieval application as well as the technological core upon which additional context vector applications have been built
convectis is currently being used by datatimes a large commercial information provider as the core technology for routing information to customers based on customer specified interest profiles
in order to reduce the severity of this problem hnc has developed a one step learning law that approximates the behavior of the original learning law at a fraction of the computational cost
context vectors allow information to be represented in a universal meaning space opening up the possibility that text images and information in other media can be represented in a unified fashion
one problem with the matchplus system has been the large computation requirements of the learning law
preliminary performance results with this learning law have been quite encouraging
durchschlagend a this represents any occurrence of the word with the specified morphological properties
this has to be taken into account for their description and their recognition in texts
the formalism provides a set of re operators to combine the descriptions of single words
structural flexibility this includes phenomena like passivization topicalization scrambling raising constructions etc
currently the system determines the word s part of speech and whether the word is part of an mwl
for instance in g durchschlagender erfolg sweeping success lit
pedals without loosing its idiomatic meaning but not by la tronche lit
as we have shown above this simplifies considerably the description of the different patterns of variation oecuring in mwls
these approaches to our knowledge can not satisfactorily represent lexical variants nor the restricted flexibility and modifiability of mwls
NUM NUM NUM NUM verification method demonstration and inspection
the application can provide a custom browsing interface to display annotated documents to the user
detection criteria may be stated as boolean keyword criteria and negative operators to exclude documents
processing statistics shall include various counts related to document processing and progress status during runs
the detection component shall be compatible with other components and modules of the tipster architecture
figure NUM time to completion by task
the generator is a simple template based system
in this case a more effective approach is to learn features that characterize the different contexts in which each word tends to occur
each of these elementary trees has root s with children ck NUM NUM k rn in the same order as these appear in the formula of ins subsequently the children of ck are the non terminals that correspond to its three disjuncts dkl dk2 and dk3
an instance ins of 3sat can be stated as follows given an arbitrary a boolean formula in NUM conjunctive normal form 3cnf over in the sequel ins ins s formula and its symbols refer to this particular instance of 3sat
NUM since we want to be able to know whether a parse is generated by a second type derivation only by looking at the probability of the parse the probability of a second type derivation must be distinguishable from first type derivations
moreover if a parse is generated by more than one derivation of the second type we do not want the sum of the probabilities of these derivations to be mistaken for one or more first type derivation s
a sentence which corresponds to a successful assigmnent must be generated by n derivations of the first type and at least one derivation of the second type this is because the sentence w 3m fulfills n consistency requirements one per boolean variable and has at least one w as wak l wak NUM or w3k NUM for all NUM k m
for the mps problem of scfgs for example one searches for the sentence which maximizes the sum of the probabilities of the parses that generate that sentence i.e. the probability of a parse is also a function of whether it generates the sentence at hand or not
table NUM overall performance of all methods baseline base trigrams system scores are given as percentages of correct predictions
NUM for the condition where the words have the same part of speech table NUM shows that bayes almost always does better than trigrams
the target languages in tltis case were spanish chinese and japanese
aside from ten hard wired names all names are found from first principles
we have applied this rule sequence approach to a variety of realistic tasks
some linguistic phenomena are easier to model with regular sets than context free grammars
the rule language is simple which makes it easy to write rules
this problem is patched by a subsequent namefinding rule namely the following
initial phrase labeling the initial labeling process seeds the phrase finder with candidate phrases
two places to the right of the phrase test first resp
figure NUM the initial scenario figure NUM the proposed route figure NUM the corrected route
the following are examples of speech recognition sr errors that occurred in the sample dialogue
the display supports a communication language that allows other modules to control the content of the display
specifically there is no need for goal management because the goal is fixed throughout the dialogue
this extension allows us to better handle the second example above replacing to try with detroit
to be honest with the current system it is hard to defend ourselves against this
this leaves us open to the criticism that we are not using the most sophisticated models available
the discourse processing is divided into reference resolution verbal reasoning problem solving and domain reasoning
since engine l originated in detroit it then decides to reinterpret the utterance as a correction
do we approach evidence combination for homograph distictions differently than for polysemy
the el tel system provides a set of user friendly educational play activities logic games or scenarios designed from communication and language training and learning exercises and designed to stimulate encourage and help users to employ common language to build up an everyday language dialogue in interaction with the system within a modular and multimedia context
the interface looks like here in the french version the user has begun a sentence echange le carrd noir avec le fond permute the black square with the circle and the system according to the contextual situation proposes the possible words to be selected blanc gris noir white gray black
these various activities are especially designed to help medical staffs in the evaluation and the rehabilitation of the users abilities to associate a word with a picture generalise a concept an idea work on space locating and logical constructions illustrated by pictures and by the movement of puzzle pieces on the screen according to the actions expressed by the users
we have shown why guided composition is especially relevant to the development of communication aids and how the use of illico makes it possible to develop software which can help users to improve their language and cognitive skills
the notorious ambiguity of nominal compounds remains a serious difficulty in obtaining head modifier pairs of highest accuracy
this allows him to compose rapidly with minimal cognitive load sentences which are always correct at each level this ensures also that the system never jams i e always understand what the user says
guided composition of sentences the kernel of the illico system carries out an interactive processing of natural language based on partial synthesis which checks the well formedness of the produced sentences as the user goes on composing the sentences
a first level proposes to point out a square simply by clicking on it then the system completes the sentence of the ttser with a noun phrase corresponding to the expression of the position designated that way
these sub indexes can be searched independently and the results can be merged meaningfully into a single ranking
an encouraging thing to note is the sharp increase of precision near the top of the ranking
phrase identification some examples sequencing patching and simplicity the hallmarks of brill s part of speech tagger are also characteristic of our phrase parser
this lexicon is then exploited to form the associations between short and long proper name forms through an extension to the rule repertory defined above
finally some rule actions actually introduce new phrases that embed the candidate mad its test context this allows one to build non recursive parse trees
in phrases like there is no alternative analysis or clever trick in order to get the correct syntax and interpretation alternative analysis or clever trick has to be treated as a conjunction of premodified nbars
in this plot we consider dutch word forms from the udb corpus ending in en
a speech recogniser based on hidden markov models in forced segmentation mode is used to outline phone boundaries within spoken logatoms
test set as shown in table NUM the mismatch between the training and test segmentations degrades performance by half a point from NUM NUM to NUM NUM
within a turn a participant may ramble on and on making the utterance too long for a speech recognizer to handle
any dysfluencies between the end of a previous sentence and beginning of the current one is considered part of the current sentence
when two independent clauses are connected by a conjunction they are divided with the conjunctions marked as described in ss2 NUM NUM
lcb f uh rcb i do lcb f uh rcb some lcb f uh rcb woodworking myself noise
however when we look at the data we see that participants often interrupt and talk over one another so even separating turns is not so simple
in this section we have described a simple technique of hypothesizing segmentations using an n gram language model trained on annotated data
l ecatlse il is inf ornial ive t o know which of the NUM lo0 rtlles is used nt a givou lr o no h and sitice the particular tlonlernlinal at egory associated wilh any lode of llw l ree is alwa ys
lot t ho tl enmnk l rlitor graphically displays tit l arse bt st br NUM he s lll ellc ill NUM ilic iso sci sii ivc i arse tr r wi dow figure NUM
a trecbanker s workstation a y b l ool is n mol if based x windows appli ati t which allows the treel anker to int era lcb t with the a i i lcb l glish gra mmar in order to produce he lltosl accllrate t reel alik
a body of documentation and lore was developed and frequently referred to concerning how all semantic and certain syntactic aspects of the tagset as well as various grammar rules are to be applied and interpreted
an sgml like markup language is used to caplure a variety of organizational level facts about each document such as list structure titles and captions and even more recondite events such as poem and image
on the far right the feature values of the vbar2 constituent indicating that the constituent is an auxiliary verb phrase bar level NUM containing a present tense verb phrase with noun semantics substance and verb semantics send
there is not a complete break m between texts which present meaning and frequency lists which do not
at the pdr control gate the following tipster application documentation is expected to be put in the tacad and under tipster cm control NUM tipster application design documentation
where n is the set of features used to describe all instances ni is the ith feature in the ordered set xn is the value of ni in the problem case yn is the value of ni in the training case and match a b is a function that returns NUM if a and b are equal and NUM otherwise
s2 i thank nike andl reebok who features s human v exists do name do up1 name prevl syntactic type comma class do t do up1 the antecedent involves two constituents
in the first approach we label the each constituent feature by its position relative to the relative pronoun
each case is described in terms of the normalized feature set which contains an average of NUM NUM features
one important problem that we have not addressed is how to select automatically the combination of linguistic biases that will achieve the indicate significance with respect to the original baseline result shown in boldface p NUM NUM p NUM NUM rm refers to the memory limit
to incorporate the restricted memory bias and the combined recency bias into the baseline case representation we NUM apply the right to left labeling NUM rank the features of the case according to the recency weighting and NUM keep the n features with the highest weights where n is the memory limit
furthermore the algorithm relies on an inductive bias that may be more appropriate to problems in natural language understanding than the information gain metric used in the c4 NUM decision tree system our linguistic bias approach to feature set selection automatically and explicitly encodes any of a predefined set of linguistic biases and cognitive processing limitations into a baseline instance representation
where n is the normalized feature set ni is the ith feature in n pn is the value of ni in the problem case tn is the value of ni in the training case and match a b is a function that returns NUM if a and b are equal and NUM otherwise
for a there is a complete agreement on the object s category while for b decisions are spread evenly over m categories
suppose that we asked 2m raters to assign two objects a and b to one of m categories and found results as in table NUM
work has been done however to find statistical differences among the systems see paper a statistical analysis of the trec NUM data by jean tague sutcliffe and james blustein in the trec NUM proceedings
also the trec NUM routing task was more difficult both because of the long federal register documents and because there was a mismatch of the testing data to the training data for the computer topics
the NUM participating groups ran the adhoc topics separately on each of the NUM subcollections merged the results and submitted these results along with a baseline run treating the subcollections as a single collection
first there was a desire to allow a wide range of query construction methods by keeping the topic the need statement distinct from the query the actual text submitted to the system
pircsl queens college cuny trec NUM ad hoc routing retrieval and filtering experiments using pircs by k l kwok and l gmnfeld used a spreading activation model on subdocuments NUM word chunks
in general the participating groups took two approaches NUM they used roughly the same techniques that they would have on the longer topics and NUM most of them tried some investigative manual experiments
additionally they used a shortened topic title description first sentence of narrative because it was more similar in length to the topics submitted by their users
this better ranking could have happened because of the many fewer terms that were used or could be caused by the use of passage retrieval in the city run
topics with many more relevant documents initially tend to have more new ones found and this has led to a greater emphasis on using topics with fewer relevant documents
a larger percentage increase is seen in the routing task due to fewer runs being pooled i.e. a higher percentage of documents is likely to be unique
splat is a first attempt to provide such a facility for the penman generation system in the form of an authoring tool for sentence plan language spl
notice that the semantically more important words have full annotations whereas the support words do not have lexical or conceptual information
the head concept can be modified by a number of keywords signauing the underlying grammar to generate different sentence patterns
ing tool is an authoring tool intended to facilitate the creation of sentence plan specifications for the penman natural language generation system
support for managing the construction of spl plans and accessing the necessary resources in a systematic and user friendly manner was almost completely lacking
not all words of a sentence will be annotated at each level as some annotation levels might not apply to a particular word
it contains a very large systemic functional grammar of english the nigel grammar and an extensive semantic ontology the upper model
as users develop their own spl plans they can add them to the sentence bank by choosing the annotation feature on the current template
currently sentence plans must be created by experts who are very knowledgeable about both linguistic theory and the characteristics of the particular generation system
figure NUM shows that the perfect scheme would achieve roughly NUM precision and recall which is a dramatic increase over the top NUM accuracy of NUM precision and NUM recall
this paper presents a statistical parser for natural language that obtains a parsing accuracy roughly NUM precision and NUM recall which surpasses the best previously published results on the wall st journal domain
in those parts the data is sparse and the high level generalization is questionable from a linguistic viewpoint
the shape of the decision tree learned by lasa NUM is sensitive to parameter a and the purity threshold
introducing a thesaurus or a semantic hierarchy in a case frame tree seems a sound way to ameliorate these two problems
amp quot retrieving records from a gigabyte of text on a
will rogers provided valuable assistance in installing updated versions of prise at nyu
in effect therefore we aim at obtaining a semantic representation
the context information x is reminiscent of that employed in the word translation application described earlier
this hotspot retrieval option is discussed in the next section
president bill clinton and president clinton with a degree of confidence
we can then use this representation to make predictions about the future behavior about the process
these efforts while varied in specifics all confront two essential tasks of statistical modeling
we then present a series of refinements to the method to make it practical to implement
in a the darkened line represents the search space we restrict attention to
the actual algorithms along with the appropriate mathematical framework are presented in the appendix
we therefore introduce a modification to the algorithm making it greedy but much more feasible
syntactic phrases extracted from ttp parse trees are head modifier pairs
however we are limiting our attention to a limited number of semantic domains and metaphorical extensions from the words in our wordlist that go far beyond our semantic fields will probably have to be set aside
there are also marked differences in performance between text types the decision tree method performs best on news reports and editorials but worst on col mug
the average number of subjects per text was NUM NUM NUM we find in table NUM however that there is only marginal agreement among subjects
note that NUM si NUM si NUM when there is total agreement among the raters for a given category j on the ith row
p e is defined as the sum of chance agreement for each category def NUM representing the overall rate of agreement by chance
in this paper we discuss an approach to automatic abstracting where an abstract is created by extracting sentences from a text that are indicative of its content
here NUM and p shows the hexidecimal value of each byte in t and p
consider the transducer in figure NUM reproduced below as figure NUM
this causes errors when transducing previously unseen words after training is complete
otherwise the machine outputs its input and moves to state NUM
these vowels were simply never seen at this position in the input
computational models of morphology have made use of a similar faithfulness bias
faithfulness underlying segments tend to be realized similarly on the surface
be pushed back down the tree as is done when merging states
this is demonstrated for the word importance in figures NUM and NUM
we will discuss the remaining NUM NUM error in section NUM below
in the next section we show how these arcs were further generalized
the japanese copula da z or desu c y means definition or speaker s judgment with confidence
however in order to select the most reliable struchtre of sentences we use another important discourse feature tile conjunctive pt rticles have i.e. modmity
constituent label in label set constituent or to join the previ ous one check yes no decide if current constituent is complete table NUM tree building procedures of parser
NUM NUM estimating the lexical priors for rare forms for a common form such as lopen walk a reasonable estimate of the lexical prior probabilities is the mle computed over all occurrences of this form
the senmntic classification of proper nouns is use fill in many applications
figure NUM NUM illustrates the design of adept
refer to appendix b for a processed document example
refer to appendix a for a sample document example
odbc adds an additional layer of flexibility to dm
the am manages the routine system administration of adept
some of these strings will be normalized
NUM NUM system adaptation manager sam
it includes NUM NUM words and NUM NUM characters
it includes NUM NUM words and NUM NUM characters
it plays the similar role of surnames
pronunciation is composed of syllables and tones
they are just artists stage names
some rare surnames are not real surnames
table NUM identification results of organization names
the categories of verbs are very typical
we conjecture that our construction can be extended so that given any tig as input an ltig generating the same trees could be produced
states NUM and NUM are removed a combined state NUM NUM is added and the probabilities are adjusted
when we include constraints derived from template NUM features we take our first step towards a context dependent model
at a first pass a measure of corpus homogeneity or similarity should be able to compare corpora of different sizes
this is precisely the approach we took in selecting our model p at each step in the above example
among the models p e c the maximum entropy philosophy dictates that we select the most uniform distribution
there is one path for each utterance in the corpus and each path is used by one utterance only
language independence given a perfectly translated text its connectivity profile should turn out the same as that of the original
the evaluation process is made up of the following steps which have to be executed for every sentence in the text
this suggests that redu ing evalnator dependence will lower all means which would defeat the purpose of this research
defining the scope of meaning relations we have established above that meaning relations hold between consecutive sentences this is however not self evident
an originm english texl was chosen a l hen a perfi ct but aligned lapanese
a fourth group was asked to use interface c but also to extract topic and comment before connecting the sentences d
for an n sentence text this results in a list of n NUM sentence to sentence relationships which we call the text s conneetivlty profile
for languages that do not recognize this class surrogates can be concocted for japanese a mixture of conjunctions and conjoining adverbs
small scale preliminary experiments on paper showed that in spite of the above refinements evaluator differences were still larger than seemed reasonable
a rather unsettling result however was that the most chosen sentence connector was identical across texts fur ahnost each of the sentence pairs
in order to increase the quality of human computer interaction call specialists turned to cl and ai
computer mediation has stopped being synonymous with language practice in a linguistically impoverished environment
developing network based learning environments calls for collaboration
this new language learning notion has already had impact on teaching practice
although interpersonal connections should remain at the core of any learning environment interactive instruments of linguistic inquiry individualization and assessment are important elements of such systems in the absence of teachers
student completions are returned to skryba and analyzed
networks connect not only learners and teachers but also resources
the currently available macintosh front end can easily be ported to other platforms including the web
computer technology however did not fulfill the expectations of language teachers
instrumental technologies vs teaching instruments a challenge for computational linguists
if no inconsistencies were found and an acceptably high percentage of the data had been accounted for then the descriptive category set might have appeared adequate
other organization names such as hamas had no obviously specifiable morphological features in common with large numbers of other names
fidelity and interactiveness are key issues tbr its
extensibility has been a key issue in nlp
it does however suggest that it will take significant work in design as well as implementation to create an nlp driven its for language that engages students and helps ttlem learn efficiently
actions available to both the student and the system include selecting and moving objects and making human figures walk turn point grasp and release objects and so on
proclaiming a future for nlp in its is not to deny benefits from other kinds of devices or the continuing importance of hmnan teachers tutors and other conversational partners
some high percentage of this NUM presumably could be accounted for by an adequate structural description of chinese surname pins given name patterns
two central themes in our current work are to identify the relevant partial structures of such trees and to determine their semantics such that for instance the text generation system can search the dialogue history and interpret what it finds in order to guide the choice of intonation for the system utterances
the primary tone selection in a tone group serves to realize a number of speech functional distinctions for instance depending on the tone contour selected the system output lisle wollen um f nfzehn uhr fahren you want to leave at NUM pm
detection criteria is the user statement of information need and may include selection statements short form free text queries boolean keyword queries or example documents document attribute is a characteristic of a document as represented by a single specific value or a set of values of the same type e.g. date received or authors
a linguistically based discourse model would be able to provide more information but in the context of an interactive conversational system in which there NUM are practical limits on how tong it can take to produce a response we believe that a full fledged discourse analysis system would be too slow
negotiation and speech function are the two ranks of the stratum of interpersonal semantics see figure NUM
unless the user volunteers the destination it must request this information from her NUM the user did not say where she wanted to travel hence the system initiated the exchange this is represented by the following path through the negotiationha network negotiation negotiating exchanging initiating
NUM would typically be used in an exchange as the realization of a responding to move in terms of the coa model NUM would be a possible realization of a request within an inform or within a request the speaker wants to make sure she has understood correctly
utterance f is also in response to a user inform but what makes this situation different from the response above is that here there is a mismatch between what the user wanted and what the system can offer user wanted NUM o clock while system can only offer NUM NUM
next we want to investigate the tone more closely before turning to the actual system networks in the komet grammar following NUM we assume five tones s the primary tones plus a number of so called secondary tones that are necessary for the description of german intonation contours
in terms of speech function a request is typically a question i.e. demanding information m the request question correlation in the kind of dialogue we are dealing with here constrains the choice of mood to interrogative or declarative e.g. NUM wohin mschten sie fahren
section NUM shows that the unaugmented ostia algorithm is unable to induce the correct transducer for the simple flapping rule of american english
when there is no move on the next input symbol from the present state a new branch is grown on the tree
we performed experiments using two versions of the algorithm varying the order in which the algorithm tries to merge pairs of states
the segment ordering used for the results in table NUM grouped similar segments together and performed better than a randomized segment ordering
this causes the machines to generalize in linguistically implausible ways i.e. producing output strings incorrectly bearing little relation to their input
when performing a transduction variables are interpreted as referring to a certain symbol in the input string with specific phonological features changed
the key provision here of course is the limit we are clearly not giving ostia sufficient training data
thus even the entire vocabulary of a language may be insuffi null final result of merging process on transducer from figure NUM
as the next step the output symbols are pushed forward as far as possible towards the root of the tree
an example of a push back operation and subsequent merger on a transducer for the words and and amp is shown in figure NUM
let p be a proper representation of u and q be a minimal underspecified part of p the scope of the ambiguity of underspecification exhibited by q is the fragment v represented by q
the proposed method is language independent does not use a dictionary and can be applied with only minimal linguistic knowledge thus reducing the cost of system development
this method works on a phoneme or syllable basis and can give adequate results in languages where the spelling is very similar to the pronunciation such as italian
the ptgc method can work as a stand alone module or in co operation with a look up module with a small to moderate size dictionary containing the most common words of the language
in this formulation the sequence of phonemes produced by the system can be seen as the observation symbol sequence of an hmm that uses the graphemic forms as a hidden state sequence
the only drawback was the decision about the type of the first letter uppercase or lowercase nouns always start with a capital letter while other words do not
the model s parameters can be estimated using the definition formulas since both the hidden state and the observation symbol sequences are known during the training phase of the conversion system
the size of the training corpus and the sparseness of the resulting matrices can lead to different approaches in the definition of the estimation function n x
the system was implemented using the c programming language on computational linguistics volume NUM number NUM a NUM based computer in protected mode thus exploiting its full NUM bit architecture
the architecture shall allow detection results from different collections or different detection components to be combined for viewing proposes
NUM eats cookies and drinks beer
dress NUM NUM corresponding to the v conj vinstantiation
a further experiment incorporated word class information from wordnet into the model by allowing the transformations to look at classes as well as the words
NUM collapsing the supertrees above the vp node
an experiment was made with all counts less than NUM being put to zero NUM effectively making the algorithm ignore low count events
NUM chapman eats cookies and drinks beer
NUM keats steals and chapman eats apples
here f w p is the number of times preposition p is seen attached to word w in the table
these figures should be taken in the context of the lower and upper bounds then choose noun attachment else choose verb attachment
the algorithm never builds derived structures
a coordinated node will never dominate multiple foot nodes
ambiguities of segmentation into utterances are frequent and most annoying as analyzers generally work utterance by utterance even if they can access analysis results of the preceding context
the baseline is again provided by attachment according to the principle of right attachment to the nmst recent possible site i.e. attaclunent to noun2
the word grammar is compiled into a shift reduce parser
the following is a description of the interpreter
two level analysis listing NUM is the main predicate
s emh e a generalised two level system
uninstantiated contexts are denoted by an empty list
the recursive case is shown in listing NUM
if an expression is not an empty list i.e.
in generation the opposite state of affairs holds
the trigger backward phase would incorporate the temporary graphs for address mail post office and stamp
expansion backward find words in the dictionary whose definitions contain the semantically significant words from the concept cluster
start with a central word a keyword for the subject of interest that becomes the trigger word
the concepts inw lved are general and typical of a daily lid conw a salion
the concept hierarchy concentrates on nouns and verbs as they account for three quarters of the dictionary definitions
the expansion backward would finally add the temporary graphs for card and note again terminating after two steps
lustering is often seen as a statistical operation that puts together words somehow related
the number of occurences note of each word present in the definition of letter is given
we can set a limit to the number of steps in the expansion phase to ensure its termination
for each possible new word find the maximal common subgraph between its temporary graph and the cckg
fass also distinguishes a selection restrictions violations view presenting the metaphor as a kind of anomaly
we turn to the context principle for an intuition about how to solve this problem
these clues can be characterized by syntactic structures and lexical markers
table NUM gives the average processing times per input lattice for each type of processing times measured running sicstus prolog NUM NUM on a sun sparc NUM hs21 showing how the time is divided between the various processing phases
however as the results in section NUM below will show this is not in practice a serious problem because the second pruning phase greatly reduces the search space in preparation for the potentially inefficient full parsing phase
these properties already found in the previous works can help detecting the clues themselves
of course neither assumption is any more than an approximation to the truth but assuming dependence has the advantage that the estimate of the joint probability depends much less strongly on n and so estimates for alternative joint events can be directly compared without any possibly tricky normalization even if they are composed of different numbers of atomic events
we would like to thank christer samuelsson for making the lr compiler available to us martin keegan for patiently judging the results of processing NUM NUM atis utterances and steve pulman and christer samuelsson for helpful comments
let us now examine the salient points of each type o1 technique
the two techniques do not differ from an external functional point of view
multisentential text this section describes tile three text production techniques under assesslllelt
there were also other criteria but they were too application oriented and confidential
proximity personalisation human letters NUM NUM better
phrasal rules the majority of which define non recursive noun phrase constructions are used as they are non phrasal rules are combined using ebl into chunks forming a specialized grammar which is then compiled further into a set of lrtables
we adapted this score to noun phrase patterns however the similarity measures based on cooccurrence scores and nominal phrase patterns are less relevant for an ontological analysis
we found that subsequential transducers tend to handle leftward context much better than rightward context
on one hand a terminologist must identify the essential entities of the domain and their relationships that is its ontology
the parser presented here outperforms both the bigram parser and the spatter parser and uses different modelling technology and different information to drive its decisions
we present this approach in section NUM section NUM describes the results on two technical corpora with two different robust parsers
parsers lcb he accents are removed during tile analysis the lemmas are used instead of inflected fo ms
we conclude that boundaries identified by at least three of seven subjects most likely reflect the validity of the underlying notion that utterances in discourse can be grouped into more orless coherent segments
however it is extremely likely that some subtrees especially trivial ones like
we call the best parse tree under this criterion the maximum constituents parse
thus he concludes that his algorithm runs in polynomial time
figure NUM simple example stsg can still be parsed
one could try to find the most probable parse tree
this tree corresponds to the most probable derivation of xx
thus this tree has on average NUM constituents correct
all other trees will have fewer constituents correct on average
the results are disappointing as shown in table NUM
for example one of the incorrect translations made by champollion is that important factor was translated into facteur factor alone instead of the proper translation facteur important
the problem of assigning a correct tense of the english output there are only NUM tenses in polish is currently solved on the basis of the surface structure of the polish input expression the solution is far from perfect
for any two chinese characters in a sentence denoted as x and y if xy can not be combined together to function as a word a single word boundary exists between these two characters
computational linguistics volume NUM number NUM words that pass these tests are collected in a set s from which the final translation will eventually be produced
we compared the results and found that out of the NUM source collocations NUM were not frequent enough in the database to produce any candidate translations
we reduced the effects of low frequencies by purposefully limiting ourselves to source collocations of frequencies higher than NUM containing individual words with frequencies higher than NUM
this happens when one or more of the parts of the final translation appear frequently in the corpus but not together with the other parts or the source collocation
the bilingual collocations could be used to translate the query particularly protection de NUM environnement taxe de vente f derale tools for the target language
consequently it might be beneficial to take into account both the distribution of the base form and the differences between the distributions of the various inflected forms
jean francois champollion a linguist and egyptologist made the assumption that these inscriptions were parallel and managed after several years of research to decipher the hieroglyphic inscriptions
these two types of matches correspond to the cases where either both word groups of interest appear in a pair of aligned sentences or neither word group does
while collocations are not predictable on the basis of syntactic or semantic rules they can be observed in language and thus must be learned through repeated usage
for example champollion translates make decision employment equity and stock market into prendre d6cision 6quit6 en mati6re d emploi and bourse respectively
this ensures that every elementary auxiliary tree will be uniquely either a left auxiliary tree or a right auxiliary tree
auxiliary trees in which every nonempty frontier node is to the left of the foot are called left auxiliary trees
by convention the foot of an auxiliary tree is indicated in diagrams by using an asterisk
the l tig created can be represented compactly by taking advantage of sharing between the elementary trees in it
books on more sophisticated call systems are still scarce NUM NUM so is the work that shows how current nlp technology could be used in the classroom NUM NUM NUM yet call is a field with considerable potential
given a lexicon with tagged surface pronunciations the next required step is to count how many times each of these pronunciations occurs in a speech corpus
how can this technology meet the demands of pedagogical theory for communicative language teaching in a natural environment
if each word had only a single pronunciation and if each phone had some fixed duration the phonetic string would be completely determined by the word string
the second step of the algorithm forced viterbi alignment takes this vector of likelihoods for each frame and produces the most likely phonetic string for the sentence
in the last two paragraphs we sketch some ongoing work
so on a bigram model the multilevel system is NUM NUM better than the best two level system which supports our claim
this latter result is based on a similarly sized training set and so our NUM improvement compared to their test set perplexity improvements
figure NUM shows clearly that the smoothed bigram component does indeed find each class level useful at different frequencies of the conditioning word
in order to estimate the average class mutual information for a classification depth of s bits we compute the average class mutual information
the main algorithm takes several weeks to cluster the most frequent NUM words on a sparc ipc and several days for the supplementary algorithm
in our computational linguistics volume NUM number NUM training corpus boy and seat are individually more likely than boys and eat
currently no method exists that can find the globally optimal classification but suboptimal strategies exist that lead to useful classifications
since the structural tag representation is binary this first level seeks to find the best distribution of words into two classes
we make the assumption that any influence that these infrequent words have on the first set of frequent words can be discounted
this information consists of phrase head patterns around the possible locations of pp adverb attachments
the resuli sop l his cxl oi ilileni
in the case of multiple expressions on the right hand side of a statement we pursue each of them entirely independently of the others
global inheritance operates in exactly the same way the global context is initially set to the node and path specified in the query
and we take white space spaces new lines tabs etc to delimit lexical tokens but otherwise to be insignificant
we extend this convention to variables discussed more fully in section NUM NUM below which we require to start with the character
in this section we have moved from simple attribute value listings to a compact generalization capturing representation for a fragment of english verbal morphology
finally the definition of mor form at verb adds an explicit ing resulting in a value of love ing for wordl mor form
in this paper we shall distinguish them by a simple case convention node names start with an uppercase letter atoms do not
this is the simplest form of datr inheritance it just specifies a new node and path from which to obtain the required value
verb mor root come mor past came mor past participle mor root syn intransitive
here the empty path inherits from verb so the value of come is equated to that of verb
the paragraph unit everyone who writes a grammar within the alep platform has some contact with its text handhng tit system which converts each input into a sgml tagged expression
there are several ways of representing the semantic interpretation of each of the utterances and three of them NUM NUM are discussed by groenendijk stokhof a in classical predicate logic
and as it is well known the interpretation of sentences embedded in larger units is often distinct from the one of sentences which are standing on their own
i would like to thank gordon cruickshank ray systems laxembourg who gave me the initial idea to use this strategy in order to describe the interdependency of information between sentences
the dynamic semantic interpretation of this quantifier blocks the passing of the information the ouptut of the first sentence is empty with respect to the information concerning anaphoric binding
the resolution of cross sentential anaphora is one of the problems we have to deal with when we switch towards the analysis or synthesis of such larger linguistic units
as usual and also obligatory for tile development of grammars within alep a so called ts ls rule a mapping between text structures and linguistic structures has to be defined
in order to propose an analysis of the cross sentential anaphora one has to be able to refer back to an antecedent which is to be found in a preceding sentence
tagging spelling lbr one correction sentence NUM NUM NUM NUM msec
note start means the beginning of a sentence stop means the end of a sentence
step NUM NUM these steps are preference based pruning syntactic based pruning and semantic based pruning
the techniqueis implemented by using word boundary preference syntactic coarse rules and semantic dependency strength measurement
in the following sections we will begin by reviewing three non trivial problems of thai morphological analyzer
consider the following example t spelling error t q jlfl lcb he pron
in our corpus more than NUM of sentences include word boundary ambiguity
accordingly tag ambiguity in thai causes a large set of tagged word combinations
while the learning NUM tree is more complex than the tree of figure NUM it does have slightly better performance
noun phrase generator responsible for drawing lexical information from the lexicon to create a self contained functional description representing each noun phrase required by the fd skeleton processor
there is a difference of at least NUM attachments NUM NUM accuracy between the best results in these tables and the results that did not use word classes or partial patterns
in all these cases the use of partial patterns and word classes was varied in an attempt to determine their effect
despite the large statistical differences in attachment preferences in the two corpora training on the first corpus and testing on the second gives an accuracy of NUM NUM
in the current experiments equal weights are used for simplicity but results are still good on the trains corpora NUM NUM and NUM NUM accuracy
topic nodes are subtopics of exposition nodes and each topic node includes a representation of the conditions under which its content should be added to an explanation
the result is a probabilistic parser which unlike a pcfg is capable of probabilistically discriminating derivations which differ only in terms of order of application of the same set of cf backbone rules due to the parse context defined by the lr table
in future work we intend to explore a more restricted and semantically driven version of this approach in which firstly probabilities are associated with different subcategorisation possibilities and secondly alternative predicate argument structures derived from the grammar are ranked probabilistically
in addition schabes et al do not recover tree labeling whilst magerman has developed a parser designed to produce identical analyses to those used in the penn treebank removing the problem of spurious errors due to grammatical incompatibility
given that accuracy is increasing only slowly and is relatively close to the asymptote it is therefore unlikely that it would be worth investing effort in increasing the size of the training corpus at this stage in the development of the system
for example textual adjunct clauses introduced by colons scope over following punctuation as 2a illustrates whilst textual adjuncts introduced by dashes can not intervene between a bracketed adjunct and the textual unit to which it attaches as in 2b
NUM construct a list adj list of all the phrases which fill adjunct slots of a
l subcat l dtrslheaddtr phon sings
but these theories have been inadequate in accounting for the semantic end point of a change in fact they have scarcely recognized the need for such an account
the category of temporal structure and the member notion of continuity as expressed by keep are high in the graduated inventory of closed class concepts
here all the structural characteristics of the earlier referent have been altered e.g. the interrogative instead of the declarative future tense instead of past and different assignments of number and definiteness
for this paper the spontaneous and read recordings for one male speaker were acoustically analyzed fundamental frequency and energy were calculated using entropic speech analysis software
but due to their difference in closed class forms the two sentences evoke alternative conceptualizations of the situation conceptualizations that differ with respect to choice of perspective point and distribution of attention
in addition there is the imaging system of force dynamics the different patterns of force relationships in which one object can act on another
while this inventory is universally available each language has closed class forms that represent only a subset of all the structuring concepts and this subset is different in each language
the linguistic principle that here appears to constrain the geometry of a closed class schema is that while the schema as a whole is magnitude neutral its parts must be of comparable magnitude
we concluded that this statistic did not serve for example to capture the differences observed between labelings from text alone versus labelings from text and speech
the embedding relationships reflect changes in the attentional state the dynamic record of the entities and attributes that are salient during a particular part of the discourse
these principles are thus more general than the first order identification of the structuring concepts found to be present in the universal inventory and they are in part explanatory of those entries in the inventory
to map out conceptual structure in language accurately care must be taken to distinguish the actual properties of the qualitative geometry that structures closed class reference in language from the forms of topology current in mathematics
however this can not be the whole story since the object denoted by the internal argument of kill is presumably enduringly affected by the killing yet a killed man seems about as odd as a seen movie
these problems are further exacerbated by errors of the speech recognizer
the parsing grammar specifies patterns which represent concepts in the domain
these grammars are compiled into recursive transition networks rtns
a failed concept does not cause the entire parse to fail
we evaluate the translation modules on both transcribed and spee ch
thus it is well suited to doinains ill which nongrammaticality is coalition
expressions and incorporates the sentence into a discourse plan tree
for speech synthesis we use a commercially available speech synthesizer
these results indicate that combining the two approaches has the potential to improve the translation performance
the linguist may choose to add knowledge as it is needed or may prefer to do this work in batches
for example 2a classifies as ditransitive and 2b as a specialized transitive with a pp
also it will be necessary to decide what semantic features are needed to restrict the fillers of the argument structures
to support the batch approach it may be useful to extract detailed subcategorization information from english learner s dictionaries
second the classifier can identify an existing verb class that might explain an unassigned verb s behavior
linking involves restricting the fillers of the gfs in the subcategorization to be the same as the arguments in an event
specialized event entities are used in the definition of verbs in the lexicon and represent the argument structures for the verbs
once such a class is identified the meaning component that the member verbs share can be identified
when writing and maintaining a large grammar inconsistent rules is one type of grammar writing bug that occurs
we present a maximum likelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently using as examples several problems in natural language processing
given this sample which represents an incomplete state of knowledge about the process the modeling problem is to parlay this knowledge into a representation of the process
in d the two constraints are inconsistent i.e. q n c3 NUM no p e can satisfy them both
maximum entropy principle to select a model from a set c of allowed probability distributions choose the model p e c with maximum entropy h p
these two models plus a search strategy for finding the that maximizes NUM for some f comprise the engine of the translation system
this yields a great savings in computational complexity over computing the exact gain an n dimensional the likelihood l p is a convex function of its parameters
when we discuss real world applications in computational linguistics volume NUM number NUM combining NUM NUM and NUM yields the more explicit equation
c NUM association for computational linguistics computational linguistics volume NUM number NUM rules from annotated text automatically and incorporate these rules into statistical models of grammar
after applying iterative scaling to recompute the parameters of the new model the likelihood of the empirical sample rose to NUM NUM bits an increase of NUM NUM bits
for every word position j in f aj is the word position in e of the english word that generates yj figure NUM depicts a typical alignment
one benefit involves economy of code many of the processes which need to be coded to deal with ideation for a text as a whole can also be used to deal with ideation for single sentences
the input specification for the wag sentence generator is a speech act which includes an indication of which relations in the kb are relevant for expression at this point
however grosz s notion of relevance is based on the needs of a text understanding system which objects in the knowledge base can be used to interpret the utterance
taking this approach the role of the semantic specification is to describe how the information in the kb is to be expressed including both interactional and textual shaping
sinformation status only partially constrains the choice of referential form the choice between the remaining possibilities can be made by the sentence planner by specifying directly grammatical preferences
the recoverable entities field a list of t the ideational entities which are recoverable from context whether from the prior text or from the immediate interactional context
when talking about ideational specification we need to distinguish ideational potential the specification of what possible ideational structures we can have and ideational instanrials actual ideational structures
in penman the ideational specification is central a semantic specification is basically an ideational specification with the speech act added as an additional and optional field
this concerns for instance the thematic structuring of the ideation presented in the text its presentation as recoverable or not the semantic relevance of information etc
the second measure used is an average of the precision for each topic after i00 documents have been retrieved for that topic
it took on added importance in the trec environment because only the top NUM documents retrieved for each topic were actually assessed
each participating group was provided the data and asked to turn in either one or two sets of results for each topic
an informal spanish test was run in trec NUM but the data arrived late and few groups were able to take part
in each of the trec evaluations each topic was judged by a single assessor to ensure the best consistency of judgment
the averaging method was developed many years ago NUM and is well accepted by the infomaation retrieval community
these criteria were designed to approximate a high precision run a high recall ran and a balanced run
there were about NUM megabytes of spanish data the el norte newspaper from monterey mexico and NUM topics
these workshops were held at the national institute of standards and technology nist in november of NUM and NUM respectively
the results from searches using q2 and q3 are the official test results sent to nist for the routing and adhoc tasks
the decision problem is whether there is a parse generated by the resulting stsg for this sentence that has probability larger than or equal to q
then the probability of a parse is redefined as the probability of the mpd that generates it thereby collapsing the mpp and mpd
the proof here is informah we show a non deterministic algorithm that keeps proposing solutions and then checking each of them in deterministic polynomial time cf
the probability of an elementary tree can be redefined as the sum of the probabilities of all derivations that generate it in the given stsg
we conclude that computing the mi p mps mpp from a sentence word graph word graph respectively is np hard under dop
but as soon as the grammar size becomes an important factor e.g. in dop polynomiality becomes a very desirable quality
this results in machines that may seemingly at random insert or delete sequences of four or five segments
while the larger transducer of figure NUM is accurate the smaller transducer is desirable for a number of reasons
the behavior of different phones within each context is represented by the different arcs without making separate states necessary
as we will discuss in section NUM this is similar to what occurred in the machine induced for flapping
gildea and jurafsky learning bias and phonological rule induction table NUM results on german word final stop devoicing NUM NUM word test set
since our approach learns only segmental structure a more relevant comparison is with other algorithms for inducing segmental structure
for each segment the system uses the version space algorithm to search for the proper statement of the context
for other applications it may be desirable to keep a cross validation set for this purpose
first the necessary number of sample transductions may be several times the size of any natural language s vocabulary
the transducer of figure NUM will insert an ae after any b and delete any ae from the input
in this section we describe a method for statistical language modeling that transcends these limitations
the parsing model is a probabilistic recursive transition network similar to those described in miller et ai
our strategy is to embed the instructions for constructing ms directly into parse t o resulting in an augmented tree structure
the highest scoring theory is then selected and a straightforward computation derives the final meaning frame mo from output vector y
because pronouns can usually be ignored in the atis domain our work does not treat the problem of pronominal reference
the training procedure for estimating these decision tree models is similar to that used for training the semantic interpretation model
these vectors have the following interpretations x represents the combination of previous meaning me and the pre discourse meaning ms
the conditional word probability p wit has already been computed during the parsing phase and need not be recomputed
we have trained and evaluated the system on a common corpus of utterances collected from naive users in the atis domain
the nmsu document manager was also integrated into the lockheed martin text processing system nltoolset in a flexible way that will allow other document managers to be included easily
the feature set and search algorithm were tested and debugged only on the training and development sets and the official test result on the unseen test set is presented in the conclusion of the paper
figure NUM performance of different models
NUM NUM effect of proportion of unknown words
does the word include a colnlna
john np x x name x john ran vp x y x run x argl x y past x fast adv x x fast x quickly adv x x fast x
does the word include a hyphen
l he established matching in previous stage can be changed when another matching which has higer matching weight is identified in this algorithm
from the parsing viewpoint this suggests that each conjunctive particle has modification preference with certain predicates or auxiliary verbs
we applied ldg to a prosodic information control method in a japanese text to speech conversion system to confirm the conjunction level experimentally
in other words modality information of the subordinate clauses is attached to the plain form of the main verb
however this inclines toward an improper output since the locally highest likelihood is sometimes low on the whole
besides em0unctiw partmes japanese conjunction nouns u relative nouns are also classiiied avd assigned it level
one of them is the adverb moshi o l that indicates a supposition reading is applicable
in future research ldg will also be applied to other prosodic information rhythm and intonation
we tried the simplest approaches first and then only generalized those algorithms whose inadequacies clearly degrade the performance of the system
the architecture provides a shopping list of capabilities that may be included in the application requirement document as appropriate
kappa is widely accepted in the field of content analysis
this structure is in conflict with the previously constructed affinity relation between the characters dg b n and r n
based on pos matrix and constraints now we can use the following definition to detect the position of error in the word chains
the latter can not be detected by simply using dictionary since the error call lead to words that are unintended but spelled correctly
like many other languages such as japanese chinese and korean thai sentences are formed with a sequence of words mostly without explicit delimiters
the authors claim that when a relevant threshold is set the algorithm can recommend connections for NUM for the words in NUM sentence pairs
NUM n NUM a person mlo keeps a supply of moncy or pieces for paymcnt or use in a game of chance
table NUM indicates that acquired lexical infornmtion augmented and existing lexical information such as a bilingual dictionary can supplement each other to produce optimum aligmnent results
we believe the proposed algorithm addresses tile problem of knowledge engineering bottleneck by using both corpora and machine readable lexical resources such as dictionaries and thesauri
the algorithm s performance discussed here can definitely be improved by enhancing the various components of the algorithm e.g. morphological analyses bilingual dictionary monolingual thesauri and rule acquisition
the class based can be acquired either from bilingual materials such as example sentences and their translations or definition sentences tbr senses in a machine readable dictionary
they recommended using an aligned bilingual corpus to estimate the parameters of translation probability pr st tt in the translation model
our algorithm attempts to handle the problem using class based rules which are automatic acquired from bilingual materials such as a bilingual corpus or machine readable dictionary
this system can easily be extended to additional languages such as japanese russian chinese etc
the relative directions of these vectors encode the meaning and context of information that is to be retrieved
this system attaches context vectors to cluster centroids of feature vectors composed of gabor features and color information
the arrival of the information age has brought with it new challenges for handling the vast quantities of electronically available information
neural network based training laws are used to adjust the components of these vectors in an iterative fashion
the icars system is being developed to solve the difficult problem of image retrieval and image content characterization
this technology has also been transitioned to the commercial sector thereby satisfying a major objective of the funding agencies
this system uses redundant hash table addressing to store context vectors for a multilingual vocabulary
preliminary tests have demonstrated that this system is able to retrieve relevant images without the need for complex image understanding algorithms
the purpose of this brief paper is to summarize hnc s research accomplishments and to make recommendations for further study
in contrast the maxent model combines diverse and non local information sources without making any independence assumptions
the model s probability of a history h together with a tag t is defined as
upon further examination NUM the tagging distribution for about changes precisely when the annotator changes
this training data does not overlap with the development and test set used in the paper
unlike maxent can not be used as a probabilistic component in a larger model
here the entropy of the distribution p is defined as
table NUM errors on development set with baseline and specialized models
given a sentence a tag sequence candidate has conditional probability
note that each parameter aj corresponds to a feature fj
otherwise the specialized features will model noise and perform poorly on test data
hi figure NUM c denotes the count of the specified event in the training corpus
however there is ambiguity in the segmentation of the string NUM zl j NUM
the average word lengths of all words and that of low frequency words were NUM NUM and NUM NUM respectively
we used word segmentation pronunciation and part of speech in the morphology information field of the annotation
we limited tile maximum character length of the a unknown word to NUM in order to save computation time
where p is precision r is recall and NUM is the relative importance given to recall over precision
suppose there exists a perfect reranking scheme that for each sentence magically picks the best parse from the top n parses produced by the maximum entropy parser where the best parse has the highest average precision and recall when compared to the treebank parse
language engineering is slich an activity that implements various fnnctions related to a language and builds lip an information base
included in this class are natural language analysis pattern recognition multimedia data base and data conversion tools
we started the project in NUM to yield version i platform in NUM and are working on version NUM platform
these will be i clhded after the tirst phase of the project following future direction of the project
engineering platform an open architecture for language engineering cec and cray sys
yet another point is that the server client model makes the platform transparent to the users
its current precision is over NUM percent for the grammatical inl ut sentences
NUM ltttp world kaist ac kr kle kibs is
centering theory can be used to segment a discourse by noting whether the same center of attention cb is preserved from one ut null terance to another
these items may be retrieved modified and stored as new items
the specific search and identification algorithms are dependent upon the particular application
additionally text that caused template or object instantiation shall be tagged
these may be stored separately from the document to which they apply
annotations are information added to a document by user or computer processing
the format for selection statements must be common and sharable between applications
applications shall not be prevented from interfacing to external source control information
given a set of some partial ordering constraints and a domain within which they are to be enforced the implementation of this as threading proceeds as follows
however a useful compromise is to add to our formalism a new type of feature whose values are members of an implicit lattice of atomic types
secondly for each relevant rule introducing these categories in the given domains we need to identify among the daughters some kind of head complement or governorgoverned relation
however summarization is not a trivial task
the use of a particular method is dependent upon the specific tipster application
we need to know the subcategorized for categories the symbols used to identify them and the maximum number that can occur in a single verb complement construction
rules or lexical entries that build a member of categories must have the scat feature added with tuple values like npl np2 np3 etc
that is the speech of clients who had participated in the experiment with agent a was compared with the speech of agent b likewise the speech of clients who had participated in the experiment with agent b was compared with the speech of agent a because these conversants were not talking to one another the lexical overlap in their speech could not be a result of accommodation to one another
speech accommodation theorists who fall under a broad category which might be called sociolinguistics tend to ascribe one of three motivations to speakers who accommodate evoking listeners social approval attaining communicational efficiency between interactors and maintaining positive social identities
in the first experiment native english speaking subjects acting as clients were instructed that their task was to get directions to the site of a conference they were attending by engaging in a cooperative dialogue with a native english speaking conference agents
but not as many as would be expected with the loaded die
with the same reasoning the words de struc faming invention y y implication and zhbngd profound are identified
the middle character i gdng can combine with the previous character yudn to form the word hi yudngong worker leaving the third character functioning as a monosyllabic word zud do
the system s ability to generate different word boundaries for a globally ambiguous sentence arises from its stochastic search mechanism which does not rule out a priori certain possibilities
the probability of selecting an instance of a word codelet an affinity codelet and an affix codelet is NUM NUM NUM NUM and NUM NUM respectively
for example the relaxation approach uses the usage frequencies of words and the adjacency constraints among words to iteratively derive the most plausible assignment of characters into word classes
he is a foreigner the word boundaries identified will be t4 sh w d gu6r n he copula out citizen which is incorrect
when a large number of structures deemed to be good have been found which entails a low temperature the system will proceed in a more deterministic fashion always preferring good paths to bad ones
NUM represent each new document that arrives as a numerical object
NUM the foreign name model is implemented as an wfst which is then summed with the wfst implementing the dictionary morpho13 the current model is too simplistic in several respects
the only way to handle such phenomena within the framework described here is simply to expand out the reduplicated forms beforehand and incorporate the expanded forms into the lexical transducer
this architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives and models for personal names and foreign names in transliteration
thus we feel fairly confident that for the examples we have considered from gan s study a solution can be incorporated or at least approximated within a finite state framework
both of these analyses are shown in figure NUM fortunately the correct analysis is also the one with the lowest cost so it is this analysis that is chosen
other strategies could readily NUM as a reviewer has pointed out it should be made clear that the function for computing the best path is an instance of the viterbi algorithm
then each arc of d maps either from an element of h to an element of p or from c i.e. the empty string to an element of p
on the other hand in a translation system one probably wants to treat this string as a single dictionary word since it has a conventional and somewhat unpredictable translation into english
we evaluate the system s performance by comparing its segmentation tudgments with the judgments of a pool of human segmenters and the system is shown to perform quite well
a number of general topics have been tested for developing specialized libraries for our on line search system
the identification of single word terms is based on the variation of a t test
such a toolkit has proven to be useful in a number of real applications
figures NUM and NUM present the area for document browsing and key terms selection
the topic illustrated in the figures is the legal topic medical malpractice
documents representing the four highest frequencies for the selected term will be displayed first
the following descriptions should be viewed together with the appropriate figures of the gui component
to identify interesting word patterns in both samples a set of statistical measures are applied
the document identifier window identifies the document that is currently displayed in the document window
for instance coreference between two templates that are far away might be unlikely if there are no coreferring expressions between them but quite likely if there are
again however the reduction in cross entropy is important as the statistics produced by the system will be integrated with other probabilistic factors in the downstream system
for example in the ewduation data set increasing n fl om NUM to NUM introduces only NUM new possible word error improvements
table NUM describes the nuinber of mne as a function of n for the training data set and evaluation lata set
this s ore in li ates the similarity b tween the set of keywords and the article
summary anext week some inmates released early from the hampton county jail in springfield will be wearing a wristband that hooks up with a special jack on their home phones
wu and xia NUM NUM used i ilingual dictionary to segment the sentence but the selectioii of segment can lid ttes is hard to make with rdiable accuracy
in particular the arallleters i ccotlllt or th cooctll ren e probilities el bilingual word pairs and phrase pairs
tqgure NUM an example of lforea n
NUM he structural dissimilarity between korean and indo european languages requires more flexible measures to evaluate the alignment candidates between the bilingual units than is used to handle the pairs of indo european languages
the g eneration of segments to be aligne l is an additional prol h m to the decision of aligning units before 1he aligmnent takes l b ce
NUM NUM characterlsl i s of korean english
the parameters are estimated using the em algorithm
for example if we presuppose that the given code string is encoded with euc jis then the adjacent two bytes that match a1h feh lcb NUM rcb a two byte sequence whose values ranges from a1 to fe in hexadecimal representation correspond to a japanese or jis character
for this reason it is necessary to identify the language from the content of the given character string
next the statistic based language identification is applied to each decoded string table NUM shows the score probability of each character as regards to the language that produced the highest likelihood i.e. average score
zho chinese kor korean jpn j apanese the bottom row shows that the highest average score is obtained when the input is decoded with euc j s and the language is japanese jpn
although most of the local coding systems in the world are compatible with iso NUM many of them lack escape sequences which are not necessary for choosing the correct character set in the local domain but are necessary in the international domain
the last np feature global pro is computed from the coding of other features and of previously occurring boundaries
a potential correlation between discourse signaling uses of cue words and adjacency patterns between cue words cue2 was also suggested
in addition we are concerned with the extra step of developing segmentation algorithms rather than with the demonstration of statistical correlations
in section NUM we use boundaries abstracted from the data produced by our subjects to quantitatively evaluate algorithms for segmenting discourse
the modalities of the corpora investigated include dialogic or monologic written spontaneous or read and the genres also vary
NUM the large majority of responses NUM fall within the bars for n NUM NUM NUM
duration is assigned x convention NUM or y for convention NUM if pause is true NUM otherwise
NUM as we discuss further below both the rate at which subjects assigned boundaries and the size of segments varied widely
yet until recently there has been little attempt to quantify the degree of variability among subjects in performing such a task
for thcse phrascs to be fixed there will have to be more than one rule to nudge the appropriate phrase botmdaries over
it assigns a large probability to a character sequence that appears in the beginning prefixes the middle and the end suffixes of a word
the second and the third ones carry out reestimation before extraction where the pruning thresholds of the expected n gram counts in the reestimation are NUM NUM and NUM NUM respectively
tile more often the unknown word appears in the corpus the more it is likely to be extracted even if there is word segmentation ambiguity in each sentence
what makes semantic tagging appealing is among others the justified hope that it will contribute to the improvement of the performances and the robustness of nlp systems
most of the new words extracted by the system are acceptable as a word at least for us and nmy not necessarily be a wrong word entry
figure NUM shows three possible analyses of the input sentence where each box represents a word hypothesis whose meaning and part of speech are shown above and under the box
where c a denotes the count in the segmented corpus and cuns a denotes the estimated count in tile unsegmented corpus
this means that we regard word length as the interval between hidden word boundary markers which are randomly placed with an average interval equal to the average word length
here cpq wi is the probability of the most likely word segmentation sequence for the character sequence cq0 whose final word wi spans the substring c
rules NUM and NUM are closely analogous to rules NUM and NUM
however the first a2 rule has the a1 rule substituted into it
the first lemma converts cfgs into a very restricted form of tig
our direct purpose is not to create tagged corpora but to tag enough corpus lines to allow us to make reliable generalizations on the meanings and on the semantic and syntactic valence of the lexical entries we have set out to describe
identifying the semantic flame associated with a word and the fes with which it constellates does not of course constitute a complete representation of the word s meaning and our semantic descriptions will not be limited to just this
it is important to recognize these cases since the lexical semantics of verbs sometimes require that certain frame elements be instantiated or clearly recoverable from the context corpus research on the verb cure for example shows that the dis order is regularly instantiated
the study of the frames which enter into human cognition is itself a huge field of research we do not claim to know in advance how much frame knowledge must be specifically encoded in frame descriptions to make them useful for either linguistic or nlp purposes
we have suggested a theoretical basis and a working methodology for coming up with an appropriate set of semantic tags for the semantic frame elements and believe that such frames may constitute a sort of basic level of lexical semantic description
first appealing to common unformalized knowledge of health and the body the frame semanticist identifies the typical elements in everyday health care situations and scenarios a process involving the interaction of linguistic intuition and the careful examination of corpus evidence
an initial formulation of the combinatorial requirements and privileges of a frame s lexical members here we concentrate on verbs can be presented as a list of the groups of fes that may be syntactically expressed or perhaps merely implied in the phrases that accompany the word
an education and research tool for computational semantics
the o e NUM e term gives a measure of the difference in a word s frequency between two corpora and while the measure tends to increase with word frequency it does not increase by orders of magnitude
this phenomenon which in the domain of inflectional morphology is termed syncretism can be illustrated by a dutch example such as lopen walk which can either be the infinitive form to walk or the finite plural present tense form we you or they walk
pruning is done by stepping through each state of the machine and pruning as many branches as possible from the fringe of the current state s decision tree
we conclude our discussion of the community bias by seeing how a more on line implementation of the bias might have helped our algorithm induce a transducer for r deletion
but we believe that the biases we have relied on to improve the ostia algorithm may also prove useful when applied to such stochastic linguistic rule induction algorithms
a decision tree is induced for each segment classifying possible realizations of the segment in terms of contextual factors such as stress and the surrounding segments
in any case the o nm complexity of the preprocessing step is subsumed by the o nmk term of ostia s complexity
the correctness of the algorithm requires that the states be ordered such that state numbers always increase as one walks outward from the root of the tree
in our first experiment we applied the flapping rule repeated again in NUM to training corpora of between NUM NUM and NUM NUM words
when trying to learn phonological rules from finite linguistic data however we found that the algorithm was unable to learn a correct minimal transducer
that is given an infinite sequence of valid input output pairs it will at some point derive the target transducer from the samples seen so far
the statistics converged to their final values quickly
thus this figure of merit can be written as
the following recursive formula can be used to compute al
from figure NUM we can see that the models using the geometric mean appear to level off with respect to an exhaustive parse when used to parse sentences of length greater than about NUM
we refer to this model as the trigram estimate
we will refer to this as the prefix estimate
this function can be performed by existing document managers or cots products such as a standard dbms with the addition of a wrapper
moreover type NUM processing tries to determine the semantic relation between the noun with ga and the noun with no originally wa
for each such applicable rule the learner considers the possible improvement in phrase labeling conferred by r in the current state
during document setup pre existing markups must be converted to the standard in order to be used by the tipster parts of the application
llmrelbrc lhc aim of lhis paper is to discuss a melhod for malyzing lhc japanese double subject constnlction havhlg an adjective predicate from lhe point of view of engineering
type NUM is set ff the modifier with case marking particle ga is bound to the subjective case and the modifier with adverbi d particle wa is not
an application must be able to ignore any markups it does not use that is unused markups should not cause it to break
a component is an aggregation of software that satisfies an end use function such as detection extraction clustering or user interface
the technology transfer officer is responsible for the integration of an application into the end user s environment and for major upgrades to the application
we build a knowledge graph where the concepts interact with each other giving impel taut implicit information that will be useful for natural language processing tusks
the learning terminates successfully when all lp rules are found i.e.
east into a ropresentation that sul ports our learning process
the whole process ends when all lp rules are learned
we also need to define the interpretation and instance selection processes
a in short we use t to optimally generalize the values of an attribute at each tree generation step which makes the extension quite natural
since proposition NUM holds we can solve the optimum attribute generalization problem by finding the shortest traverse NUM in the traversal graph
we can observe that both semantic codes and word forms are mixed at the 7part of lasa NUM was used as the dtla
they actually refer to the case restrictions for the case and the translation of the verb respectively in our application
in this paper we allow to use the node sets which cover the word forms in the table uniquely and completely
then we want to clarify the relationship between the number of leaves data amount denoted by d and the number of arcs in the traversal graph
we analyzed lexical accommodation in a variety of interactions in order to determine how accommodation can be expected to operate in a machine interpreted context and learn ways in which to support lexical accommodation in the design of human machine interfaces
by comparing these proportions for the roles of client and agent we ascertained the relative frequency of the word for each role giving us some idea of the importance of the word for that role
we will argue that accommodation is a real phenomenon in dialogue it follows then that at least some of the instances in which conversants use lexical items previously used by their partners are instances of accommodation
these files c mtain information about the position of words by their key in the corpus file up to a certain maximum e.g.
NUM c st largemettt tributaire de ce que le p re gundlaeh a 6crit dans un article intituld amis mitisme
the tool provides a dictionary lookup it gives examples fl om corpora and displayes morl hological information all on line
the morpho logical analysis and disamlfiguation of this selected word and tire dictionary entry will m cordingly be displayed in the relevant windows
we envision a user f internmdiai e level in ig ench sc hool level not universil y
in order to determine i he size of corpus needed wc experimented with a frequency list of the NUM NUM most frequent words
our design calls for l1 xemb dased search however and a preliminary version of this has also been implemented
common an examination of the use of each word in common with respect to overall word use for client and agent i.e. the word s importance showed the following results figure NUM three way anova
if house appears within the next three words e.g. the phrases in the house and in the red house then dans might be a more likely translation
finally in section NUM we describe the application of maximum entropy ideas to several tasks in stochastic language processing bilingual sense disambiguation word reordering and sentence segmentation
here the log likelihood is represented as an arbitrary convex function over two parameters a corresponds to the old parameter and a to the new parameter
if there are NUM NUM total french words there are 2ivy i possible features of templates NUM and NUM and 2ddy i NUM features of template NUM
in fact the principle of maximum entropy does not directly concern itself with the issue of feature selection it merely provides a recipe for combining constraints into a model
we now describe a maximum entropy model that assigns to each location in a french sentence a score that is a measure of the safety in cutting the sentence at that location
in the second part of this paper we described several applications of our algorithms concerning modeling tasks arising in candide an automatic machine translation system under development at ibm
as the algorithm proceeds more and more constraints are imposed on the model p bringing it into ever stricter compliance with the empirical data x y
this is useful to a point insofar as the empirical data embodies the expert knowledge of the french segmenter we would like to incorporate this knowledge into a model
in general lines drawn between corresponding lexemes in a french sentence and its les neo democrates ont aussi paris de general motors dans ce contexte
the translation of a word occurring at the end of a french sentence is likely to occur towards the end of the english translation
the output of the decision procedure was a model of word correspondences between the two halves of the training corpus a translation lexicon
an automatic ewluation technique such as bible should be used to gauge the effectiveness of any mt system which has a lexical transfer component
filters based on word alignment patterns will only be as good as the model of typical word alignments between the pair of languages in question
even so given a test corpus of a reasonable size it can detect very small differences in quality between two n best translation lexicons
figures NUM and NUM show mean bible scores for precision of the best translations in lexicons induced with various cascades of the four filters discussed
these corpora usually contain more grapheme transitions which give greater detail about the spelling mechanism of the language and provide the most efficient training possible
we get a perplexity NUM for a general chinese corpus with NUM NUM million characters NUM
in this paper we present an iterative procedure to build chinese language model lm
however the construction of a chinese lm itself requires word boundaries
baltimore md21218 usa xiao j hu
the results indicate that the impact on segmentation accuracy would be small
we ran our computation intensive procedure for one iteration only
we segment chinese text into words based on a word based chinese language model
deg we present an iterative procedure to build a chinese language model lm
our first attempt is to see how accurate the segmentation algorithm proposed in section NUM is
the subsequent subdialogues intended to improve the route were interpreted to be extensions to the route causing the route to overshoot the intended destination
we of course also fail to identify by the methods just described given names used without their associated family name
2deg knight s grades on the content organization and correctness dimensions did not differ significantly from the biologists table NUM
on the overall rating and on each of the dimensions knight scored within approximately half a grade of the biologists table NUM
more generally they suggest that we are beginning to witness the appearance of computational machinery that will significantly broaden the bandwidth of human computer communication
first after the domain knowledge has been represented a discourse knowledge engineer will develop edps that are appropriate for the new domain and task
moreover although edps are effective for generating explanations achieving other communicative goals for example correct misconception may be beyond their capabilities
for example if the expressions in an edp s inclusion condition are not satisfied knight can not create a plan to satisfy them
listtree is the primary class for implementing the tree visualization
we will describe the procedure for collecting evidence by using the example mentioned previously e t tj
the final result of jma is then passed to a cfg parser which calculates the cost of possihlc structures and the attribute values attached to each node in a solution
experiments were carried out using NUM compound nouns NUM for NUM kanji words NUM for NUM kanji words NUM for NUM kanji words and NUM for NUM kanji words
this evidence contains not only correct examples such as as l oc
we assume that the structure of a compound noun can be represented in the framework of binary tree grammar by using attribute wdue pairs
the first line indicates the number of samples for which the correct dependency structure was given as the single minimum cost solution
we used the articles contained in nikkei shinbun for january and february in NUM as the corpus for the experiments
the segmentation is preserved as document annotations
the information gain does not depend on the selected thresholds since it acts on all the probability values and it is related to the complexity of the learning task
however we know from the experiments in section NUM that the competence that we are using shallow nlp and statistical operators is insufficient to cope with highly repetitive ambiguities
the values in NUM shows that the average mi is close to the perfect correlation NUM and has a small variance especially in the enea corpus that is in technical style
the global plausibility of the syntactic collocates i imposta di persona tax of people and ii reddito dipersona income of people is i NUM NUM and ii NUM NUM
during the test phase the objective is to evaluate the ability of the system at separating within each colli null sion set correct from wrong attachment candidates
a significant improvement measured over the testset NUM to NUM relative increment is shown by fig NUM b as a result of the learning steps
after the first scan of the corpus by means of the ssa grammar the corpus is re written as a set of possibly ambiguous collision sets i.e. if c is the corpus and
NUM NUM collision sets were found in the enea corpus and NUM NUM in the ld NUM figure NUM plots the percentage of colliding esl s vs the cardinality of collision sets
must present the declaration of which at comma 4th of item NUM relatively to the payment done and the profit distributed in the year NUM within april NUM
this phenomenon is likely to be more relevant in sublanguages medicine law engineering than in narrative texts but sublanguages are at the basis of many important applications
in particular a document store provides operations to create modify and navigate document collections
the rammar a feature based context fl cc phrase st ructtjre gratmnar is related to the ibm t nglish h ammar as l ul lished in blac k
parsc lcb solil ellc c s rolll l he a ll t treebank aud originally l rom a thitwse i ake oul food li r
therefore we are provisionally excluding this NUM of the treebank about NUM NUM words fi om use fbr parser training though we are experimeating with the use of the entire treebank expected tagging error rate NUM NUM for tagger training
a shaded cons it tent no h itidicates that there ar nicnu listhlg tho all el nat w analys s atly of which atl b disl laycd l y s tecl hlg t he al l ropriat e ill hill il etu
part of speech tags are assigned in a two stage process a one or more potential tags are assigned automatically using the claws hmm tagger b the tags are corrected by a treebanker using a special purpose x windows based editor xanthippe
overall the rationale for seeking to take as broad as possible a sample of current standard american english is to support the parsing and tagging of unconstrained american english text by providing a training corpus which includes documents fairly similar to almost any input which might arise
first we provide a static description with a a discussion of the mode of selection and initial processing of text for inclusion in the treebank and b an explanation of the scheme of grammatical annotation we then apply to the text
the idea informing the selection of documents for inclusion in this new treebank was to pack into it the maximum degree of document variation along many different scales document length subject area style point of view etc but without establishing a single predetermined classification of the included documentsj differing purposes for which the treebank might be utilized may favor differing groupings or classifications of its component documents
parse s pre st ecificd by a human trecbanker figures among the parses i iroduced for any given sentence by tile a i r ralnlnar roughly NUM of the time or l ext o1 the unconsl rained wide open sol l that tim tree dahk ix onil oscd of
a major use of the tool is for comparison of different semantic theories and methods of semantic construction
it is also intended to integrate a wider range of grammars parsing strategies and pronoun resolution strategies
indeed n1 p itself may have variegated ways of contributing to the mix of learning resources by systems that use different modules of nlp and that aim to foster various kinds of language knowledge in the student
interactiveness of an its can include its immediate responsiveness by faithfully updating the visible situation after a student makes some kind of move as well as longer range responsiveness to an individual student based on a model that it builds of that student s knowledge
the intent is that new words phrases and grammatical usage will become comprehensible through meaningful exposure and use
it runs like this intelligent tutoring systems its with simulated problem environments are potentially excellent for learning a key module of an its is a computational model of expertise in the domain nlp systems are such models in the domain of hmnan languages so let s use nlp knowledge bases as the expert module of a foreign language its
even for explicit teaching of grammatical lexical or other knowledge of a particular language scw is flawed
fluent NUM is a language learning and tutoring system helping students learn and letting teachers influence what is learned
i ll subdivide each mention some interplay among them and comment on fluent NUM in light of them
for the most part the nlp based work has been at the low end rarely going above syntax
the lower figures reported in that paper are due to a test procedure
most probable tree in data oriented parsing and stochastic tree grammars in proceedings
a non terminal on the frontier is called an open tree ot
the reduction preserves answers the proof concerns the only two possible answers
a left most derivation NUM m d is a sequence of left most substitutions
or convenience derivation in the sequel refers to NUM m
for the elementary trees of our example see the top right corner of figure l
NUM let q denote a threshold probability that shall be derived hereunder
sthis is a realistic figure from experiments on the atis
in this phase classification files of the inflection of polish words as well as the coding of polish and english inflection paradigms were prepared
the modified method performed better than the original lexical association scoring function but it still only obtained a median accuracy of NUM
if there are two categories occurring in equal proportions on average the coders would agree with each other half of the time each time the second coder makes a choice there is a fifty fifty chance of coming up with the same
qlfs at all stages of resolution are interpreted by a truth conditional semantics via a supervaluation construction over the compositions meeting the description
models variable assignment flmclions generalized quantifier interpretations and the qlf definitions for the connectives abstraction and application etc see appendix carry over unchanged
ie l present address speech research unit dra malvern st andrews road great malvern worcs wr14 3ps uk croueh0signal dra
base case for so with nonrecursive values of grammatical functions show y t a w e
the approach presented in this paper uses the finite state recognizer built to recognize the regular set but relies on a very efficiently controlled recognition algorithm based on depth first searching of the state graph of the recognizer
in fact our results show that it was not possible to single out a primary accommodator in the human interpreted setting either in terms of proportion of words used first or the frequency with which words in common were used
in the orthography application the configuration space is the set of possible english words represented as finite linear graphs labeled with ascii characters
we will refer to this latter setting as the machine interpreted setting keep in mind however that translation was actually done by trained interpreters mimicking a computer based system NUM the experimental configurations are shown in figure NUM
this will expedite problem identification and reporting facilitate direct user support and in general provide a more structured and consistent operational environment
a form NUM document is a form NUM document plus anything done to it before the end user s site receives it
viewed in this context a particular natural language is one of many possible sets of conventions used to convey information within a document
in short the tipster application must undergo a preliminary design review pdr and a final operating capability foc review
because changes and improvements to the architecture are expected the architecture will be under version control administrated by the tipster configuration control board
from the point of view of the architecture only one tipster module is involved and only its interfaces must conform to the icd
the existence or nature of sub modules is irrelevant for the architecture as long as the module itself accepts tipster compliant input and outputs tipster compliant output
the researcher who is considering implementing a new idea will thus be able to determine whether or not a similar tipster module already exists
over the long term it is envisaged that multiple applications will bc able to use a set of common persistent knowledge repository items
statistical modeling addresses the problem of constructing a stochastic model to predict the behavior of a random process
in constructing this model we typically have at our disposal a sample of output from the process
one component of this word reordering step deals with french phrases which have the noun de noun form
during the iterative model growing procedure the algorithm selects constraints on the basis of how much they increase this objective function
here NUM is the space of all unconditional probability distributions on three points sometimes called a simplex
suppose that we are given n feature functions fi which determine statistics we feel are important in modeling the process
in section NUM we give an overview of the maximum entropy philosophy and work through a motivating example
up to this point we have proceeded by assuming that the first task was somehow performed for us
one way candide captures word order differences in the two languages is to allow for alignments with crossing lines
the derivation of NUM uses an urn model in which words are sampled with replacement
as expected both NUM and the parametric smoother reveal the characteristic overestimation pattern
first consider how accurately we can estimate the vocabulary size of the population from the sample
to this end i carried out a short series of experiments of the following kind
nevertheless we again observe a consistent trend for the expected vocabulary size to overestimate the actual
from trouw NUM tokens representing NUM types were extracted among which NUM hapax legomena
the dotted line is a least squares regression the solid line a nonparametric scatterplot smoother
according to a least squares regression dotted line there is a significant increase in
in such an approach however the size of the text slice is of crucial importance
in sequences of sentences words are more likely to be reused than expected under chance conditions
in particular the paper focuses on creating abstracts of japanese newspaper texts
on the average each test had about NUM subjects assigned to it
we have seen how human reliability can affect the performance of automatic abstracting
for each text set the size of the majority to NUM
figures in parentheses axe the number of texts with a given threshold
the row represents k thresholds and the column represents text types
however little attention has been paid to reducing the computational expense of ocr
y and n represents classes yes and no respectively
we used scanworx ocr xerox hnaging systems NUM for the ascii encoding
df w is the number of sentences in the text which have an occurrence of w
the number of extractions varied from two to four depending on the length of a text
we conducted experiments with humans to collect data on how they perform on the sentence extraction task
the corpus is developed through human judgements on possible s mmary sentences in a text
the value is continuous and determined slmil ly to the location attribute above
figure NUM histogram of relevant context features for part of speech tagging
figure NUM incorporating the recency bias using a right to left labeling
NUM return the k highest scoring cases plus any ties
we use a simple majority vote and break ties randomly
table NUM additional results for the subject accessibility bias representation
null the remainder of the paper is organized as follows
NUM let the retrieved cases vote on the value of the antecedent
compare the first and second rows of results
unfortunately incorporating the restricted memory limitations into the case representation is problematic
liceneing restrictions preclude tile distribution of muc scoring tools with gate but shetfield may arrange for evaluation of data i rodu ed by other sites
similarly the natural language engineering nle l community has identified the potential benefits of reducing repetition and work has been flmded to promote reuse
this paper describes gate a general architecture for text engineering which is a freely available system designed to help alleviate the problem
a substantial amount of work has already been done on architecture for nle systems and gate reuses this work wherever possible
tuning le system resources to new domains is a current research issue see also the lre delis and ecran projects
ber of projects with similar directions one of the latest examples of which being elra tile euro i ean language resources association
much progress has been made in the provision of reusable data resources for natural language engineering such as grammars lexicons thesauruses
gate can not eliminate the overheads involved with porting le systems to different domains e.g. from financial news to medical reports
adding a semantic processor to complement a bracke ting parser in order to produce logical form to drive a discourse interpreter
we make use of two types of co occurrences word co occurrences within each language corpus and corresponding co occurrences of those in the parallel corpus
a near miss means that the pair is not perfectly correct but some parts of the pair constitute the correct translation
we hope to acquire better translation patterns by combining the current results with our work of structural matching for finding out fine grained correspondence
although the np recognizers detect about NUM distinct noun phrases in both languages the correctness ratio of the total data is not reported
num of words the results show that though dice coefficient gives a slightly better correctness both methods do not generate satisfactory translation pairs
the system ranks ten candidates of translation results for the user
the set of selected translation rules is called the initial population
these common and different parts are used as translation rules
first a user inputs a source sentence in english
in this paper japanese words are written ill italics
fable NUM results of expei imeuts using genetic al
and we have confirmed that the method requires many translation examples
the fitness value is calculated by tile fitness fimction as follows
figure NUM outline of the new inethod of machine translation
figure NUM example of how the translation result is produced
fill rules are a collection of criteria which describe the constraints used to select information for template slots and the conditions under which template objects are instantiated
other document attributes may be specified by the application by the operating system by the installation by the user group and by the user
these criteria may be provided as a fill rules document or as a sufficient number of examples of correct fills with context obtained from sample text
certain categories of documents shall be processed at higher priority than others if required by the application for example flash messages before routine messages
the detection and extraction components may provide an estimate of the component confidence level about each document selected or each piece of information that has been extracted
the architecture shall allow documents to be processed in any human language provided the appropriate knowledge bases have been built and language specific modules are available
the document management area is also closely related to detection and extraction by providing documents for these two areas as well as performing document accounting and control
extraction component is a component that selects information from a document or a list of documents based upon the template structure fill rules and patterns
the architecture shall support the application security requirements of the organization responsible for the application for example security labels on processes and or data items
a linear observed time statistical parser based on maximum entropy models
figure NUM confirms our assumptions about the linear observed running time
the no and yea actions of check correspond to the shift and reduce actions respectively
this leads to a preponderance of cases where pause and cue propose a boundary but where a majority of humans did not
the evaluations in this section allow us to compare the utility of two tuning methods error analysis and machine learning
this can lead to higher or lower performance higher because the training examples are more homogeneous representing only cases where the words have the same part of speech lower because there may not be enough training examples to learn from
thus the potential boundary site is classified as boundary and the phrase is taken to be the beginning of a new segment
second to estimate the p flwi terms rather than using a simple mle it performs smoothing by interpolating between the mle of p flwi and the mle of the unigram probability p f
as discussed above there is no training data for the algorithms in this section which are derived from the literature
the ratios of test to training data measured in narratives prosodic phrases and clauses respectively are NUM NUM
feature NUM includes the tag poss i et for possessive determiners his her etc and matches for example the sequence in his NUM in NUM he made an entry in his diary
a total of NUM boundaries are eliminated in NUM of the NUM test narratives out of NUM in all NUM
figure NUM shows the result when check looks at the proposed constituent in figure NUM and decides no
llowever a cck i is generated automaticmly and does not rely on prin itives but on an unlimited number of concel ts showing objects persons and actions interacting with each other
the ability of humans to reliably code linguistic features similar to those coded in figure NUM has been demonstrated in various studies
in both the correct and corrupted conditions tribayes scores are mostly higher often by a wide margin or the same as word s in the cases where they are lower in one condition they are almost always considerably higher in the other
examples for the confusion set lcb dairy diary rcb include NUM milk within NUM words NUM in poss det where NUM is a context word feature that tends to imply dairy while NUM is a collocation implying diary
table NUM compares the actions of build and check to the operations of a standard shift reduce parser
type b errors misclassification of nonboundaries were reduced by redefining the coding features pertaining to clauses and nps
the extensional definition of clausal interjections was expanded thus certain utterances were no longer classed as ficus under the revised coding
we experimented with two methods of automat really learning functions for combining our six scores into one composite score namely a genetic progranmfing approach and a neural net approach
thus we want our learning approach to learn not only which factors are important but also to what extent they are important and under what circumstances
in this paper we discuss how we apply discourse predictions along with non context based predictions to the problem of parse disambiguation
the probabilities of the parse actions induce st atistical scores on alternative parse trees which are then used for parse disambiguation
the parser always chooses the indelinite reference meaning since the vast majority of training examples use this sense of the word
we assume that knowledge from different sources provides different perspectives on the disambiguation task each specializing in different types of ambiguities
because t he spontalleous sche lding dialogues NUM re unrestricted ambiguity is a major problenl in enthusiast
when the parser produces more than one iljl for a single sentence it scores these ambiguities according to three diti e rent
because a large flexible grammar is necessary to handle these features of spoken language as a side effect the number of ambiguities increases
the system urrently requires sicstus NUM plus cl version NUM d and l k w rsion NUM NUM or later versions lit is awfilablc at the ftp address ftp coli uni sb de pub fracas
there are some small costs due to indirection instead of calling e.g. a reducer directly a program first calls a routine which chooses the reducer according to the parameters
two attrilmtes of tel tk which were important lbr this applieattion were the l rowision of translation routines from graphic canvasses into postscript allowing generation of diagrams such as figures NUM to d and the ease of providing scaling routines for zooming
because a tutorial system of this kind has to be based largely on standard routines and algorithms that are fundamental for the area of computational semantics a secondary aim of the project was to provide a set of well documented programs which could form the nucleus of a larger library of reusable code for this field
it also provides the possibility for the teacher to provide interactive demonstrations ami to produce example slides and handouts
graphical structures are described using a les ril tion stritlg a plain text hi erarchical description of the object to be drawn without any exact positioning information l or example the following tree which ajlow tim user to perform actions by clicking on mouse sensitive regions ill the display are
s app NUM NUM np vp id id pn v f i anna laughs anna xa laughs a c man c j ioves c j woman j vc man c 3j ioves c j woman j
the pre spl reflects this choice as follows NUM lexicallzation of the entities this is traditionally considered to be the task of the lexical choice process
the test set was restricted to those cases where f NUM nl NUM f NUM
two human judges annotated the attachment decision for NUM test examples and the method performed at NUM accuracy on these cases
the decision is then if NUM 11v nl p n2 NUM NUM choose noun attachment
there the task is to estimate the probability of the next word in a text given the n l preceding words
an approximate upper bound is NUM NUM it seems unreasonable to expect an algorithm to perform much better than a human
typical examples would be if p ofthen choose noun attachment or if v buy and p for choose verb attachment
moreover if the NUM tuples in the above table were ranked by accuracy the top NUM tuples wouhl be the NUM tuples which contain a preposition
another obvious method of combination a simple average NUM gives equal weight to the three tuples regardless of their total counts and does not perform as well
another major goal is the construction of parallel analyses for sentences of the same type in german english and french
a postnominal single genitive is interpreted as agent if the head noun is derived fronl an intransitive and as a patient theme if derived from a transitive
claims that there exists a dee t difference in the pr lcb lcb lieational structure of auxiliaries like will and have and the l ench aura
ih re the gen1 and gfn2 functions of the germml fstructure have to be related correctly to tile sulij and obj functions of tile english f structure
since mt is based on f structu es within patt iram the argument structure has to be present at this level of representation
however if two genitives occur as in NUM the prenominal genitive is restricted to an agent and the postnominal one to patient
however it is not immediately clear how an abstracting program trained on such untrustworthy data would perform
the approach developed for multiple genitive nps provides a mor at straet language independent representati m of genitives associated with nominalized verl s
however the algorithm can easily be extended to handle such constraints by including them in the predicate adjoin px zx
expressing a pattern of up to contiguous elements
tribayes compares quite favorably with word in this experiment
the confusion sets appear in table NUM
both effects show up in table NUM
we will also refer to the incorrect condition as the corrupted condition
por each substitution it calculates the probability of the resulting sentence
it selects as its answer the word that gives the highest probability
such corrections require contextual information and are not handled by conventional spelling programs such as unix spell
after working through the whole training corpus bayes collects and returns the set of features proposed
ewe confirmed this by running bayes without context words i.e. with collocations only
in the same tags condition however trigrams performs only as well as baseline
a hybrid method called tribayes is then introduced that combines the best of the previous two methods
the three morphemes produce the underlying form katab which surfaces as ktab since short vowels in open unstressed syllables are deleted
the multiple outputs make it possible to apply a language model in sentence level for disambiguation at a subsequent stage
this would offer a choice between alternatives making it possible to find the best solution at a following stage
marginal totals sums for all values of some variables of the observed counts are used to estimate the parameters of the loglinear model the model in turn delivers estimated expected cell counts which are smoother than the original cell counts
this might help to address the performance gap between our models and human subjects that ha s been documented in the literature z a more ambitious idea would be to use a statistical model to rank overall parse quality for entire sentences
the tagger maximizes the probability of the tag sequence t t l t NUM t given the word sequence w wz w2 w which is approximated a s follows null
so a two step procedure wa employed a first time to obtain a set of rare words ms training data and again a second time to obtain a separate set of rare words ms evmuation data
the features first noun level aam second noun level use the same estimates in other words in contrm t to the split lexi al association method they were not estimated sepaxatcly for the two different nominaj attachment sites
by statistical language model we refer to a mathematical object that imitates the properties of some respects of naturm language and in turn makes predictions that are useful from a scientific or engineering point of view
thus a second more realistic series of experiments was perforlned that investigated different pp attachment strategies for the pattern verb noun1 noun2 preposition noun3 that includes more than two possible attachment sites that are not syntactically heterogeneous
in this paper we present an empirical argument in favor of a certain approach to statistical natural language modeling we advocate statistical natural language models that account for the interactions between the explanatory statistical variables rather than relying on independence a ssumptions
if such a model were to include only a few general variables to account for such features a lexical a ssociation and recency preference for syntactic attachment it might even be worthwhile to investigate it a s an approximation to the human parsing mechanism
instead of starting with the trivial model one can start with a smaller easy to produce model but one has to ensure that its size is still larger than the optimal model
in practice a constraint will be discarded before no further merge is possible otherwise the model could have been derived directly e.g. by the standard n gram technique
the first one is to use each word type as a state and estimate the transition probabilities between two or three words by using the relative frequencies of a corpus
to be exact we need for a n NUM b nxm double precision floating point numbers double precision floating point numbers double precision floating point numbers double precision floating point numbers double precision floating point numbers short integers for the meaning of NUM and b see the algorithm presented in the appendix
another problem is that compound words usually keep the initial pronunciation of their components e.g. in words such as whatsoever therefore etc this leads to many errors for an algorithm like the one proposed here which has no information about the origin and etymology of each word
a final consideration is the type of errors a dictionary based ptgc system introduces when it encounters a word that is not contained in the dictionary the system will produce the closest existing word in its dictionary as the best candidate which may give a completely incomprehensible if not wrong meaning to the input phrase
the columns have the following meaning d l s l w NUM NUM etc is lw x NUM where lw is the size of a word in error in characters is is the number of incorrect characters in the word and is lw x NUM is the mean value estimated over all wrong words
since at every time point the intermediate variable d t is calculated only from d t NUM we keep only two copies of dij one for t and one for t NUM finally the fact that only multiplications are involved in the processing of the conversion algorithm led us to transform the algorithm to use only additions
column l s shows that the percentage of erroneous symbols is very small indeed while column d shows that even though a word may be incorrect only a small percentage of its symbols may be wrong about NUM on average in exp NUM which proves that the output of the algorithm is very easily human readable even when it contains errors
in this manner the response time of the complete algorithm can be proportional to n NUM rather than to n NUM yielding a system that can serve as a module for any real time speech recognition system
finally as expected the model performed worse in experiments using as input a simulation of a speech recognizer output distorted speech than in the corresponding experiments using a correct phonemic representation of the words
furthermore the recency bias performs well in spite of the fact that the baseline representation already provides a built in recency bias
the dynamic programming in the context of alig nnent assumes fltat th previous selections do not interfere with the fllture decisions
edu unlike the problems of part of speech tagging and parsing where commonly utilized training and test sets such as the brown corpus and penn treebank have existed for a number of years evaluation of word sense disambiguation sytems is not yet standardized
examples of issues for discussion include how should part of speech level distinctions be treated when evaluating wsd systems
of course this still fails to differentiate many contexts beyond the scope of n grams while n gram models of language may never fully model long distance linguistic phenomena we argue that it is still useful to extend their scope
for that part of speech most frequently associated with the word we give a high weight with decreasing weight for the second most frequent part of speech and so on for the top four parts of speech
when the locally optimal two class hierarchy has been discovered by maximizing ml t whatever later reclassifications occur at finer levels of granularity words will always remain in the level NUM class to which they now belong
for example if we restrict our consideration to the two previous words in a stream that is to the trigram conditional probability estimate p wilwi then the sentences NUM a
some advantages of this last approach include its applicability to any natural language for which some corpus exists independent of the degree of development of its grammar and its parsimonious commitment to the machinery of modern linguistics
classification trees can be sectioned into distinct clusters at different points in the hierarchy each of these clusters can then be examined by reference to the distribution of lob classes associated to each word member of the cluster
s p wi i wi NUM wi l sp cs wi i cs wi NUM p wi i cs wi NUM
routing retrieval and filtering experiments using pircs by k l kwok and l grunfeld is a manual modification of the automatic queries in pircsl
these topics were much shorter than any previous trec topics an average reduction from NUM terms in trec NUM to NUM terms in trec NUM
what special problems exist when evaluating wsd performance in a multi lingual setting
thns it becomes clear that the scalar non scalar dichotomy and not the attributive predicative distinction which dominates the literature is the single most important distinction in semantic treatment of adjectives
the difference between file two is essentially in the position of varl in the lex mai and ill the scope of atlribntion of the two attitudes inherited from file verbal entry
what is abusive is either file event e itself as ill abusive speech or abusive behavior or the agent a of the event its in abusive man or abusive neighbor
the langtmge in which these representations arc expressed is called file text meaning representation i mr langlmge mid texts in dais language are called simply tmrs
natla auy file adjective entries replace the verbal syn struc below wifll the standard adj one see NUM above for more data and discussion see also raskin and nirenburg NUM
in other words while we continue to discover more specific relations between the lexical entries of denominal adjectives mid the nouns they are derived from file generic pertain to property should not be discarded
most adjectiwd meanings of one language are however expressed adjectiwdly as well in another language and the lexical entry for this me ruing is then unchanged
the work on adjectives reported in tlfis paper constitutes a descriptive microtheory in the mikrokosmos semantic analyzer onyshkevych and nirenburg NUM and beale et d
but there are many other adjectives which use exactly the same type of lexical entry and their similarity to each other and to fake had not been noticed before
for abstract references to size lhe fillers in english e m ire and scale value for an adjective as a prolrerty valuc pair in the frame describing the meaning of the noun the adjective memifies
rules allow and control the discrepancies between the abstract words in the lexicon and the surface words being
the intersection of this root with the pattern cacac yields the stem daras ty NUM
figure NUM full system with two levels of r ules
NUM it had to be able to analyze arabic words as they appear in real texts
the result is equivalent to the original lexical transducer described in figure NUM
the arabic system is however substantially slower than the
work began in NUM to convert the analysis to thc xerox fst format
any arabic root at combine legally with only a small subset of the possible patterns
hand compilation of a complex rule which can easily take hours is a real disincentive to change and experimentation
since these methods take all the components of compound nouns as index terms without evaluation irrelewmt terms can decrease retrieval precision
more accurate measure woukl be made using a tlidden marker model that is about a stochastic process of fun lion words
tleeause the nominals o itnlllnber lcb tnd grow faster than other categories of words it is more elticient to halidle non nominal words mamlally
NUM ldcni ify co ill el lid iioliiis llsillg lioitthtm diel iona ry
in general the existing methods for compound noun analysis have been focused mainly on recall performance with little attention to the precision
compound nouns tend to carry more specific contextual information than simple nouns thus they are likely to contribute to the retrieval precision
it is very difficult to provide an t lcb systeni with the suf icient list of iotllls
simple nouns are by definition those that do not have any of their substrings as a noun according to the dictionary
in our example which starts from a relatively specific tsl the content delimitation module does not make visible changes all roles present in the pre spl are determined to be realized
unlike this work which builds up spl expressions anew using the text plan as a source of information to guide processing our sp transforms the text plan itself into the spl
in which the intermediate role situation is removed pnd its filler moved to occupy the head of the fragment rooted at x
NUM the intermediate steps of the sp should be accessible and easily interpretable to people building the sp to enable cross module interconnection and debugging
at any point in the network the selection of a feature with an associated tree rewriting rule causes application of that rule to the current pre sp5
in the dynamic case we are combining compatible knowledge sources i.e. they share the a semantic tagset
however in many language engineering applications manually tagged corpora are not available nor easily implemented
to expand its meaning we want to look at the important concepts involved and use their respective temporary graphs to extend our initial graph
in our example pen and crayon have a common subgraph write inst
non consecutive word strings as in the second example
we now describe an experiment to measure these phenomena in corpora
instead ambiguous patterns are highly repetitive especially in sublanguages
a straightforward measure of recurrence is provided by the average mutual information of colliding esl s
rather we believe that the size of the corpora may be in fact too small
produces in general a noise prone set of esl s some of which represent colliding interpretations
this phenomenon is known to be typical in sublanguage but was never analyzed in detail
addressing the reusability of annotation schemes for particular domains one will have to consider if they can be just added to existing morpho syntactic annotation schemes as we described in the example above or if the annotation work should be started from scratch which could be necessary for more complex applications
NUM expresses the following genermisation when the w rb pi endere co oecurs with a noun having luogo as a h yperonym then this noun is interpreted as the object
the bilingual dictionary is designed to give appropriate correspondence words to the headwords contained in the word dictionmy in machine processings
replacing probabilities of the fbrm d ilj NUM m with relative distortion is a feasible alternative
in section NUM we compare sensealign to several other approaches that have been proposed in literature involving computational linguistics
ii of a car or aircraft to move ith one side higher than the other esp
ii a placc in which money is kept and paid out on demand and where related aetmties go on
in our second experiment we use sensealign described above for word aligmnent except that no bilingual diclionary is used
the corpora provide us with training and testing materials so that empirical knowledge can be derived and evaluated objectively
the above cited work s discussions of the z2 1ike statistic and the fan in factor provide a valuable reference for this work
this paper has presented an algorithm capable of identit ing words and their translation in a bilingual corpus
according to our results although dictalign produces high precision alignment the coverage for both test sets is below NUM
table i presents the ten rules with the highest applicability acquired from the example sentences and their translations in lecdoce
the concept classification dictionary a subdictionary of the concept dictionary describes the similarity relation among concepts listed in the word dictionary
they were clearly relevant to a single category and much less relevant to the other categories
the difference increased with progression of n n NUM NUM NUM
most multi word lexemes mwls allow certain types of variation
while certain mwls only occur in exactly one form e.g.
while we save a computational expense we lose some information which original document images have
in the mwl may undergo certain inflections
formal description of multi word lexemes with the finite state formalism idarex
resultant topic categories are affirmative action internet stock market local traffic NUM
the average size of the original documents was NUM and ranged from NUM to NUM NUM words
shape token based and the ocr based approach number of correctly assigned documents number of test documents
this provides us with the possibility to express certain things in a very compact way
modification one of the mwl s constituents can be modified preserving the idiomatic meaning
the discrepancy can be sent back to the developing contractor and the government contracting officer s technical representative cotr with a request that the discrepancy be cleared up
the pm is responsible for approving the tailoring of cm policies practices and procedures to ensure that adequate methods are implemented for identification and control of the tipster architecture
the cm organization is responsible for identifying the specific items that define the architecture and will be configuration controlled as well as when and how they are to be controlled
for class i changes the erb assigns preparation of a request for change rfc to the responsible groups for submittal to the ccb for their review and disposition
the discrepancies will be submitted to the ccb for disposition which can be one of the following an rfc can be initiated to incorporate the discrepancy as a change to the architecture design document
the tipster ii se cm project employs a configuration management organization to establish cm procedures oversee the application of these procedures by all team members and provide all necessary reports and support for the cm function
the cmm in performing the foregoing responsibilities will have the independence and authority to coordinate and communicate cm functions with management and technical personnel involved in the architecture effort
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
on rare occasions submissions may come from the se cm and will be reviewed by the cawg or other person s as designated by the architecture committee and approved disapproved by the architecture committee
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
adding the feature f allows the model paul to better account for the training sample this results in a gain al f in the log likelihood of the training data
we replace the computation of the gain al s f of a feature f with an approximation which we will denote by al s f
in the first example the system has chosen an english sentence in which the french word sup ieures has been rendered as superior when greater or higher is a preferable translation
since template NUM features are independent of x the maximum entropy model that employs only constraints derived from template NUM features takes no account of contextual information in assigning a probability to y
for example we might discover that the expert translator always chooses among the following five french phrases lcb dans en l au cours de pendant rcb
given a set of statistics the second task is to corral these facts into an accurate model of the process a model capable of predicting the future output of the process
in the example we have been considering each sample would consist of a phrase x containing the words surrounding in together with the translation y of in that the process produced
features shown here were the first features selected not from template NUM verb markerj denotes a morphological marker inserted to indicate the presence of a verb as the next word
as long as each new constraint imposed allows p to better account for the random process that generated both pr and ph the quantity l h p also increases
our analysis of the french vp also provides an account of past partieilfle agreeinent
this is of course unacceptable as the cgf comprises NUM different lexical id rules
the above metarules apply either to the verbal complex or to the complements thus reducing considerably the combinatorics
if the type feature is simply passed from one category to another as it is for example on the np rule given earlier then the selector feature must likewise be coindexed on the two categories
unfortunately there are some limitations on the amount of subcategorization information that can be expressed by the resulting technique in particular categories have to be represented by atoms which is an inconvenient limitation
while not essentially changing the weak generative capacity of a cfg the use of kleene operators does change the set of trees that can be assigned to sentences n ary branching trees can be generated directly
compilation of these apparently richer devices into expressions that can be processed just using unification will generally allow the grammarian to use them freely without necessarily sacrificing the advantages of efficiency that pure unification systems offer
if a member of the tuple can not precede or follow the current category we put a no in that position of the tuple otherwise we leave it uninstantiated
in this case and similar ones the description in terms of type inheritance would be regarded as capturing the facts in a more natural and linguistically motivated way
although npl np2 is logically equivalent to np2 g npl the selector list will not allow the second ordering to be found because this would involve an attempt to unify npl with np2
on lexical entries for subcategorizers the subcat value can be stated as a simple list of possibilities subcat np np pp etc
after compilation these NUM id rules expand to NUM expanded id rules and NUM phrase structure rules
then for each feature value equation construct for the value an n i vector whose first member is NUM and whose last is NUM and where all the other members are initially distinct variables
where n headi stands for the number of patterns containing ha i n headl head2 stands for the number of the patterns containing both hem and head NUM the value of accum cost of each leaf node is set to NUM
the dialogue manager is responsible for interpreting the speech acts in context formulating responses and maintaining the system s idea of the state of the discourse
if a grammar contains phonologically empty elements traces relativizers and the like the set of ill formed signs will be infinite because wird i could be combined with arbitrarily many empty elements
in table i a stands for a given key b stands for a sequence of kanji characters we only treat kanjicompound nouns in this paper and d stands for an extended delimiter d is identical to a space a symbol a katakana or a hiragana except c no o3
to classify the evidence we developed the following rules r a if l the length of a is NUM and the length of b is l md NUM there is no entry for the concatenated string ab ba in the dictionary used by jma then recognize the concatenated string as an unregistered word and apply r c
having multiple lexical entries for the same word is a form of disjunction and all forms of disjunction entail increased nondeterminism leading to inefficiency in analysis
the fact that the algorithm scans the input word linearly once from the beginning to the end means that it can work in parallel with other modules of speech recognition systems and produce output with a very short delay after the end of the input
in the latter case the look up module employs a distance threshold when the difference between the input and the words in the dictionary is greater than this threshold control is passed to the hmm system which converts the input phoneme string to graphemes
nevertheless languages with diphthongs or double letters can not benefit from this method since it creates long lists of homophonic candidates that are all correct in the sense that they are pronounced as the input word but that do not exist in the language
aside from the problem of storage the computer has to execute the inner part of the second order multiple output conversion algorithm n NUM x e x t NUM times the average length of a word is about t NUM i.e. NUM NUM NUM times per word
as can be seen in the equations of the appendix the algorithm is highly parallel since the values of djk t are independently computed from the values of dij t NUM this means that these calculations can be performed concurrently
two areas were selected for possible advancement first to make the system contain more detail in the modeling of the language and second to use a system that could produce more than one output solution for each phonemic input homophones
the columns show the density i.e. the number of nonzero elements of the respective model parameters initial hidden state probability vector r initial hidden state pair probability vector p observation symbol probability matrix b and hidden state transition probability matrix a
if one of the globally e best hidden state sequences that starts at t NUM and ends at t t passes from state si at time t then it must have one of the members of qt e as part of the path from time NUM to t
the second very interesting feature revealed in tables NUM to NUM is that the improvement in the system s performance decreases rapidly from the first to the last position of the output which means that the majority of correct suggestions is included in the first two positions
unfortunately the treatment so far will still allow fewer than three complements to be found even where another is needed for the sequence to be grammatical
used a set of NUM symbols for allophones which we adapted to the sampa standard requirements fourcin89 t
a statistical evaluation of manual and automatic segmentation discrepancies is performed so as to estinmte the reliability of automatically derived labels
in concatenation systems both the choice and the proper segmentation of the traits to be concatenated play a key role
as a result a fully automatic seglnentation of speech segments is hardly conceivable in the context of concatenation synthesis
most of the problems arise when detecting bursts of plosives as tile automatic procedure tends to shorten their closures considerably
figure NUM automatic above and manual center and below segmentation of the logatom inacu
the authors wish to thank 3bma erjavec tbr proofreading of the text and his usefld comments on the article
the situation improves when plosives are taken as a whole closures and bursts together
consequently the quality of the synthetic speech was considerably affected and we are therefore planning to record another diphone inventory
except for the topmost node all parse nodes are required to have some slot filling operation
obviously the prior probabilities p ft can be obtained directly from the training data
neither the parse tree t nor the pre discourse meaning ms depends on the discourse history h
next the probability p ms tiw can be rewritten using bayes rule as
the probability of a parse tree t given a word string wis rewritten using bayes role as
we now proceed to a detailed discussion of each of these three stages beginning with parsing
for example p class i first class of service npr is the probability
the n best of these theories are then passed to the second phase of the interpretation process
the discourse model is trained from a corpus annotated with both pre discourse and post discourse semantic frames
our probabilistic disambiguation system currently makes no use of lexical frequency information training only on structural configurations
in these cases it was clear that responses had been inadvertently misclassified so the system either misclassified the response also
another reason for multiple category assignment is to be able to provide content relevant explanations as to why a response was scored a certain way
such a scoring system has to be able to identify the relevant content of a response and assign it to an appropriate content category
responses do not appear show typical features of sublanguage in that there are no domain specific structures and the vocabulary is not as restricted
saw see or cut wood ball round thing or dance bank edge of river or financial institution
it is therefore interesting to investigate whether the system requires more or less training data than a tagger
the actual synset numbers of each level of synset groups are NUM NUM NUM and NUM
however we think it might be a more serious problem because many uses of nouns seem to have an anaphoric aspect i.e. the synset which best fits the real world object is not included in the set of synsets of the noun which is used to refer to the real world object
the current implementation has one type so which represents subject verb object relation
a heuristics rule using some fixed frequency threshold and a surface support bound are adopted in the current implementation
l here are a variety of similarity measures awu able for sets of atm es bill all make their eoml arisons t ase l on some combination of shared etltlh es dislillel eal ttres altd sharect ttl selll l ea
the most specific common abstraction msca method compares two concepts based on the taxonomic depth of their common parent for example dolphin and human are more similar than oak and human because the common concept mammal is deeper in the taxonomy than living thing
inherited features are in italh while disliuctive llalcnl h vu chiid falhcl male lave child iflothel female hove chihl NUM fig NUM fragment ot c mccptual taxonomy salutes are in bold
betwe m a penguin and a rot in will t e partially based on the fe ture ean ly assigned to the concel t bird ewm though it toes not apl ly it dividually to t et guins
inputs the surfa e tril le and select s lhe most l ausil le deep iril le based ou abstracted triple scores matched with the deep triple flm surface sulh octs iu l t hie NUM show the intuitne tendency that a suftlcient number of triple data will generate solid results
each once l has a set of features NUM each concept inherits features from its get ermizations hypernyms NUM u h concept has one or more listinctiw features which are not inherite l ft om its hy el nyllls
after several steps of merging it is no longer the same output but still mainly states that output words of the same syntactic category are merged
for a composite algorithm recall can not be increased if neither np pause nor cue found a boundary then no combination of them can
we tested all pairwise combinations and the combination of all three algorithms as shown in table NUM precision is the most likely metric to be improved
note that the error rate and fallout which in a sense are more robust measures of inaccuracy than precision are both much better than cue and pause
if any feature has a positive value no boundary is assigned if all have negative values ficui i ficui is classified as a boundary
and discourse segment boundaries grosz and hirschberg NUM hirschberg and passonneau and litman discourse segmentation if pause true then boundary statistically validated versus algorithmically derived boundaries
the recall of the three algorithms is comparable to human performance the precision much lower and the fallout and error of only the noun phrase algorithm comparable
two coders were asked to identify segments the core utterance of each segment and certain intentional and informational relations between the core and the other contributor utterances
however since each pronunciation can have multiple derivations the counts for each rule from each derivation need to be weighted by the probability of the derivation
the result of the forced viterbi alignment on a single sentence is a phonetic labeling for the sentence see figure NUM for an example from which we
for this reason the words found only in the limsi source lexicon did not participate in the probability estimates for the syllabic and reduced vowel rules
one goal of our rule application procedure was to build a tagged lexicon to avoid having to implement a phonological rule parser to p rse the surface pronunciations
that is for each of the NUM phones in our phone set we compute the probability that the slice of acoustic data was produced by that phone
then we use a speech recognition system on a large corpus of recorded speech to check how many times each of these surface forms occurred in the corpus
error analysis also led to a redefinition of infer and to the inclusion of new types of inferential relations that an np referent might have to prior discourse
the referring expression algorithm np performs better than the other unimodal algorithms pause and cue and a combination of pause and np performs best
in addition for t NUM NUM of the NUM test sets happen to contain no boundaries for these cases c NUM and thus the value of recall is also sometimes undefined
NUM NUM and u h they come over NUM NUM and they help him NUM NUM and you know NUM subject c NUM NUM help him pick up the pears and everything
to illustrate graphically the improbability of the occurrence of wide bars high consensus boundaries we also show a typical random distribution for a parallel data set in the right hand bar chart of figure NUM
we evaluate our method by using krippendorff s c to evaluate the reliability of boundaries derived from one set of subjects compared with those derived from another set of subjects on the same narrative
for example given the algorithm shown in figure NUM a generation system could better convey its discourse boundaries by constructing associated utterances where the values of corer infer and global pro
segment z well anyway so u m tsk all the pears are picked up and i he s on his way again figure NUM discourse segment structure and linguistic devices
in the aa perl script for analyzing bod s data is available by anonymous ftp from ftp ftp das harvard edu pub goodman analyze perl
null given the tree of figure NUM we can use the dop model to convert it into the stsg of figure NUM the numbers in parentheses represent the probabilities
since the probability of the most probable parse decreases exponentially in sentence length the number of random samples needed to find this most probable parse increases exponentially in sentence length
when using a chart parser as bod did three problematic cases must be handled e productions unary productions and n ary n NUM productions
fortunately it is possible to find an equivalent pcfg that contains exactly eight pcfg rules for each node in the training data thus it is o n
the original atis data from the penn tree bank version NUM NUM is very noisy it is difficult to even automatically read this data due to inconsistencies between files
bod himself does not know which technique he used for n ary productions since the chart parser he used was written by a third party bod personal communication
the second number is the probability that a test set of NUM sentences drawn from this database will have one ungeneratable sentence 75p NUM NUM p NUM
one alternative alignment method is the lexicon based approach that makes use of the word correspondence knowledge of the two languages
for small values of k we observe a significant divergence between e v n and v n
rise to substantial deviation between e v n and v n in texts with no overall discourse organization
for instance ahab appears to be a specialized word in text slice NUM but he is mentioned only in passing in text slice NUM
the preceding analyses all revealed violations of the randomness assumption underlying the urn model that originate in the topical structure of the narrative as a whole
in the next section i will argue that intra textual cohesion is in large part responsible for the general downward curvature of du k
in other words an approach to lexical specialization in terms of concentration of use is incomplete without a specification of the unit of concentration itself
to explore these two potential explanatory domains in detail we need a method for linking topical discourse structure and local topic continuity with word usage
the effects of nonrandomness are so strong that they introduce an overestimation bias in distributions of units derived from words such as syllables and digrams
since the expected growth curve and the observed growth curve are completely fixed and independent of k the former is fully determined by the fre
it may be useful to develop a formalism and procedure that bare the same relationship to the rosenkrantz procedure that tig and the ltig procedure bare to the gnf procedure
to see this consider the following suppose that there are a set of auxiliary trees t that are allowed to adjoin on a node in a tig
using an implementation of the earley style tig parser that was specialized for left anchored ltigs it was possible to parse more quickly with the ltigs than with the original cfgs
methods of converting cfgs into left anchored cfgs e.g. the methods of greibach and rosenkrantz do not preserve the trees produced and result in very large output grammars
therefore the only way that any element of t can be used in a derivation in g is by adjoining it on the root of an initial tree u
this yields the new nonterminal set ntl step NUM for each nonterminal ai include the following rules in p yi c and zi c
second when predicting a node b whose first child is a terminal symbol it is known from the above that this child must match the next input element
the only requirement when sharing nodes is that every possible way of constructing a tree that is consistent with the parent child relationships must be a valid elementary tree in the grammar
when the modifier in the input sentence satisfies both restrictions for a certain wllency element in the valency pattern for the predicate the modifier is botmd to that valency element
as shown in figure i valency elements are described using both the semantic restriction on the noun including the modifier and the restriction on the case marking pmaicles including the modifier
alt j e perfonns sentence analysis by binding the modifiers for the predicate in the input sentence to the valency elements in the wtiency pattern for the predicate in the wdency pattern dictionary
in contrast to lhis japanese language marks each case by a certain sort of postposilional parlicle located next to noun phvases sudl as ga or we
determining the valency structure in the type NUM preprocessing step in figure NUM the modifier with wa time reference is considered as an adverbial plwase
problem with type NUM cases fignre NUM shows the m alysis of the type NUM example zou wa hana ga nagai elephanls have long thinks
for any corpus of reasonable size we can find cases where a valid translation is missed because a part of it does not pass the threshold
these symbolic machine translation systems must have access to a bilingual lexicon and the ability to construct one semi automatically would ease the development of such systems
finally another criterion for selecting a similarity measure is its suitability for testing for a particular outcome where outcome is determined by the application
second type modality includes necessity such as nakereba naranai re t fc t ft3 NUM toc u and ta hou ga yoi t t NUM u which correspond to have to or must and had better should or preferably in english
there are identical tendenci between two adw rbial forms of adjectives k f and kute a NUM and two adverbial forms of pseudo adjectives ni and de r
ttere discourse means an inner sentence congruence in japanese long sentences thttt contain two or more predicates
ldg assumes that each conjunctive particle has a preference in modifying predicates or auxiliary verbs with consistent modmity
these levels construct t hierarchy i.e. at lower level clause caltlctofi subordinate a higher level one
ldg classifies the encapsulating powers of function words into six levels and modality in predicates into four types
from the viewpoint of modality there are four predicate types in japanese NUM auxiliary verbs of the first type modality conjecture etc NUM auxiliary verbs of the second type modality necessity etc NUM copula and NUM plain present and past tense forms of verbs
ldg can presume the inter clausal dei endency within japanese sentences prior to syntactic and semantic allalyses by utilizing tilt difdrences of the encapsulating powers each apailese function word has and by utilizing modification preference between function words and predicates that reflects consistency if lnodality reading or propositional attitude inte rpretation
the average pause length for each level was calculated for two separate cases words preceding a for words without a cormna marked with white bars in fig NUM the result shows that the higher the conjunction level is the longer the average pause length is except for lev NUM which is a particle for quotation
therefore in the present data a pause was inserted after the particle in only one in table NUM no level is assigned to two of tile most fl equent groups verbs and auxiliary verbs in adverbial tbrm and verbs and auxiliary verbs in tile same form with a conjunctive particle te c
error tolerant recognition enables the recognition of strings that deviate mildly from any string in the regular set recognized by the underlying finite state recognizer
this paper has presented an algorithm for error tolerant finite state recognition that enables a finite state recognizer to recognize strings that deviate mildly from some string in the underlying regular set
this algorithm called agrep relies on a very efficient pattern matching scheme whose steps can be implemented with arithmetic and logical operations
i would like to thank the anonymous reviewers for suggestions and comments that contributed to the improvement of the paper in many respects
it is most efficient when the size of the pattern is limited to NUM to NUM symbols though it allows for an arbitrary number of insertions deletions and substitutions
the approach is very fast and applicable to any language with a list of root and inflected forms or with a finite state transducer recognizing or analyzing its word forms
in the context of spelling correction error tolerant recognition can be used to enumerate candidate correct forms from a given misspelled string within a certain edit distance
the algorithm for error tolerant recognition is very fast and applicable to languages that have productive compounding or agglutination or both as word formation processes
it has been found that kml ai NUM iwk i x logf w where k NUM iwk il is the size of the segment results in a useful language model of this form
note that it is difficult to compare this result to results on wall street journal as the two corpora may be quite different
the algorithm can be applied to morphological analysis of any language whose morphology has been fully captured by a single and possibly very large finite state transducer regardless of the word formation processes and morphographemic phenomena involved
however the user interface that is concerned with screen layout windows command choices output displays and the sequence relationships between these items is closely tied to user information requests and user information outputs areas
user2 i want to fly from boston to denver
the architecture shall support tagging of the text strings in a document that caused selection of the document so that when the document is passed to the user interface for viewing highlighting or other notification may be applied
features developed as a result of the requirements specified in this document will generally but not always be initiated by the user through user interface commands that may be passed to the user information request for processing
in the near term templates objects and patterns that is the component specific structures which allow the extraction component to recognize slot fills shall be constructed combined by manual or semi automated processes and the appropriate libraries updated by normal maintenance operations
the architecture shall allow an application to define which parts of formatted or structured documents including document attributes summaries abstracts subject word delineated zones etc get displayed when a document is shown to the user
the architecture shall provide for the use of a common detection criteria library containing statements of user information needs as well as the associated application translated user understandable queries that can support various document processing tasks in different applications
starting from the abstract tsl intermediate roles situation would have been introduced in the fragments labeled by the variables w l f and r
at the present stage this includes the following NUM constituents of a predication con null stituents that are to be encoded in the pre spl
so far three major distinctions are made NUM marker no marker for example the condition relation can be marked by if in case etc
in this option we allow a module to replace an input with multiple alternative outputs instead of only one when it can not make a choice
we believe these constraints to be fairly general that is that sentence planners developed for applications other than healthdoc will have much the same structure
mation rules to pre spl expressions and when they match unifies all variables and replaces the matched tree fragments with the right hand sides of the rules
NUM this allows us to adopt an already developed and well known representation and the machinery that processes the information encoded in terms of this representation
this option is only feasible if the networks of the modules are not very detailed i.e. do not lead to an excessive number of alternatives
we can say that t has approximately linear order time complexity in practice
annotations shall be extensible in accordance with the architecture maintenance policy as the architecture matures
selection statements shall describe in free text form the kind of information the user requires
user information requests define the manner in which the application shall operate and perform its various tasks
a part of a case frame tree obtained by lasa NUM is shown in figure NUM
we can also observe that semantically close words are generalized by their common semantic code
to do this search efficiently we propose a new algorithm t
the nature of this requirement implies co operation between the operating system the application and the architecture
i b indicate matched korean phrase with ej b in current status and v j denote their matching weight
so far n2 and n3 in the wflcncy paltem have already been bound
iued as tna xinntm ntt nt er el words which c nsis of a phrase
as the parallel corpora become more and more accessible many researches based on the bilingual corpora are now encouraged that were once considered impractical
of various alignment options the alignlnent of word units is to compute a sequence of the matching pairs of words in a parallel corpus
therefore file modifier with wa is bonnd to tim subjective case n1
therefore the japanese double subject construction is also referred to as file wa ga construction
in alt j e japanese sentence analysis is performed based on lhe wdency slruclure for the predicalc of the input sentence
however lnmly japancse adjeclivcs and some verbs can dominate two surface subjective cases within a simple sentence
the integration of kankei with the trains parser needs to be completed
next one point crossovers are pertbrmed for likes and 9a suki desu
because the verbal complex is build before any nonverbal argument of a verb gets saturated it is possible to account for phenomena like auxiliary flip
however these methods require many translation examples to realize a practical and high quality translation
if necessary we can reestimate the word n gram probabilities by replacing p w and p w lw with f w and f wolw
the parameters of the modified pos trigram word unigram and word bigram are estimated by equations NUM NUM NUM and NUM in figure NUM
in the following experiment we decided to use word bigram as the segmentation model and the combination of the spelling model of all words and the length model of low frequency words as the word model
be defined as finding the set of word segmentation and parts of speech assignment v t that maximize the joint probability of word sequence and tag sequence given character sequence p w tic
initial estimates of the word frequencies were 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
however several languages such as japanese chinese and thai do not put spaces between words and so in these languages word segmentation word frequency counting and new word extraction remain unsolved problems in computational linguistics
first we count the number of unknown words in the corpus segmentation std the number of unknown words in the system segmentation sys and the number of matching words m
however it is impossible to apply the viterbi algorithm and the forward backward algorithm for word segmentation of those languages that have no delimiter between words such as japanese and chinese because word segmentation hypotheses overlap one another
the approach we take is as follows first we design a statistical language model that can assign a reasonable word probability to an arbitrary substring in the input sentence whether or not it is truly a word
the accuracy rate of translation increased fronl NUM NUM to NUM NUM by applying genetic algorithms
in the experiments without genetic algorithms crossover mutation and the selection process were not performed
ea results are better than np in table NUM or table NUM
the redefinition of ficu motivated by error analysis led to fewer clauses
no algorithm or combination of algorithms performs as well as this baseline
NUM note that the humans did not have access to pause information
or from NUM to NUM clauses avg NUM NUM
except in rare cases no subject segmented more than i narrative
the corpus contains just over NUM NUM prosodic phrases with roughly NUM NUM words
we use measures from information retrieval to quantify and evaluate our results
by using genetic algorithms the accuracy rate of translation increased from NUM NUM to NUM NUM
then the second branch is taken and the feature coref is tested
does not encode the boundary nonboundary classification of the particular site in question
a translation rule includes a japanese sentence or words corresponding to an english sentence or words
the insertion of the auxiliary into the vp structure to produce a flat vp is done by a metarule
this grammar is compared to a comprehensive computational grammar of english which was implemented using the same computer workbench
like to choose weights more judiciously
this is due to the differences in spelling in each language and consequently to the training the model required
figure NUM which plots the difference between the expected number of new types using
NUM these texts were obtained by anonymous ftp from the project gutenberg at obi std com
unfortunately dispersions deviate substantially from normality so that z scores remain somewhat impressionistic
as table NUM shows these rules achieved higher recall on the very hardest phrase type organization than their hand crafted counterparts albeit at a cost in precision
in effect the morphological transformations patch errors that were due to gaps in the lexicon and the contextual rules patch errors that were due to the initial assignment of a lexeme s most common tag
these largely arose as part of our information extraction efforts and have been either directly or indirecdy evaluated in the context of two evaluation conferences muc NUM and mffl for multilingual entity tagging
for a rule i that tests whether a phrase contains a certain lcxcme i wc construct an acccptor machinc that accepts any string with i in its midst
we are also excited by the promise of the learning proccdure not just because it learns good rules but dso because the rules it learns can be freely intermixed with hand cngineered rules
given a document to be analyzed it proceeds through a rule sequence one rule r at a time and attempts to apply r to every phrase in every sentence in the document
to begin with we are concerned not so much with the traditional goal of analyzing the comprehensive structure of complete sentences as much as with assigning partial structure to parts of sentences
this task has become a classic application for finite state pre parsers and indeed our work was in part motivated by the success that has been achieved by such systems in past information extraction evaluations
in addition the rules refer to a few morphological predicates and some short word lists one such list for example lists words designating business subsidiaries e.g. unit
the mt lcb l cvahtation rcquircd actual system performance resuhs to be kept strictly monymotts which precludes our reporting here any scores as specific as we have cited for english
we use a mapping function as discussed in section NUM NUM to build a mapping of NUM byte characters to one byte
refer to valency pattern sentence analysis as ordinary construction
processing flow for analyzing a japanese doume suhjeet construction
example NUM shows wa as a proxy for case marking particle hi in tile sentence wamshi no ie wa gakkou ga chikai file school is near my house
key terms used in this paper me defined as follows case marldng particle in english each case is marked by its relative posilion in terms of tile predicate or by a preposition
in the type NUM prcprocessing step in figure NUM case marking particle ga has to be converted into the case marking particle wo before binding the former which represents an objective case
simple sentences normally have only one sub icctivc
such sentence structure is called lhe double subject construction
case with subjective case marking particle ga
the mefl od has fllrec processing phases
this paper has classified the construction into four types
this is because each language community uses its own coding system which is optimal for internal communication but is not appropriate for exchanging information with those outside at community
for each language this step calculates how likely the decoded string is to be from that language by comparing the string with the statistic model of the language
thus it is crucial to know in what language the target document is written in order to choose the appropriate system or language specific rules and dictionaries
f xz NUM NUM chare i ext where p ehar l is the unigram probability of char in language l
the input to such component should thus be as high level as possible without hindering portability
we are both indebted to kathleen mckeown for her guidance and support during those years
figure NUM root drs with cvcvc template and passive voweling i pattern and the vocal element aa representing active voice
the character i has a high score to be a surname
each sequential step is driven by a rule that attempts to patch residual errors left in place in the preceding steps
we report here evaluation of a simple additive method for combining the three algorithms described above
on the statistical model this type of errors is difficult to avoid
for each elementary tree with root ui or ui there are 3m elementary trees in tile new stsg that correspond each to creating a child ij for the t or f on its frontier
but the ultimate solution to the complexity of probabilistic disambiguation under the current models lies we believe only in further incorporation of the crucial elements of the human processing ability into these models
NUM adverbial which handles mapping of satellite roles onto the peripheral svntactic structure
age at the top level surge is organized into sub grammars one for each syntactic category
are certain kinds of technological advances particularly urgent because of their consequences for human usage
will computational linguistics play a key role in changing human languages and the rules of the language gaines in society
will improved language technology reduce the effects by narrowing the gap between human and machine competence
and or shall we expect a trend towards constrained languages in a more controlled society
this fact will presumably remain true in the next century
or rather promote mechanisation making programmed agents more influential
will computational linguistics contribute to enhancing human communication above anything we have so far imagined
that sounds like a brave new world for those who care about language development
if not in a decade or two then in a remote but conceivable future
note also that cue words italicized explicitly mark the boundaries of all three segments
while the output is not a direct translation of input articles collocations that appear frequently in the news articles will also appear in summaries
for example we have already mentioned that mr speaker was translated as monsieur le prdsident which is obviously only valid for this domain
furthermore more experimentation with the values of the thresholds needs to be done to locate the optimum trade off point between efficiency and accuracy
our scoring only uses the relative frequencies of the events without taking into account that some of these events are composed of multiple single events
this line of research aims at producing tools smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons that satisfactorily handle relatively simple tasks
e gax ni y wo z ni tabe sase rareru
NUM 7c that does not exist
x nom y accz dat z dat give perfective
to compute the mpp under dop one possible solution involves some heuristic that directs the search towards the mpp a form of this strategy is the monte carlo technique
one is selectional and the other is overlapping
two structural constraints were also imposed on the content units that subjects were asked to identify
fig l subcategorization frame for ageru
NUM combining surface and deep frames NUM NUM
the one on the left gives the results for the full narrative excerpted in figure NUM
precision was NUM for the best version compared with NUM for humans recall was NUM
for example the documents from the ap are similar in length but the wsj the ziff and especially the fr documents have much wider range of lengths within their collections
the results from trec NUM showed significant improvements over the trec NUM results and should be viewed as the appropriate baseline representing state of the art retrieval techniques as scaled up to handling a NUM gigabyte collection
the results of this training are used to create q2 the routing queries to be used against the test documents testing task shown on the middle column of figure NUM
this task is shown on the right hand side of figure NUM where the NUM new test topics are used to create q3 as the adhoc queries for searching against the training documents
this averages to well less than one new relevant document per run since NUM runs from all systems were used in the adhoc test NUM runs in the routing test
the inquery system outperforms the berkeley system by NUM in average precision with much of that difference coming in the high recall end of the graph see figure NUM
the ranking algorithms rely on combining the results of increasing less restrictive queries until the only allow removal of words and phrases modification of weights and addition of proximity restrictions
it may be that the statistical clues presented by these shorter topics are simply not enough to provide good retrieval performance and that better human aided systerns need to be tested
the result of their investigation was a term weighting algorithm that combined the okapi algorithm city university for high frequency terms with the old inquery algorithm for lower frequency terms
the topics were expanded by NUM phrases that were automatically selected from a phrase thesaurus infinder that had previously been built automatically from the entire corpus of documents
dominance relation i.e. im dominates in
b marys bj poured a glass of milkobj for tinam o
the test inputs are the sentence predicate and gf fillers arranged in the order of the event arguments against which they are to be tested
to indicate the alternation types for these sentences i call sentence 2a a benefactive ditransitive and sentence 2b a benefactive transitive
which al ternations are associated with a verb class is a matter of linguistic evidence the linguist discovers these associations by testing examples for grammaticality
to determine the alternation type of a test sentence the sentence must be syntactically analyzed so that its grammatical functions e.g.
we applied the technique to the task of relative pronoun disambiguation and found that the case based learning algorithm improves as relevant biases are used to modify the underlying case representation
in the cache model an interruption may give rise to an expectation of a return to a prior intention and each participant may attempt to retain information relevant to pursuing that intention in their cache
it should also be possible to test whether entities that are in the focus spaces on the stack according to the stack model are more accessible than entities that have been popped off the stack
two factors determine when cache operations are applied NUM the speaker s in3 obviously linear recency is simply an approximation to what is in the cache
if they are salient by virtue of being on top of the stack they should be accessible for processes such as content based inferences or the inference of discourse relations
when new items are retrieved from main memory to the cache or enter the cache directly due to events in the world other items may be displaced because the cache has limited capacity
consider dialogue c without 22b 22c and NUM i.e. replace 22a to NUM with but as far as the certificates are concerned i do n t like all my eggs in one basket
thus what is contained in the cache at any one time is a working set consisting of discourse entities such as entities properties and relations that are currently being used for some process
where p cw l is the unigram probability of c in language l and hw is the class name of the word w
for example the byte code contains the pattern esc b then it must be encoded with is NUM and include japanese characters
in our algorithm the human intuition on his familiar language is realized by a statistic based module which calculates how likely a text to be from a specific language
for example jis x0208 NUM referred to ms jis character set in this paper is a character set for encoding texts mainly in japanese
specifications for the implementation are given in terms of data structures and required algorithms
the implemented model proved to be particularly suitable to work in a multilingum domain
in this section we focus on the choice of the head and the modifiers tbr noun phrases
another relevant factor is the pragmatic setting of the discourse formality and politeness
we have presented the computational model implemented in the gist system for referring expressions generation
else variable attempt pronominalization using the algorithm described in section
in general word achieves a high score in either the correct or the corrupted condition but not both at once
the test sets for the correct condition are the same ones used earlier based on NUM of the brown corpus
given a target occurrence of a word to correct it substitutes in turn each word in the confusion set into the sentence
a few confusion sets not in random house were added representing typographical errors e.g. lcb begin being rcb
in a few cases such as lcb begin being rcb this effect is enough to drive bayes slightly below baseline
table NUM performance of the component methods baseline base trigrams t and bayes b
the thresholds are set in a preprocessing phase based on the training set NUM of brown in our case
the previous section evaluated the performance of tribayes with respect to its components and showed that it got the best of both
the second point about bayes is that like trigrams it sometimes makes uninformed decisions decisions based only on the priors
the rule which seems both as obvious as walking and as fool proof comes from the name findinig processor we developed for our participation in the NUM m message understanding conference mtjc NUM
this strategy achieves the desired high initial recall r i as these tags are well correlated with bona fide proper nanles md are reliably produced in mixed case text by our part of speech tagger
other a ternativcs include setting a strict limit on the number of rules learned or cross testing the performance improvement of a rule on a corpus distinct from the training set
correspondence to the regular sets it is straightforward to prove that this approach recognizes a subset of the regular sets so we will only sketch the outline of such a proof here
coco noting that the regular sets are closed trader intersection wc them proceed to build the machine that intersects the acccptor with bli
the morphological rules arc followed by contextual transformations these rules inspect lexica context to relabel lexemes that are ambiguous with respect to part of speech
this strategy does not yield quite as good initial precision i.e. it yields false positives for a number of rcasons such as the fragmentation problcms noted above e.g.
preliminary exploration shows that a of NUM NUM seems to boost precision with no significant loss in the long term recall or f measure of the rule sequences
test entire string spanned by phrase test phrase s label sets the label of the phrase modify the phrase s eft or right boundaries table h repertory of unary rule clauses
the fact that the interpretation of bank depends on the semantic properties of money and boat is what persuades us that the form bank is being used to realize two diffe rent
we have further to acknowledge the correct intuition that for telic events the perfcctive fbcuses on the end point of the event where the sinq le aspect views it as a whole
this distinction becomes even clearer when we consider it seems that whereas you can have both a date and a duradon with the perfective you call have either but not both with the simple past
we do not however want to describe the present participle marker as being ambiguous with different interpretations which depend on the semantic context in which it occurs unless we are absolutely forced to
sentence s like 1l make reh renee to s nle anaphorically determined instant and this gives them a slightly difli rent flawmr ty om sinqfle past sentences
indeed since only one peach is involved the remainder of the mp for eat which would include the information that you can only eat something once would presumably force this conclusion
sul l ose w have the following mps for aktionsarts and thematic roles fl ht st arc all straighl forward enough
mp prog says in each case that there must be an event whose start point is before now and an event which does not have an end point before now
for NUM where the verb denotes a homogeneous state of affairs the simple aspect supports tile conclusion that such a state of affairs does indeed hold
the properties of the verb live and t he sin ple aspect seem to collude in this case and there is no need for anything like coercion
in addition to the linguistic learning this type of exercise makes it also possible for the child to develop his capacity to locate himself in the space
these two categories of collections will clearly separate adaptation documents from production documents
adept will be connected to the rose feed servers via a 16mb second token ring local area
an sgml tag defined by oir is associated with each annotation
adept is comprised of eight processes each performing a specialized task
unknown sources are sent to the problem queue to for user intervention
to investigate resolve a problem document the desired document must be selected
appendix c depicts the sgml tags which will be identified by adept
pqm functions provide the user the ability to select and resubmit multiple documents
the diagnostic information contains an error explanation as well as suggested corrective actions
total chinese personal names in each section are listed in cohlnm NUM
a married woman may mark her husband s surname before her surname
NUM NUM NUM NUM clue NUM gender there is a special custom in chinese
however case b also appears very often in newspapers
some chinese characters have high score for male and some for female
it is just segmented by a word segmentation system without checking manually
it is segmented by a word segmentation system and is checked manually
the baseline model forms the first level i.e. character level
models a and c have two score functions
the pattern that we have observed for the dutch infinitive plural ambiguity can be replicated for other cases of morphological ambiguity
a number of weights are associated with each term in the training set
we think that the edr electronic dictionary satisfies those conditions and hope that it will be widely used for various natural language processing applications
it consists of stemmed non stop single word tokens plus hyphenated phrases
leggptri ll NUM ll i c tl tcl pretare ll i terprctarc ll pt bbltcazione h ptlbblicaztom i
the distinguishing value here is paragraph which has been added to the type system
such methods however are either costly for each new text a new set of questions will have to be devised or hard to quantify objectively or even both
in summary a profile will be an ordered list of meaning relations xi i NUM n which describe the relation of sentence i with what came before
to test whether the meaning of a translated text has come across one could simply ask the evaluators questions about the translated text or have them summarise it
the method we will adopt involves constructing a profile of both ttre original and the translated text in terms of some salient semantic or pragmatic property of its constituent sentences
the set of conjuncts was designed to be minimal no redundancies no ambiguous conjmlcts there were NUM of them spread over NUM categories el
yet another assumption is that the meaning relationships between sentences of a text combine to form a characteristic feature NUM profile of that text and that this profile needs to be preserved in translation
as for the salient property to be used in the profile we settled on meaning relations of single sentences with previous text this property seemed to us to be both fairly discriminating and implementable
NUM if there is more than one topic comment pair order the pairs as seems best and determine using the same method as for sentences which conjuncts fit best between the pairs
id lcb syn syn lcb constype phrasal lcb constr paragraph rcb
these are two important aspects if one considers the task of developing large scale grammars
although the reasoning that language acquisition can do without explicit rules may hold for oral language proficiency there is no evidence that it generalizes to the acquisition of written language skills
the recently published survey of the state of the art in human language technology does not spend a single word on computer aided language learning call
if writing skills do require the application of explicit orthographic morphological syntactic etc rules by the learner then antigrammar attitudes must be detrimental
this explains not only the bias in favor of oral and conversational language skills in current hmguagc pedagogy but also the reluctance to work with explicit grammar rules
instead of initiating research into improved grammar teaching methods they have tended to play down the importance of grammar rules and linguistic awareness in learning how to write
a particularly important reason why human language technology should begin to take written hmguage instruction seriously derives from the following argument
many instructional scientists subscribe to the view that language skills are best acquired in a situation similar to that of children learning their mother tongne
the expected results of such a discussion are not only an increase of resources manpower in the call domain but also an increase of awareness that is a sharpening of the researchers understanding of what the problems are that people encounter when processing language
it is also worth mentionning that no cross fertilization has taken place between the call community and people working in the machine learning paradigm NUM NUM NUM while there are fundamental differences in terms of goals and methods there are also some important overlaps
our results demonstrate an extremely significant pattern of agreement on segment boundaries
third so that the general topics of discussion would be comparable we asl ed writers to focus on anatomy physiology and development
because we wanted to give writers an opportunity to explain both objects and processes each writer was given an approximately equal number of objects and processes
sentence NUM instantiates the longest possible pattern a NUM gram that here consists of need orange in and elmira
advisor infers a user s current goal from his or her most recent utterances and uses this goal to select a hierarchy from the multiple hierarchy knowledge base
instead the domain expert preferred for explanations to include the content associated with this topic only when the process being described was a conversion process
the resulting explanations were presented to our domain expert who critiqued both their content and organization and we used these critiques to incrementally revise the edps
note that full patterns can be as small as bigrams such as when an adverb follows an np acting as a subject
we would also like to explore how kankei performs in a more general domain such as the wall street journal corpus from the penn treebank
the trains corpora are much too small for kankei to rely only on the full pattern of phrase heads around an ambiguous attachment
the surface string was generated from each underlying form by mechanically applying the one or more rules we were attempting to induce in each experiment
besides a transliterated name consists of a string of single characters after segmentation
although the segment set used was slightly different from that of the english data the same set of NUM binary articulatory features was used
the generalization of segments into classes performed by the decision trees and the induction of the structure of the transducer by merging states
johnson then proposes that principles from universal grammar might be used to choose between candidate rules although he does not suggest any particular principles
it shows that the characters used in transliteration are selected from some character set
a common feature of all these systems is that they are often too expressive in that too many words are stressed mainly due to the lack of discourse information for instance on focus domain or the given new distinction
yet most of the current implementations of the concept to speech approach use the conceptual representation only to avoid syntactic ambiguities with the assignment of intonational features still based on the written text see NUM
form contexts of a request does it become possible to map the dialogue move speech function correlation of request question to the mood and key features declarative answering answer to question strong see also section NUM NUM
furthermore in a dialogue situation as given in our approach it is not sufficient to look at isolated sentences instead one has to look at the utterance in its context as part of a larger interaction
in the model a dialogue is represented as a sequence of dialogue moves e.g. request inform withdraw request which are further decomposed into sequences of atomic acts dialogue moves and sub dialogues
according to systemic functional linguistics sfl see NUM NUM NUM intonation is just one means among others such as syntax and lexis to realize choices in the grammar
the major shortcoming is that traditional text to speech systems e.g. NUM NUM NUM and concept to speech systems NUM alike use purely syntactic information in order to control prosodic features
NUM state of the art in speech generation in this section we give a survey of existing speech generation systems for german arguing that their syntax based approach does not suffice to generate natural speech in dialogue systems
the lack of good alternatives however might condition a wh question what is your next preferred departure time with tone NUM interrogative wh type whnontonic neutral involvement
statements of this type do not need any particular intonational marking since at this point they are expected hence we choose the features declarative stating neutral ndegnemphatic ndegncontrastive i.e. tone la
for this verbalization the informational status of the entities is used
this property is expressed by different means in different languages
the processes are illustrated by examples taken from the implementation
one important parameter of information structuring is the informational status
the computation of the informational status of discourse entities
during language production processes of information structuring constitute a relevant part
these processes are regarded as a mapping from a conceptual structure to a perspective semantic structure
accordingly i have described and illustrated an implemented algorithm based on a detailed representation of context
the actions of the procedures are designed so that any possible complete parse tree t for the input sentence corresponds to exactly one sequence of actions call this sequence the derivation of t each procedure when given a derivation d lcb al an rcb predicts some action a l to create a new derivation d lcb al a l rcb
cantly underdispersed at the NUM level the significance level i will use throughout this study for determining underdispersion reveal a strong tendency to be key words
the result is a cleaner set of data in which clearer distinctions may be found as evidenced by the lower cross entropy achieved
however as noted earlier the texts in our application are relatively short and therefore the coreference sets are usually of manageable size
despite the reduction in training data and corresponding increase in test data the results of this experiment appear to consistent with the first two
we presented the encouraging results of initial experiments with several approaches to estimating such distributions in an application using sri s fastus information extraction system
in such a scenario the downstream system must fuse the incoming information from each of its sources requiring the resolution of conflicts
each node in the tree has a unique address obtained by applying a gorn tree addressing scheme shown in the tree ol cooked fig NUM
NUM he derived structure corresponding to a coordination is a compositc structure built by applying the conjoin operation to two elementary trees and an instantiation of the coordination schema
give has a structure as in fig NUM then we can use the notion of contraction over the anchor of a tree to derive the sentence in NUM
in a graph when an edge joining two vertices is contracted the nodes are merged and the new vertex retains edges to the union of the neighbors of the merged vertices s
also in eases of unbounded right node raising such as keats likes and chapman thinks mary likes beans chapman thinks simply adjoins into the right conjunct of the coordinate structure NUM
l harry cooked and mary ate the beans we introduce a notation that will enable us to talk about this more formally
a derivation is valid if the input string is accepted and each i ode in a contraction sl ans a valid subs ring in the ini ut
the process of coordination built a new derived structure given previously built pieces of 1this notion of sharing should not be confilscd with a deletion type an dysis of coordination
but in the former case where p1 extends some p2 which is defined at n p1 assumes a definition by default
this extension occurs for all paths in the right hand side whether they are quoted or unquoted and or nested in descriptor sequences or evaluable paths
the architecture shall provide for the use of a common sgml tag sets dictionary the basis for these tag sets shall be reference a tei and any augmentation provided by various applications
reference a tei addresses the concept and retention of common document structures as well as methods for assembling complex structures including graphics multimedia and text from basic structure definitions
in addition to a reference mechanism span and document id that identifies the scope of the annotation the two principal parts are the annotation type identification and the specific information in the annotation
the output shall result from either finding all identical documents in a collection which match a specific document or finding all identical documents in a collection based only on the selection specification
the architecture shall provide for the use of a common document structure library which will store formal descriptions of the structures of specific document types to aid in their processing
every application implemented under the tipster architecture will need an application requirements document that selects specific features allowed by the tipster architecture as well as specification of any non tipster capabilities
it should be noted that while persistent knowledge requires a place to keep knowledge items and the format of the item or storage area not all items must be completely filled initially under the tipster project
in the detection process items such as personal names organization names equipment names locations dates and identification numbers may be treated as single units when specified as such by the user
if instead the two coders were to use four categories in equal proportions we would expect them to agree NUM of the time since no matter what the first coder chooses there is a NUM chance that the second coder will agree
first although kappa addresses many of the problems we have been struggling with as a field in order to compare k across studies the underlying assumptions governing the calculation of chance expected agreement still require the units over which coding is performed to be chosen sensibly and comparably
note that the cases of testing for the existence of a boundary can be treated as coding yes and no categories for each of the possible boundary sites this treatment is used by measures NUM and NUM but not by measure NUM
this is accomplished by applying persistent knowledge information in conjunction with matching algorithms annotations and template pattern matching techniques to reduce a document collection to the desired sub set or to extract information from the document collection
the extraction process shall use templates fill rules patterns or other methods in conjunction with document management detection and the persistent knowledge repository to extract desired information from documents in document collections
any subdivisions of the corpus which tended to keep contiguous material together and which gave an appropriate n mber of chunks say between NUM and NUM all of approxlmately the same size would be satisfactory
if corpus NUM is twice as large as corpus NUM do we call them equally homogeneous if corpus i contains twice as many language varieties as corpus NUM or the same number of language varieties but twice as much of each
although money bar and river bank are counted together corpora using the one and corpora using the other will tend to be discriminated because the one corpus will use money account and barclays more the other river and grassy
at a krst pass it would appear that the chi square test will serve to indicate whether two corpora are drawn from the same population or whether two or more phenomena are signi cantly fli erent in their distributions between two corpora
assuming that adverbs modify verb phrases and not sentences there will be no interactions when the john ran edge is moved to the chart
two corpora of the same size and the same number of texts may still have a very different shape if in one one of the texts accounts for most of the corpus whereas in the other they are all of similar size
both are rejected however on the grounds that they would involve using a predicate from the original semantic specification more than once
our most linguistically advanced stream is the head modifier pairs stream
where can we get the right queries
NUM streams used in nlir system
the total usage frequency of these words is NUM NUM NUM
NUM NUM the interaction between the workspace and the conceptual network
a cycle is the execution of one codelet
for instance the fragment shif n which is a bisyllabic word in sentence 3a functions as two separate word s in sentence NUM
computational activities are indirectly guided by a semantic network of linguistic concepts which ensures that these activities do not operate independently of the system s representation of the context of a sentence
on the other hand if only one way of segmenting the word boundary of an ambiguous fragment is allowed in a sentence we call this local ambiguity with respect to the sentence
this type of idiosyncrasy is encoded in the lexicon
a link represents an association between two nodes
there are four main components in this model
eventually all word boundaries will be identified
then during text processing fin example a portion of text could be analyzed using the appropriate cck to lind implicit relations and hell understanding the text
expansion forward for each semantically significant word in the cckg not already part of the concept cluster find the maximal common subgraph between its temporary graph and the cckg
the graph operations maximm commou subgraph and maximal join defined on conceptual graphs anti adapted here play an important role in our integration process toward a final cckg
in i igure NUM with is subsumed by instrument and by lnapping them we disantbiguate wilh from corresponding to another semantic relation such as possession or accompaniment
we have reached the root of the noun tree in the concept hierarchy and this would give a similarity of NUM based on the informativeness notion
as some relations are defined using the closed class words and many of those words are ambiguous the resulting graph will itself be ambiguous
figure NUM exemplifies one of theln where two graphs containing the same word or morphologically related here draw and drawing used as different parts of speech can be related
the generation module automatically produces the reply letter in a standard l rmat sgml
deg l ack of pcrsonalisation NUM NUM and NUM NUM
l liminalory criteria not always reel due lo problems of comprehension and gramlnar
the delivery will in jact take a little longer than planned
the rhetorical module chooses concrete operators modalities and surlace order according to rhetorical rules
inference chain from the remaining set
we discuss now how the discourse scores are derived
conslrainls a new type of constraint we introduced
multiple speech acts can be inferred for one ilt
the main components of an NUM NUM are the sl eech act e.g. suggest accept reject the sentence type e.g. state query j g
the resuiting score is called the slalislical score
the discourse scores are derived by taking into accotmt attachment preferences to the discourse tree as reflected by two kinds of focusing scores and he score returned by the qradcd
here two kind of attributes are used head which records the head of a node as a value and nu d rel which records the kind of relationship found between two heads of children
it has been taken for granted that practical effects if any are positive
but they do not always appear in the text
the style of syllabic order is specific in transliteration
some organization names are composed of several related words
the remaining characters are treated in the similar way
however it is easy to capture by pre segmentation
chinese personal names are composed of surnames and names
near NUM of errors come from this type
about NUM of errors belong to this type
NUM chinese personal names NUM NUM structure of personal names
in the economic section stock companies often appear
i think the first thing they said i have written this down so it would is it p do you think it s possible to have honesty in government or an honest government
looking at british national newspapers is the independent more like the guardian or the telegraph
at the same time theoretical accounts of the use of lexical rules such as for instance preemption or blocking are rather too general and underspecified to support actual processing
and kiku ribas made helpful comments on an earlier draft
in fact humans are not doing well on them either
hieroglyphics remained undeciphered for centuries until the discovery of the rosetta stone in the beginning of the 19th century in rosetta egypt
from this recurrence equation and the boundary conditions given above we can compute the values of NUM and i for all i
for example for no NUM there are NUM NUM NUM different points with nabx NUM but only one with nabx NUM
canadian family is another example it is often translated as famille the canadian qualifier is dropped in the french version
note that ignoring inflectional distinctions can sometimes have a detrimental effect if only particular forms of a word participate in a given collocation
however as figure NUM shows high values of the threshold parameter cause the algorithm to miss a significant percentage of valid translations
this is an important feature of the system since in this way the sublanguage of the domain is employed for the translation
the present theory though supplies the reason for this difference in likelihood
the basic open class forms are the stems of nouns verbs and adjectives
but closed class forms are severely restricted as to the concepts they can refer to
in the course of historical linguistic change each language gradually shifts its subset of structuring concepts
this structuring function occurs in two domains in discourse and in the conceptual inventory of language
conversely linguistic closed class forms express structural categories that appear to have little part in visual perception
basing his proposal on my previous research slobin forthcoming advances such a proposal
that is the principles in large measure characterize what is treated as structural in language
their meanings constitute respectively the content and the structure of a conceptual complex
that is the closed class component functions as the fundamental conceptual structuring system of language
again the following ungrammatical example e.g.
e g NUM NUM a john opened the door with the key
this may include creating templates patterns and fill rules needed for extraction and keeping items in libraries for future use
the architecture shall provide for the use of a common gazetteer that can support various document processing tasks in different applications
the user interface provides the mechanism for the user to interactively control various events and processes and to examine results
the long term requirement shall be met through the use of additional tag sets as defined in reference a tel
the architecture shall provide for the use of common grammar rules that can support various document processing tasks in different applications
NUM NUM the architecture shall support physical and software boundaries to devices where documents and data are stored
the architecture concept reference NUM identifies several typical forms of documents form NUM through form NUM
the architecture shall support as a minimum the following information types as slot fillers a string fills b
documents may be assigned priorities based on matches between documents and prioritized portions of queries profiles and selection statements
translating this into a claim about ils text is structured by the ways in which some utterances are intended to help other utterances achieve their purpose
as shown in figure NUM at the top level the text is broken down into two spans a and b c
in addition each minimal unit can appear in exactly one schema application and the spans constituting each schema application must be adjacent in the text
an rst analyst must judge which schema consists of rst relation definitions whose constraints and effects best describe the nucleus and satellite spans in the schema application
the speaker tries to realize each intention by saying something i.e. each intention is the purpose behind one or more of the speaker s utterances
roughly speaking an rst nucleus expresses a g s intention in a satellite expresses another intention is and in g s terms in dominates is
in the example a is the core of ds0 b the core of ds1 and c the core of ds2
computational linguistics volume NUM number NUM finally i2 is made manifest by c though no additional contribution to achieving this intention is provided
the informational structure would contain only the relations among situations events and actions that is the types of entities referred to by clauses
NUM correspondence between dominance and nuclearity now we are in a position to compare the explicit claims of g s about ils with the implicit ones of rst
from the first item kll z evidence shown in NUM NUM of figure NUM is collected and the result is stored in the form shown in NUM NUM
having worked through all the elements in wd the evidence given in NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM and finally NUM NUM is obtained
attitudinal construct attitudinal constructs in japanese include modal verbs auxiliaries
the analysis is a dil t icult problem because such compound nouns can be quite long have no word boundaries between contained nouns and often contain nnregistered words such as abbreviations
patterns in NUM NUM collect the evidence of particlecombined collocation of a and b a and b are combined by a particle c which is similar to of in english
where n is the number of possible hidden states and m is the number of all the observable events
evidently the performance of the algorithm depends on the amount of distortion introduced in the input phonemic string
in figure NUM the average number of times an output position is occupied is given for all the languages
this number is a measure of the similarity of wrong words with the corresponding correct words percentage
the only remedy in such situations wire communication laboratory university of patras patras gr NUM greece NUM association for computational linguistics computational linguistics volume NUM number NUM would be to increase the size of the reference dictionary so that every possible input word is included
this resulted in fewer grapheme transitions in the training corpus and meant that the standard training period was insufficient
clearly qx t qe p qx t p qi t vi e NUM e the probability of the complete state sequence qm t NUM m e which contains qx t would be
this algorithm proceeds computational linguistics volume NUM number NUM recursively from the beginning to the end of the word calculating for any time in the case of ptgc time is the position of a phoneme grapheme in the word the score of the best path in all possible hidden state sequences that end at the current state
in order to accomplish the outlined noise reduction process we need i a disambiguation operator and ii a disambiguation strategy to eliminate at each step some noisy collocations
thus the internal nodes of the tree will correspond to tests of the values of phonological features while the leaf nodes will correspond to state transitions and outputs from the transducer
since we have a relation hierarchy in addition to our concept hierarchy we can similarly use subsumption to match two relations
we carl update tile concept hierarchy and add this label NUM as a subclass of something and a superclass of pen and crayon
we just saw how to relax the maximal common subgraph operation and we will perform the join around that relaxed subgraph
using a children s dictionary allows us to restrict our vocabulary but still work on general knowledge about day to day oil cel ts and actions
we could hope in a higher variability of language patterns by training over NUM NUM million words corpora
rcb lcb bent verb rcb lcb so nmch modifier rcb i i wea h stron strength as shown in the above example there is no pos chain error but there exists an implicit spelling error which can be detected by using the semantic dependency strength
from the fig l thai word segmentation can be implemented as follows case i only longest segmentation or shortest segmentation is possible case ii both longest segmentation and shortest segmentation are possible
carelessness lack of knowle dge t his his free tee fa t far both boat n o on form from
finally if there is no word chain which has all right pos sequences the erroneous word chain which has the error marker at the most remote position will be selected and be expected that there is an implicit spelling error
two possible grouping characters into words longest possible segment or shortest possible segment there are two possible grouping characters into words i e shortest possible segment such as NUM and longest possible segment such as NUM in fig NUM
this work attempts to provide a robust morphological analyzer by using a gradual refinement module for weeding out the many possible alternatives and or the erroneous chains of words caused by those three non trivial problems word boundary ambiguity pos tagging ambiguity and implicit spelling error
this paper has described a new simple technique that performs the disambiguation of word boundary pos tagging and implicit spelling correction by using local information such as lexicon preference a consecutive pos preference and semantic dependency strength measurement of the associative words in a sentence
the word generating function will be called for generating a set of candidate words for the two consecutive words that have weak strength and the process will return to step NUM for pruning the erroneous pos chains and then goto step NUM for calculating the semantic strength again
another central issue for the workshop will be large scale acquisition of computational semantic lexicons for practical applications
this workshop will concentrate on lexical rules as a regulator of breadth and depth of the lexicons
the workshop will stress issues connected with the practical application of lexical rules when to apply the rules how the rules influence system design how to reexamine and adjust the theoretically posited rules in view of practical needs and evidence
this web site will remain available after the workshop for further discussions
building semantic lexicons is a very time consuming task
i breadth and depth of semantic lexicons
in order to facilitate interaction between participants a pre workshop mailing list was established
papers were also available to all participants a month prior to the event in santa cruz at http crl nmsu edu lex rule
lhsting is performed on a set of unseen dialogues that were not used for developing the translation modules or training the speech recognizer
the combination of the two translation approaches resulted in a slight increase in the percentage of acceptable translations on transcribed input compared to both approaches separately
second the phoenix module performs better than glr on the first best hypotheses from the speech recognizer a result of the phoenix approach being more robust
generation in the phoenix module is accomplished using a sirnple strategy that sequentially generates target language text for each of the top level concepts in the parse analysis
an example of nn ill is shown in figure NUM a detailed iui specitication was designed as a formal de scription of the allowable ilts
although designed t col e with speech disth encies lr can graeehdly tolerate only moderate levels of deviation from the grammar
we would like to thank all members of the janus teams at the university of karlsruhe and carnegie mellon university for their dedicated work on our many evaluations
this is don using a collection of parse evaluation measures which are combined into an integrated heuristic for evaluating and ranking the parses produced by the parser
the janus system is a large scale multi lingual speech to speech translation system designed to facilitate communication between two parties engaged in a spontaneous conversation in a limited domain
the translation modules are designed to be language independent in the sense that they each consist of a general processor that applies independently specified knowledge about different languages
neither is the current postmodern approach that there are nice rules and then an area of chaos because language is that way
but impregnable like quite a few other negative deverbal adjectives does not have a non negative counterpart which looks like an accidental gap
big is of course a typical gradable adjective and as such has a numerical scale associated with it the microtheory
but often natural language expressions do not refer to absolute magnitudes but rather to abstract relative ones as in the case of big
this situation in which semantic analyses and lexicographic descriptions of adjectives and other categories are rare is bound to change rapidly
we can either try and discover a rule which marks some semantic cases as participating in the lr and others as not participating in it
the most general case of a denominal adjective entry and its connection to that of the corresponding noun is demonstrated in NUM
abusive can inherit all of those senses both in the eventive and agentive varieties thus adding NUM more senses to its superentry
then techniques for reducing the time complexity are presented and we report two experiments using these techniques
making this approximation it is no longer necessary to reparse the whole corpus for each hypothetical merge
here only identical sequences of initial and final states are merged compare figure NUM a to c
the experiments revealed a feature of model merging that allows for improvement of the method s time complexity
but it should be still better than any n gram model that is of lower of equal order than the initial model
by using the constraint we need about a week of computation time on a sparcserver NUM for the whole merging process
the models need a large set of parameters which are induced from a text corpus
we first give a short introduction to markov mo null dels and present the model merging technique
it is even slightly lower than in the previous experiment but most probably due to random variation
the relative frequencies have to be smoothed to handle the sparse data problem and to avoid zero probabilities
it is a rule bigram model similar to pcfg with special extensions for ug type operations
ice assumes that each module has a local memory that is not directly accessible to other modules
one of our main research interests has been the exploration of performance gains in nlp through parallelization
using this strict method we achieved a word accuracy of NUM which is quite promising
i here are tive basic operations to detine the operations of the parsing algorithm
the lri parsing algorithm is a modified active chart parser with an agenda driven control mechanism
in the ctlse of acoustic scores the penmty is always zero and can be neglected
this reeursive operation of introducing new active edges is precompiled in our parser and extremely etlicient
e words keeps the covered string of word hypotheses while score is a record keeping score components
those families of hypotheses are represented as one edge with a set of end vertices
the algorithm provides a unified computational procedure for assigning interpretations to a significant variety of ellipsis constructions
NUM bill wrote reviews for the journal last year and articles this year
in designing the trec task there was a conscious decision made to provide user need statements rather than more traditional queries
this made them more suited to the routing task than to the adhoc task where users are likely to ask much shorter questions
there were NUM sets of results for adhoc evaluation in trec NUM with NUM of them based on runs for the full data set
these experiments in more sophisticated term weighting and matching algorithms are yet another step in the adaptation of retrieval systems to a full text environment
narr narrative a relevant document must pro null vide information on the government s responsibility to make amtrak an economically viable enti
specifically each of the new topics numbers NUM NUM was developed from a genuine need for information brought in by the assessors
there were NUM topics in which the inqio1 run was superior to the pircs2 nan and these were mostly because of missed relevant documents
the westpl run did not use topic expansion although a rrfixture of passages and whole documents was used in the final ranking of documents
the removal of some of the topic structure the concepts has allowed differences to appear that could not be seen in earlier trecs
both the topic expansion and the passage determination were completely new untried techniques additionally there are known difficulties in combining multiple methods
all conferences have had very open honest discussions of technical issues and there have been large amounts of cross fertilization of ideas
i lic hh hii j libho o i eai ijt c h o inherent feat lational features vevl lto gi 2ri NUM pvedieate le c l l i2 noun libi o objecl libido
t c classilicd ott tlhc basis of t ttc s tl j ct or ol j i nottns it ur wit it words o mi rhlg iu t hc s une cont ext cau d is sema utica liy simila r a lt hough i he sintiht rii v
knowledge he lexico sem mdc knowledge used for our pur poses consists of typical verl sul rcb jecl object vso co oecllrrence patl erits ul orn tieal y acquired from mi lcb ds whose single elements are ex null i i ss l as individual words
if the document is initially from the rose feed om will send the sgml template conforming specific protocol to the main rose catcher
the field names field values and their normalized forms are stored as annotations along with the document in a tipster compliant document manager
utterances were classified into four types each of which was associated with a rule that assigned a controller the discourse was then divided into segments based on which speaker had control
after at most k NUM rounds of substitution we reach a situation where every ak rooted initial tree that fails to satisfy ft has a first nonempty frontier node labeled with ak
NUM this parser is the more remarkable because for tag the best parser known that maintains the valid prefix property requires in the worst case
step NUM alter every node in every elementary tree in i and a as follows let ai be the label of
computational linguistics volume NUM number NUM p oo the key step in converting a tig into a cfg is eliminating the auxiliary trees
during parsing elementary trees are traversed in a top down left to right manner that visits the frontier nodes in left to right order see figure NUM
for instance for moby dick the ten most frequent underdispersed content words are ahab boat captain said white stubb whales men sperm and queequeg
are represented by e the sample estimates ms f m by s and the heuristic estimate mh f m by
when reading through a text word token by word token the number of different word types encountered increases quickly at first and ever more slowly as one progresses through the text
the right hand side counterpart shows a similar trend for the word tokens that is also supported by a least squares regression f NUM NUM NUM NUM p NUM
for the present purposes the crucial point is that we now have defined a population for which we know exactly what the population probabilities the relative frequencies in the complete texts are
we want the constraints to be as week as possible to allow the maximal number of solutions but at the same time the number of merges must be manageable by the system used for computation a sparcserverl000 with 250mb main memory
thus in addition to finding a model with lower log perplexity than the bigram model NUM NUM vs NUM NUM we find a model that at the same time has less than NUM NUM of the states NUM vs NUM NUM
the regular participation of verbs belonging to a particular semantic class in a limited number of syntactic alternations is crucial in lexical semantics
it is suitable for both speech recognition and part of speech tagging has the advantage of automatically deriving word categories from a corpus and is capable of recognizing the fact that a word belongs to more than one category
in part NUM we describe the illico system
once the verb type is identified verb definitions verbs are needed to provide the argument structures
in particular i consider the lexical semantics task of characterizing semantic verb classes and show how the language can be extended to flag inconsistencies in verb class definitions identify the need for new verb classes and identify appropriate linguistic hypotheses for a new verb s behavior
a verb can have multiple senses which are instances of events for example the verb pour can have the senses pour or prepare with the required arguments shown below NUM note that pour1 and pour2 in NUM are subcategorizations of prepare
problem argmaxp c h p argmax description maximum entropy maximum likelihood type of search constrained optimization unconstrained optimization search domain p e c real valued vectors lcb NUM NUM rcb solution p
once such a safe segmentation has been applied to the french sentence we can make the assumption while searching for the appropriate english translation that no word in the translated english sentence will have to account for french words located in multiple segments
the feature selection algorithm described in section NUM calculated that if this constraint were imposed on the model the log likelihood would rise by approximately NUM NUM bits since this value was higher than for any other constraint considered the constraint was selected
in other words the words to the left of the french word yj are generated by words to the left of the english word and the words to the right of yj are generated by words to the right of
by restricting attention to those models p ylx for which NUM holds we are eliminating from consideration those models that do not agree with the training sample on how often the output of the process should exhibit the feature f
a dynamic programming algorithm then selects given the appropriateness score at each position and the requirement that no segment may contain more than NUM words an optimal or at least reasonable splitting of the sentence
we remind the reader to keep in mind when evaluating figure NUM that the segmenter s task is not to produce logically coherent blocks of words but to divide the sentence into blocks which can be translated sequentially from left to right
a closely related statistic is the probability p n of sampling a new unseen type after n word tokens have been sampled
NUM i thank our sponsor ge who features s human v exists do entity do up1 name prevl syntactic type comma class do up1 v do there are two semantically legal antecedents
given the nature of the task namely the transformation and or augmentation of partial sentence specifications into full ones a set of constraints emerge for the design of the sentence planner
NUM removal of redundancy aggregation in some instances an implant wears out loosens or fails and will have to be removed
more precisely it makes the pre spl more concrete along three lines NUM lexicalization of discourse structure relations discourse relations and their cue words may be realizable by lexical means
we would like to thank bruce jakeway for implementations as well as the other members of the sentence planning group chrysanne di marco phil edmonds and daniel marcu for many fruitful discussions
since we have already provided extensive references to work in the second area and our focus in this paper is not on the detailed presentation of these subtasks we refrain from discussing it further
if an entity has been introduced verbatim its next mention can be realized as a personal pronoun generalized name definite name deictic pronoun or ellipsis
athe relation of kakari and uke equals to modifier and rood ifiee
the fourth sentence of figure NUM is an example of this type of error
however their evaluation method is very optimistic and completely different from ours
finally we show a simple example of estimating the word n gram counts in an unsegmented sentence
first we compute expected word frequencies from japanese texts using a robust stochastic n best word segmenter
in the following sections we first describe a statistical language model to cope with unknown words
table NUM shows the number of sentences words and characters for training and test sets
we then replaced the discarded characters with the unknown character tag to train the word spelling model
it is obvious that word bigram outperformed the part of speech trigram as well as word unigram
we tested the new word extraction method using the medium size test set NUM sentences
we intend to test our word segmentation method on other languages such as chinese and thai
besides the software is a real multimedia system each activity being illustrated through several coordinated methods writing sentences on the screen and synthesising them orally graphic representation on the screen
because these non terminals occur in multiple contexts some overgeneration is introduced
similarly for the right branch there are cl NUM possibilities
table NUM probabilities of sentences with unique productions test data with ungeneratable sentences
of course this research raises as many questions as it answers
efficient algorithms for parsing the dop model
the algorithm can be summarized as follows
this turns out to be very surprising
s np vp will occur repeatedly
figure NUM example of isomorphic derivation
one reasonable stopping criterion is to subject each proposed feature to cross validation on a sample of data withheld from the initial data set
satellite roles also called adverbials answer the questions when where why how did it happen
sur ge implements the full NUM english tenses identified in NUM pp NUM s207
if an auxiliary tree t is not left anchored then the first nonempty frontier element after the foot is labeled with some nonterminal ai
we assume for the moment that the set of rules for g does not contain any empty rules of the form a c
first adjoining constraints could be used to prohibit the adjunction of particular auxiliary trees or all auxiliary trees at a given node
surge also includes lexical processes inspired bv lexiealist grammars such as the meaning text theory and hpsg NUM
u f is an extension of the original functional unification formalism put forward bv kay NUM
the agreeinent patterns are more complex as well
we overview these phenomena and then examine the structures
there are nnmy more cases of agreement in french than in english and mmw more lexical items exhibit agreement fi atures determiners adjectives past participles and conjugated verbs
although many similarities may be observed in the two grammars there are important structural dif ferences which can be traced back to features specific to the f ench language notably agreement and cliticization
however the ccb is a non voting board
the term incomplete in the table denotes the cases where the tree retrieval stopped mid way because of an unknown word in the classification
NUM NUM the paragraph as the domain of topic continuity
the right hand panel highlights the corresponding difference scores
picking the most frequent cb gives better results than simply picking the most frequent words or references as the most important topics in the text
generating distributed referring expressions an initial report
in contrast unbounded lexical ul aggregation is carried out over an open set of aggregands and consequently the aggregated information is not recoverable and has to be licensed by other factors such as the hearer s goals
summarization is a particularly nice application for natural language generation because the original text can serve as the knowledge base for generating the summary
the proposed summarization heuristics are tested out on a sample text in section NUM an implementation to test out these heuristics is in progress
such cases however could sometimes hit the correct translation since the algorithm output the most frequent translation under the stopped node as the default answer
the good turing estimates mgt f m
for individual sentences the answer is undoubtedly yes
we found that for sentences covered by our current set of linguistic descriptions the system arrived at the same word boundaries despite different paths being taken at each run
when a few chinese characters are enclosed by a rectangle for example k b6nr n it indicates that these characters make up a word object
to prove that problem a is as hard as problem b one shows a reduction from problem b to problem a the reduction must be a deterministic polynomial time transformation that preserves answers
this type of codelet reports on the word gan palmer and lua a statistically emergent approach agent theme reflexive adjective aspect a graph of structures constructed against cycle number
the essential features of the model are the process of sentence analysis is a series of computational activities that determine how various constituents in a sentence can be meaningfully related
when few good linguistic structures have been found the temperature will be high leading to many more random decisions and hence to more diverse paths being explored by codelets
the temperature is clamped at NUM for the first NUM cycles to ensure that diverse paths are explored initially the range of the temperature varies between NUM and NUM
zh w i zhiyudn gongzu6 de yali hon da this cl NUM worker work struc NUM pressure very great this worker faces great pressure in his work
the context dependent activation of nodes enables the system to dynamically decide what is relevant at a given point in time and influences what types of actions the system engages in
if x and y can be constituents of the same word yet at the same time may also be free then word boundary ambiguity exists in these two characters
this latter evaluation compares the performance of the system with that of several human judges since as we shall show even people do not agree on a single correct way to segment a text
whether a language even has orthographic words is largely dependent on the writing system used to represent the language rather than the language itself the notion orthographic word is not universal
the model segments chinese text into dictionary entries and words derived by various productive lexical processes and since the primary intended application of this model is to text to speech synthesis provides pronunciations for these words
thus if one wants to segment words for any purpose from chinese sentences one faces a more difficult task than one does in english since one can not use spacing as a guide
since the total of all these class estimates was about NUM off from the turing estimate nl n for the probability of all unseen hanzi we renormalized the estimates so that they would sum to nl n
the first four affixes are so called resultative affixes they denote some property of the resultant state of a verb as in t wang4 bu4 1iao3 forget not attain can not forget
our system fails in a because of shenl a rare family name the system identifies it as a family name whereas it should be analyzed as part of the given name
any nlp application that presumes as input unrestricted text requires an initial phase of text analysis such applications involve problems as diverse as machine translation information retrieval and text to speech synthesis tts
note that a single extraction task consists of extracting a specified number of sentences from one text
in addition to intra textual cohesion there are words that contribute to the cohe null computational linguistics volume NUM number NUM sion of the discourse as a whole
although there is a fair number of key words in the first few text chunks the intensity of key words drops quickly only to rise again around chunk NUM
since intra textual cohesion is also present in the texts of novels we may conclude that the overestimation bias in novels is determined by a combination of intra textual and inter textual cohesion
for the first NUM measurement points the instances for which e v n diverges significantly from v n are shown in bold
closer inspection of the error pattern of the adjusted estimate reveals the source of the misfit for the first NUM measurement points the observed vocabulary size is consistently overestimated
more formally let k be the frequency of the i th word type in the k th text slice and define the indicator variable di k as follows
in hubert and labbe s model the optimal value of the p parameter is independent of the number of text slices k for not toosmall k k NUM
of course the success of this strategy depends on the system s ability to recognize and interpret subsequent corrections if they arise
thus the success of the system may be a result of the domain and say little about the plan based approach to dialogue
this work would not have been possible without the efforts of george ferguson on the trains system infrastructure and model of problem solving
NUM evaluating the system while examples can be illuminating they do n t address the issue of how well the system works overall
our next iteration of this process trains NUM involves adding complexity to the dialogues by increasing the complexity of the task
more importantly it allows us to address new research issues in a much more systematic way supported by empirical evaluation
since no explicit rejection is identified due to the recognition error this utterance looks like a confirm and continuation of the plan
the verbal reasoner is organized as a set of prioritized rules that match patterns in the input speech acts and the discourse state
this paper describes a system that leads us to believe in the feasibility of constructing natural spoken dialogue systems in task oriented domains
second we used varying amounts of the training data exclusively for augmenting the atis data to build language models for sphinx ii
the set of activities proposed by erel the activities to be chosen are dialogues on scenarios or on logic games in the first case a scenario is illustrated by a pic null ture or a photograph the user comments on with sentences describing what he sees on the screen
further details are available in a trec conference article
the function ea string takes a code string and the name of a coding system
for historical reasons documents on the current www are encoded in various coding systems
the language of a text is identitied by the follow null ing three steps
after the eastern asian part is identified the remainder is classified into the european part
t should noticed that the same number may appear in different character sets
problems in decoding and identifying languages on the www NUM NUM brief explanation of character coding systelns
NUM esc b shows at the beginning of ascii character set
the simplest encoding scheme directly uses the identification number of a character set for communication
se ction NUM and section NUM describes an example and the experimental resuits respectively
this means that all the four coding systems are potential candidates and that they extract the same asian character part
second their score function is not reliable especially when the number of corresponding words contained in corresponding sentences is small
our iterative approach decides sentence alignment level by level by counting the word correspondences between a japanese and an english sentence
entries in the dictionaries differ from author to author and are not always the same as those in the corpus
intuitively the number of possible correspondences for a sentence is small near anchors while large between the anchors
the output of the algorithm is the alignment result a sequence of anchors and word correspondences as by products
this is obviously because statistics can not capture wordcorrespondences in the case of short texts
on the other hand the combined and statistical methods well capture the keywords as described in the next section
the second operation deals with stochastic word correspondences which are highly confident and in many cases involve domain specific keywords
first mutual information and t score are computed for all word pairs appearing in a possible sentence correspondence in asm
in the last text slices underdispersed words are even underrepresented
again the effect of inter textual and intra textual cohesion manifests itself
this process is repeated until no new word sequences are obtained
instead the bias arises due to intra textual and inter textual cohesion
best first parsing methods for natural language try to parse efficiently by considering the most likely constituents first
it seems reasonable to base a figure of merit on the inside probability fl of the constituent
1our results show that the p n i term can be omitted without much effect
trigram estimate an alternative way to rewrite the ideal figure of merit is as follows
in fact the measurements presented here almost certainly underestimate the true benefits of the better models
this allows us to take advantage of information already obtained in a left right parse
then al n k is the sum of these products
actee others possibly modified by a projection imposed by the rhetorical relation
the program extracted words and terms in noun phrases np verb phrases vp prepositional phrases pp representation xp
for example the response officers are better trained and more experienced so they can avoid dangerous situations was misclassified in better trained police general
essentially the results show that given a small set of data which is partitioned into several meaning classifications core meaning can be identified by concept structure patterns
holland also states that lcss could not represent a near match between the two sentences the person bought a vehicle and the man bought a car
in the former case there is a limited subject pool and in the latter case we rely on what has been put into electronic form
there were some cases however where we had no choice but to include some single concepts due to the limited lexico syntactic patterning in the data
the underlying idea in bergler s approach is that the lexicon has several layers which are modular and new layers can be plugged in for different texts
any system we build must have the ability to analyze the concept structure patterning in a response so that response content can be recognized for sconng purposes
among the centers another significant entity is identified the backward looking cen er cb un
accordingly it annotates the tree with either start x where x is any constituent label or with join x where x matches the label of the incomplete constituent to the left
in this way the method reported on here will necessarily be similar to a greedy method though of course not identical
critical error warning error etc NUM verification method demonstration
for example view by date source relevance rank and template slot
in such cases appropriate references or links shall tie the annotations to the document
NUM NUM NUM provisions must be made for permanent annotations
this implies a degree of learning about document structures as they change over time
adaptive identification of formats and document structure may be made based upon representative documents
priorities may be attached to detection criteria including selection statements or portions thereof
criteria used for retrospective search and for routing shall have the same formats available
suppose the pattern string NUM is as shown in table NUM which also contains the corresponding p and failure link values fl
effectively the states in NUM l are copied across to the corresponding entries in NUM j
here j t i and l j NUM are the length of the text string NUM in terms of characters and bytes respectively
for each pattern string NUM string searching will execute the tbllowing steps a convert NUM byte characters to one byte in p to lbrm t i.e.
this array is implemented as two arrays pi and p2 which stores the first and second byte of the NUM byte characters respectively
the evaluation is performed from two points of view precision recall of alignment and word correspondences acquired during alignment
in addition the texts being aligned were structurally similar european languages i.e. english french english german
by using both correspondences the sentence pair whose correspondences exceeds a pre defined threshold is judged as an anchor
input to the system is a pair of japanese and english texts one the translation of the other
although the method works well among european languages the method does not work in aligning structurally different languages
this is mainly because the syntax and rhetoric are greatly differ in the two languages even in literal translations
these techniques are widely used because they can be implemented in an efficient and simple way through dynamic programing
the reason for the factor NUM f is a bit involved
because there is nothing on the discourse stack the initial confirm has no effect
in later systems we plan to specifically evaluate the effectiveness of this strategy
after checking that there is an engine at detroit this interpretation is accepted
this is is a first step in what we see as a long ongoing process
despite the limitations of the current evaluation we are encouraged by this first step
in fact when starting the project we thought generation would be a major problem
using this model the performance of sphinx ii alone on trains NUM data was NUM NUM
the form of these descriptions is defined for each class of objects in the domain
thus as far as furthering the dialogue the system has done reasonably well
in the modified version of the procedure whenever a new inactive edge is created with label b b then for all rules of the form in NUM an active edge is also created with label a c c
the string ran fast constitutes a verb phrase by virtue of rule NUM giving the semantics NUM and the phrase ran quickly with the same semantics is put on the agenda when the quickly edge is move to the chart
this procedure confirms perfectly to the standard algorithm schema for chart parsing especially in the version that makes predictions immediately following the recognition of the first constituent of a phrase that is in the version that is essentially a caching left comer parser
NUM newspaper reports said the tall young polish athlete ran fast the same set of predicates that generate this sentence clearly also generate the same sentence with deletion of all subsets of the words tall young and polish for a total of NUM strings
his is brill s original metric but note that it does not differentiate between rules whose overall improvement is identical but whose rate of over generalization is not
because km is accompanied by a graphical user interface discourse knowledge engineers are provided with a development environment that facilitates edp construction
although recent years have witnessed significant progress in the development of sophisticated computational mechanisms for explanation empirical results have been limited
this discourse knowledge enables it to make decisions about what information to include in its explanations and how to organize the information
next knight visits each of the other topics of the explain process exposition node output actor fates temporal information and process details
partonomic object finds the connection from object to a connection superpart of the object in the partonomy
for example it will report no superevent available if the parent event of a process has not been included
knight streak and ana were all evaluated formally i.e. quantitatively while pauline and edge were evaluated informally
the judges were not informed of the source of the explanations and all of the explanations appeared in the same format
however sections NUM and NUM lhe social and the economic sections have bad precision
many words can serve as names but only some fixed words can be regarded as keywords
we calculate the score of every chinese personal name in the corpus using the above formulas
b the social section there are many items of news about police and offenders
for those similar pairs which have different weights the entry having high weight is selected
how to tell out that a word is a content word or a name is indispensable
these strategies also have the capabilities to detect the left boundary if there is an organization name
the mutual information mentioned in section NUM NUM NUM NUM is also used to measure the relationship of two words
finally applications that must handle a large number of proper names e.g. directory service applications generally can not include all the possible names
loop NUM if a coding system is determined it is easy to extract eastern asian characters
each eastern asian string is passed to the language identification routine in the descendent order of its length
if the word is not longer than n characters the class name is the word itself
since there is no escape sequence which begins with lb the procedure in section
the first NUM bytes from b8 to al match with every pattern in table NUM
the remaining part is decoded into identifying the language and sent to european language identifier
the current version of our algorithm can handle the following NUM coding systems and NUM languages
one approach is to transfer the name of the coding system with the upper level protocols
a character set is a set of characters collected to represent texts in a certain language community
afterwards the second order hmm and the n best algorithm adapted to ptgc were implemented to provide one or more transcriptions for each word input homophones
the center panels of figure NUM show that the overestimation characteristic for interpolation is reversed when extrapolating to larger samples
in the genitive singular the stress is on the first syllable g roda whereas in the nominative plural the stress resides on the final syllable gorad a note that the difference in stress results in very different vowel qualities for the two forms as indicated in the phonetic transcriptions
for the basic translation model the hidden information is the alignment a between e and f we employed the em algorithm to estimate the parameters of the basic translation model so as to maximize the likelihood of a bilingual corpus obtained from the proceedings of the canadian parliament
as we have seen in the four examples discussed above the mle computed over hapax legomena yields a better prediction of lexical prior probabilities for unseen cases than does an mle computed over the entire training corpus
the algorithm is applicable whenever the feature functions fi x y are nonnegative fi x y NUM for all i x and y NUM this is of course true for the binary valued feature functions we are considering here
we envision in practice a separate maximum entropy model pe y x for each english word e where pe ylx represents the probability that an expert translator would choose y as the french rendering of e given the surrounding english context x
the trigram perplexity numbers are shown in table NUM
filled pauses have unrestricted distribution and no semantic content
some examples of restarts and repairs are given below
in example NUM there are essentially two sentences
ex NUM a you interested in woodworking
some examples of complex restarts are shown below
these structures are relatively rare in conversational speech
since i NUM is large reducing its multiplicative factor in the time complexity would be mtractive
the accuracy of the algorithm is then the percentage of attachments it gets correct on test data using the a values taken from the treebank as the reference set
the first character underlined of the matched string in italics is part of a name and the second character in italics function as a verb thus chinese text is often pre segmented and string searching has to patch delimiters to the beginning and end of the pattern p
we came up with NUM categories in a later redesign we took the conjuncts themselves as our starting point and by tracing crossreferenees in dictionaries were able to reduce the initial number of NUM to NUM basic conjuncts divided over NUM categories
it must be noted that the evmuators were totally untrained in the context of the intended use of this method requiring a certain level of training seems acceptable and this would surely bring resuits closer to tile goal of evaluator independence
recoguising that the class of eonjuncts was to large for the evaluator to encompass at a glance we decided to implement an interactive q a interface on the computer in order to gradually guide the evahlator to the optimal choice of a conjanet
we might add that subjects using interfaces a and b were more likely to choose safe ambiguous vague conjuncts such as soshite and then and also for what it s worth complained more
this paper elaborates on the design of a machine translation evaluation method that aims to determine to what degree the meaning of an original text is preserved in translation without looking into the grammatical correctness of its constituent sentences
llowever we also observed several instances where the choice of a conjunct was dictated by the evah ator s prior knowledge or lack of it of the subject area this is a discrepancy we can not resolve
the conjunct mean is rather high this is probably due to the unexplaine high conjunct mean for b the conjunct means of a c and a i seem to correlate with the number of unintelligible sentences in the machine translated texts
these counts are important because they represent the contexts in which unknown words likely to appear
this amount was sufficient for some languages dutch german italian greek and spanish but insufficient for overview of results for greek
once we get them we can estimate word n gram count in an unsegmented japanese corpus
the guided composition mode enables the development of user friendly interfaces in which errors on the domain of the application never occur and in which non expected i.e.
finally the use of pronouns in particular clitic pronouns like in put them in the square a3 makes it possible to designate objects referentially
but given a motivating context an individualised surrounding environment and materials some of these children may be able to exteriorise capacities which had hitherto remained mute
the operating modes and interfaces the input output devices are defined to respond to the users needs to the best and to optimise their interactions with the system
we have carried out a survey in the field of currently available communication aids for autistic persons to try to determine the qualities and shortcomings of these systems
the development of surge itself continues
we then describe the structure of the grammar
NUM extending composite processes to include mental and verbal ones
an overview of surge a reusable comprehensive syntactic realization component
consequently while for clauses input can be provided in thematic form
nuclear roles answer the questions who what was involved
an fg is an fd with disjunctions and control annotations
since this document gives the overall design of the tipster architecture the developer may be able to determine where his modules would fit in the tipster design
finally even if the developer can find no reference to his proposed modules in the architecture design document he may look in the architecture requirements document
in addition the tipster architecture will process part types that are traditionally viewed as part of natural language understanding nlu such as communication headers
markups may be embedded within the document or they may co exist with it in the form of annotations possibly containing pointers to specific locations in the document
it will provide a starting point for application development because many if not all of the required components will be identified and their interfaces defined
because the main benefits from the architecture derive from its commonality its usefulness will be directly proportional to the number of applications which are built upon it
the tipster architecture is documented in an interface control document icd which specifies the form and content of the inputs and outputs to tipster modules
the architecture was developed to meet the need for us government agencies with similar text handling requirements to share some software modules and knowledge sources meeting these requirements
this is followed by a description of how the architecture and an application interact with one another and by a definition of an architecturally compliant application
in the majority of cases the set of modules in a given application will not be the same as the set of modules covered by the icd
we call the requirement NUM a constraint equation or simply a constraint
with joint distribution x p y x
systome de quota are more likely to be interchanged in the english translation
we do not require a priori that these features are actually relevant or useful
for each feature fi we introduce a parameter hi a lagrange multiplier
table NUM lists the first few selected features for the model for translating the english word run
for each position j in f we then construct a x y training event
figure NUM shows the system s segmentation of four sentences selected at random from the hansard data
the model was grown on NUM NUM training events randomly selected from the hansard corpus
these entities are intensional descriptions of classes of individuals and are often mentioned in administrative doc null uments since the entities persons or inanimate objects addressed in this kind of texts are not usually specific individuals in the mind of the public administrator but rather all the individuals that belong to a certain class as in the following example l married women should send their marriage certificate
all the entities directly related to the applicant or to the form itself can be considered anchored as for example the applicant s name the applicant s spouse any applicant s previous job see ion NUM of the h rm
i i ai oh ation which occurs when one clause provides more details for a topic presented in the previous clause c n ti lcb ast which links two clauses describing similar situations differing in few respects and so on
the basic constraint on center realization is formulated in the following rules rule NUM if any element of cf u is realized by a pronoun in u i then the cb u i must be realized by a pronoun also
the choice of the correct referring expression depends on two major factors a the cohesive ties that we want to signal to improve the cohesion of the text b the semantic features that allow the identification of the object in the domain distinguishing semantic features
within paragraphs words tend to be reused more often than expected under change conditions
the dotted line reveals the main developmental pattern time series smoothing using running medians
a second question concerns how lexical specialization affects the empirical growth curve of the vocabulary
in alice in wonderland key words are relatively rare in the initial text slices
as a result these text slices reveal fewer types than expected under chance conditions
for vu k f NUM NUM NUM NUM p NUM
this pattern may be due to the oscillating use of key words in max havelaar
successfully processed sample documents are saved to a unix file for future review
since the position is in terms of bytes it is the last matched position i minus the length of p in terms of bytes i.e.
aa aa in hexidecimal can successfully match with the second and third bytes of the text string t y deg NUM i.e.
whether the current input is a single or NUM byte character by testing whether the converted integer value of t i is positive or negative
the error occurs where the second byte of the character in NUM is interpreted as the first byte of the pattern character
the alphabet size of chinese to be more precise hanyu is relatively large e.g. about NUM NUM in hanyu da cidian compared with indo european languages
for example if p c ps psy i then the values in pi and p2 are shown in table NUM
in section NUM NUM we describe a maximum entropy model that predicts how to divide a french sentence into short segments that can be translated sequentially
NUM NUM expansion of sin face triples to deep triples
it represents the process of making a decision as a rooted tree in which each internal node represents a test of the value of a given property and each leaf node represents a decision
but like orgun s version our model extends this bias to suggest that all things being equal a changed surface form will also be close to its underlying form in phonological feature space
a single symbol such as t or v is a shorthand for a symbol that is the same in the input and output i.e. t t or v v
thus NUM simply denotes the current input segment while NUM voiced rcb tense means the unvoiced tense version of the previous input segment
NUM who used a cluster analysis on a dictionary of arabic to argue for a particular feature geometric grouping the relationship between feature geometries and empirical classification algorithms like decision trees clearly bears further investigation
we use square brackets to indicate which phonological features of the input segment are changed in the output the empty brackets in figure NUM simply indicate that the output segment is identical to the input segment
it does this by iteratively computational linguistics volume NUM number NUM choosing the single feature that best splits the data i.e. that is the informationtheoretically best single predictor of the decision for the samples
presumably this is because the ordering grouping similar segments together causes states reached on similar input symbols to be merged which is both linguistically reasonable and necessary in order to generate the correct transducer
in summary we believe that augmenting an empirical learning element with relatively abstract learning biases is a very fruitful ground for research between the often restated strict nativist and strict empiricist language learning paradigms
the probability of a configuration that is a dag is proportional to its weight and is obtained by normalizing the weight distribution
the erf method yields the best weights only under certain conditions that we inadvertently violated by changing l g and re apportioning probability via normalization
every way of changing a label that is every substitution of one ascii character for a different one yields a possible english word
one might expect the best weights to yield d fi q NUM but such is not the case
the standard parsing techniques can be readily adapted to the random field models to be discussed below so i simply refer the reader to the literature
b b b f16 NUM NUM the probability of a given tree is computed as the product of probabilities of rules used in it
to date however no satisfactory probabilistic analog of attribute value grammars has been proposed previous attempts have failed to define an adequate parameter estimation algorithm
the last training data is a chinese personal name corpus
for different types of surnames different models are adopted
and the base of transliteration is pronunciation of foreign names
the third training corpus is a transliterated personal name corpus
however our transliterated corpus only contain NUM personal names
that is these characters can not be put together
such complex structures make identification of organization names very difficult
thus only the first and the last characters are considered
from the variation of the performance we know that cache is powerful
second a keyword may appear m the abbreviated form
let c NUM x be the weight that m2 assigns to dag x it is defined to be the product of the weights of the rules used to generate x
the metropolis hastings algorithm provides us with a means of converting the sampler for the initial distribution po x into a sampler for the field distribution q x
comparing q2 the erf distribution and q to we observe that d llq2 NUM NUM but d llq o
we think that corpus based techniques are useful to significantly reduce not to eliminate the ambiguous phenomena
the set is called the candidate set for we
table NUM numbers of extracted words and word sequences
next section describes the details of the algorithm
both japanese and english texts are analyzed morphologically
the columns specify the numbers of approved translation pairs
the method is capable of getting interesting translation patterns
the correctness ratios for unknown words are NUM NUM
automatic extraction of word sequence correspondences in parallel corpora
under epsrc NUM grant gr k25267 the nlp group at the university of sheffield are developing a system that aims to implement this new approach
we then propose the use of a command language to interface the tools with more complex applications and we show that this technique facilitates integration of tools from various sources entails a better exploitation of linguistic resources and makes easier the distribution of tools on several machines
second in the translation process the system produces several candidates of translation results using translation rules extracted in the learning process
NUM determination of fitness value the system calculates the fitness value of the translation rules by the fitness function NUM
the results of evaluation experiments showed that the accuracy rate of translation increased from NUM NUM to NUM NUM by using genetic algorithms
in this table NUM NUM correspond to NUM NUM in subsection NUM NUM
c rossover l ositions are the conhlion parts and one t oiut crossovers for these two translation examples are performed
the conditions of the selection process are that the nunlber of uses is over NUM and the fitness value is under NUM
n NUM jo w NUM chome kita ku sapporo NUM japan e mail t ochinai c hudk hokudai ac
the purposes are to establish various high quality translation rules from only a small amount of data and produce high quality translation results
in the hope of rectifying these errors we consider the problem of context sensitive modeling of word translation
this naturally has a deleterious effect on the reliability and universality of evaluation results
themselves may be divided into categories but these can remain hidden from the evaluator
we also assume that the assignment of acceptable conjuncts is reader independent to a large degree
current systenl the project has also been successflll in that it has yielded a wealth of interesting data about sentence connections
without going into technical details the following were the main tasks in the implementation
again the means of a c i are fairly enormous indicating that size is still a factor
the other step was to instruct the evaluator to extract the topic and comment of the sentence under consideration
NUM extract the topic s and comment s of the sentence under consideration
figure NUM NUM tipster application lifecycle with configuration management gates
developers the cawg or the se cm
NUM NUM architecture request for change process NUM NUM NUM the goal
indeed while most collocations exhibit unique senses in a given domain sometimes a source collocation appearing multiple times in the corpus is not consistently translated into the same target collocation in the database
accordingly the ability to automatically discover collocations for a given domain by using a new corpus as input to champollion would ease the work required to transfer an mt system to a new domain
an additional direction for future experiments is to vary the thresholds and especially the frequency threshold tf according to the size of the database corpus and the frequency of the collocation being translated
important occurs a total of NUM times in the french part of the corpus and only NUM times in the right context whereas a minimum of NUM appearances is required to pass this step
our approach is based on the assumption that each collocation is unambiguous in the source language and has a unique translation in the target language at least in a clear majority of the cases
smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons and with a similar derivation for the upper bound i NUM pi rj NUM q i
neither of these problems occurs with the dice coefficient exactly because that measure combines the conditional probabilities of l s in both directions without looking at the marginal distributions of the two variables
if the variables x and y are transformed so that every NUM is replaced with a NUM and vice versa the average mutual information between x and y remains the same
next triplets are produced by adding a highly correlated word to a highly correlated pair and the triplets that are highly correlated with the source language collocation are passed to the next stage
champollion first forms all pairs of words in s evaluates the correlation between each pair and the source collocation using the dice coefficient and keeps only those pairs that score above some threshold
simultaneous adjunction is fundamentally ambiguous in nature and typically results in the creation of several different trees
hi order to implement our t eam search method appropriately but sinq ly we define an operation agenda pu q which selects pairs of active and passive edges to be prmn d or to be processed in the future
the greek letters v and p are used to designate nodes in elementary trees
the predicate subst x is true if and only if x is marked for substitution
every derivation in g maps directly to a derivation in g t that generates the same string
the right linear ordering inherent in these structures encodes the ordering information specified for a simultaneous adjunction
for those trees where NUM k substitution as specified by lemma NUM is applied again
if g contains empty rules then the tig created in step NUM will contain empty trees
we now assume inductively that ft holds for every ai rooted initial tree t where i k
each one must have a first nonempty frontier node labeled with aj where j k
the only realistic choice we had was to translate our parser with chestnut inc s lispto c translator automatically into c since the lisp functions library is available in c source we could insert the necessary solaris parallelisation and synchronization primitives into key positions of the involved fnnctions
the fact that tile dialogue module exercises a kind of global control does not invalidate what has bee n said about the unfeasability of central control be cause the control exercised by it is very coar e
the tipster cm process imposes two control gates pdr and foc on the tipster application development lifecycle as shown in figure NUM NUM
words ten three etc then that branch gets a certain score regardless of how spread out the words are within that branch
next another structure t r is created as a copy of the main one with a single word moved to a different place in the classification space
in practice the mutual information calculation is much less than o v NUM since there are far fewer than v NUM bigrams observed in our training text
we present an approach to the sparse data problem that shares some features of the similarity based approach but uses a binary tree representation for words and combines models using interpolation
one common model of language calculates the probability of the ith word wi department of computer science the queen s university of belfast belfast bt7 1nn northern ireland
an automatic word classification system has been designed that uses word unigram and bigram frequency statistics to implement a binary top down form of word clustering and employs an average class mutual information metric
the suboptimal strategy used in the current automatic word classification system involves selecting the locally optimal structure between t and t which differ only in their classification of a single word
with the structural tag representation each tag contains explicitly represented classification information the position of that word in class space can be obtained without reference to the positions of other words
a subsequential relation is any relation between strings that can represented by the input to output relation of a subsequential finite state transducer
for our purposes the key property is the first because determinism is essential to the state merging of the ostia algorithm
other algorithms make use of negative evidence in the form of transductions marked as invalid or questions directed at an informant
this section outlines the ostia algorithm to provide background for the modifications that follow see their original paper for further details
making generalizations about input segments would in effect reduce the alphabet size on the fly making the learning of structure easier
our final problem with the unaugmented ostia algorithm concerns phonological rules that are both very general and also contain rightward context effects
in their model each tier is represented by a finite state automaton and autosegmental association by the synchronization of two automata
table NUM shows our phone set an ascii symbol set based on the arpa sponsored arpabet alphabet with the ipa equivalents
a sample phonological rule the flapping rule for english shown in NUM is repeated in 2a
if a component has previously been used in another application its associated costs both to develop and to maintain may be known
speed and throughput of searches through the fdf hardware search engine was measured using a commercial fdf NUM system a single fdf NUM produced a search rate of around NUM NUM mb s which could be obtained while searching NUM to NUM average queries simultaneously
certain applications require that the output of an information extraction system be probabilistic so that a downstream system can reliably fuse the output with possibly contradictory information from other sources
while this is unlikely to be the best strategy for smoothing from the standpoint of probabilistic modeling we are constrained by the number of alternatives we can report to the downstream system
we now have a method for obtaining a model that assigns probabilities to the pairs of templates henceforth pairwise probabilities in a coreference set that can possibly corefer
also fastus also does not build up complex representations for the syntax and semantics of sentences placing limits on the extent to which such information can be utilized in determining coreference
for example a feature fl x y pairing the characteristic of s and t having identical slot values with the outcome that they corefer would be defined as follows
level of agreement and data reliability for a behavioral scientist results in table NUM would indicate that judgements produced by humans on the summary extraction are not to be trusted on the reliability scale in table NUM rates we get for the extraction data are somewhere between slight and fair
now a decision on whether or not a sentence should be included in a summary extract is said to be a majority opinion if it is positiuely agreed upon by n subjects where n ranges anywhere from NUM to the total number of subjects assigned to a task
our work is based on systematic and very detailed linguistic studies which lead to rather complex computations for calculating the spatiotemporal semantics of a motion complex
in this paper we present a semantic study of motion complexes ie of a motion verb followed by a spatial preposition
we use non monotonic logic in order to represent defensible or generic rules and also in order to encode defaults about lexical entries
this shows the neccesity to take into account such language specific behavior in natural languages understanding systems and in natural languages machine translation
these semantic properties can be characterized by a restructuration of the space induced by the so called reference location lref cf
when we enter some place or go out of some place we have different spatial relation with the location ie
null some denote a change of position which always occur voyager to trave d for example we can not say voyager sur place to travel in place
v vania avenue and j NUM vania are NUM NUM NUM NUM and NUM NUM respectively
our method has the advantage compared to beam search that there is no need for any particular search order to be followed when pruning takes place all constituents that could have been found at the stage in question are guaranteed already to exist
suppose that we have a general grammar for english or some other natural language by this we mean a grammar which encodes most of the important constructions in the language and which is intended to be applicable to a large range of different domains and applications
then the score of each edge is recalculated to be the minimum of its existing score and the scores of its start and end vertices on the grounds that a constituent however intrinsically plausible is not worth preserving if it does not occur on any plausible paths
that is our estimate for the probability that an edge with property p is correct is modulo smoothing simply the number of times edges with property p occur in correct analyses in training divided by the number of times such edges are created during the analysis process in training
the second set of experiments tested more directly the effect of constituent pruning and grammar specialization on the spoken language translator s speed and coverage in particular coverage was measured on the real task of translating english into swedish rather than the artificial one of producing a correct qlf analysis
sometimes errors were pointed out but no correction given in such cases we skipped over the error
equations NUM and NUM will also be used to tag sentences w and w with their most likely part of speech sequences
r5 and r6 map the second radical t for p al and pa el forms respectively
uninstantiated it is determined from the feature values in r5 r6 and or the word grammar see infra ss4 NUM
category symbol f eature attrl value1 eature attrn wlu n
the reason is that in such cases the predominant distinction to be made among the words is syntactic and the trigram method which brings to bear part of speech knowledge for the whole sentence is better equipped to make this distinction than bayes which only tests up to two syntactic elements in its collocations
the linguist provides semhe with three pieces of data a lexicon two level rules and word formation grammar all entries take the form of prolog terms
the base case of the predicate is simply coerce i.e. no more partitions
further disambiguation may be possible during aggregation across documents
the combination of the various components is inspected
but cases involving other parts of speech remain unresolved
nominator does not use this type of external context
proper names contain ambiguous conjoined phrases
nominator uses no syntactic contextual information
they are linked to the anchor
proper names also display semantic ambiguity
third system architectures have evolved toward increasing language portability NUM NUM NUM NUM NUM and fourth new acquisition techniques are accelerating development NUM NUM NUM
in lfc this is available in terms of f precedence
for qlf terms with simple restrictions i.e.
this is achieved by adding interaction terms such a s ul2 ij to the nmdel
we are also working on tightening the coupling of the speech recognition and translation modules of our system
the parser uses a disambiguation algorithm that attempts to cover the largest number of words using the smallest number of concepts
in this paper we describe the current design and performance of the machine translation module of our system
module designed to be more accurate and the phoenix module designed to be more robust
for a given input utterance the parser produces a set of interlingua texts or ilts
we analyze the strengths and weaknesses of each of the approaches and describe our work on combining them
the g lr parser also includes several tools designed to address the difficulties of parsing spontaneous speech
this in turn allows the g li lcb translation module to produce highly accurate translations for well formed input
the patterns are composed of words of the input string as well as other tokens for constituent concepts
these morphemes and words u e not in the dictionaxy
in addition it requires no further dictionaxt maintenance for new terms
figure NUM kakari uke pail with NUM hgs
we first created a prototype of our parser NUM using awk language and then rewrote it NUM in c so it could be included in applications
standard morphological analyser uses a dictionary to obtain morpheme or word candidates
a portable quick japanese parser qjp
thus instead of generating multiple number of sets it most likely set is selected ld the applie tion user is presented with alternative kakari uke pmrs at the same time that the selected pairs are presented
using qjp and its ana ysis results as a base and adding other functions for processing japanese documents a valqety of applications can be developed on unix workstations or even on pcs
etc and other characters alphabets nmnbers symbols etc are used to write japa mse can be used for segmenting words
this section describes the functionality of each module
therefore sentence analysis first tries to bind modifiers that have case marking particles each of which represents which case is unambiguously marked by particle spelling like ga japanese input sentence lt kg l l l lcb NUM b l o he introduced his sister to me c morphological analysis and dependency analysis dependency structure
g p pwa ls v all odjecuve case subjective her objective like he case marking case marking particle ga particle wo type NUM in this variant the case with adverbial particle wa acts as all adverbial phrase representing time and is actually a special form of type NUM
also in all but two nonzero cases the smoothed model makes grammatically correct sentences more likely and vice versa
however with the structural tag model extra word class information allows the system to prefer the more common noun verb pattern
in our main experiment this resulted in NUM sets of values corresponding to NUM different previous word frequencies
humans usually reconstruct the most likely sentence successfully but artificial speech recognizers with no language model component can not
another limitation can be seen if we consider a third hypothesized sentence the buoys eat the sand which is
automatic word classification systems are intrinsically interesting an analysis of their structure and quality is itself an ongoing research topic
nine versions of a phonemically identical oronym ordered by weighted average w a probability x NUM NUM
several attempts to find good chunking criteria are described in the papers by rayner and samuelsson quoted above
if we look more closely at the correspondence between dominance and nuclearity we find that the structure of spans and segments is nearly identical
exactly how to derive a communicative intention from an utterance and vice versa is one of the main research issues in computational linguistics
here we argue that g s and rst are essentially similar in what they say about how speakers intentions determine a structure of their discourse
the linguistic structure of a particular discourse is made up of segments which are sets of utterances related by embeddedness and sequential order
to illustrate how a speaker s intentions determine discourse structure in this theory consider the rst analysis of the example discourse from figure NUM
intentional structure describes the roles that discourse actions play in the speaker s communicative plan to achieve desired effects on the hearer s mental state
the key to the basic similarity between these two theories is understanding the correspondence between the notions of dominance in g s and nuclearity in rst
again our first goal in evaluating the segmentation data from our subjects is to explore the possibility that subjects given as little guidance as possible might yet recognize rather similar segments in the narrative corpus
evaluation judgements produced by subjects on a summary extraction task can be cast into an assignments matrix in a number of different ways
the assumption here is that a sentence with attitudinal expressions has more of a chance to be chosen as a s11mma ry
j p summary we discuss a particular approach to automatic abstracting where an abstract is created by extracting hnportant sentences from a text
an approach to abstracting by extraction typically makes use of a text corpus with labeled extracts indicating which sentence is a summary extract
thus they will stay in the limbo forever
NUM NUM different esl s are in the testset
local plausibility values are reported on the right
table NUM mutual information of co occurring esl s
the overlearning effect is common of feedback algorithms
first use a surface grammatical competence i.e.
table NUM performance values of the la without learning
right and left adjacent words or poss
wsj to other domains is not obvious
this makes it harder to achieve statistical significance
it was evaluated with a corpus based study that produced estimates of streak s sublanguage coverage extensibility and the overall effectiveness of its revision based generation techniques
unlike previous statistical disambiguation systems this technique thus combines evidence from bigrams trigrams and the NUM gram around an ambiguous attachment
while these edps enable an explanation planner to generate quality explanations we conjecture that employing a large library of specialized edps would produce explanations of higher quality
they are structurally and functionally identical to topic nodes i.e. they have exactly the same attributes and the children of elaboration nodes are content specifications
currently we count partial patterns equally but in future refinements we would 2examples of trailing adverb pairs are first off and right now
if it relied entirely on full patterns then if the pattern had not been seen kankei would have to randomly guess the attachment
testing on the NUM dialogs tables NUM NUM and NUM show the results for the best parameter settings from these experiments
the traditional approach has been to conduct an analytical evaluation of a system s architecture and demonstrate that it can produce well formed explanations on a few examples
second to make the level of the explanations comparable we asked writers to compose explanations for a particular audience freshman biology students
hyphenated includes number capitalized inflection short
model NUM enhances model NUM by considering the dependence of pr st itt on the distortion probability d i l j NUM m where i and m are the numbers of words in st and tt respectively
the probabilities of having a correct connection as fimctions of these fhctors are estimated empirically to reflect their relative contribution to the total confidence of a connection NUM from one aspect those words sharing common characters can be considered as synonyms tha would appear in a thesaurus
however if the thesaury eft rcb act is exploited the coverage can be increased nearly three tblds to about NUM at the expense of a decrease around NUM in precision race track so that they are safer for cars tc go round
instead of d i j NUM m a smaller set of offset probabilities o i i were used where the i th word of st was connected to the j th word of tt in the rough alignment
in their study they contend that NUM like statistic works better because it uses co null nonoccurrence and the number of sentences where one word occurs while the other does not which are often larger more stable and more indicative than cooccurrence used in mutual information
by relative distortion rd for the connection s t we mean j j i i where i th word s in the same syntactical structure of s is connected to the j th word t in tt
reconstruction is characterized as the specification of a partial correspondence relation between the unrealized head verb of an elided clause and its argument and adjuncts on one hand and the head of a non elided antecedent sentence and its arguments and adjuncts on the other
in the experiments without genetic algorithms high quality translation results coukl not be obtained due to the requirement of a very large amount of translation examples which are similar to other translatiml examples
tl soheat ll dt rs head dtr phon chocolatesl synhoc head ti easel ace subcattll eomp dtrs
the algorithm will fill some of the complement positions in the subcat list of the reconstructed v with the np arguments in the ellipsis site and it will fill the remaining positions with arguments inherited from the antecedent head v
if it fills the direct object third complement position of this list with the bare np then it will fill the subject and indirect object positions with the local features of john and mary generating the reconstructed feature structure corresponding to NUM
the arguments of the predicate correspond to the fragments in the ellipsis site and ellipsis resolution consists in finding an appropriate value for the predicate variable which can apply to both the sequence of arguments in the interpretation of the antecedent clause and the sequence of arguments in the ellipsis site
in this article we introduce an approach under development in a joint collaborative project between the technical universities of darmstadt and budapest speak that combines the dialogue modelling paradigm with nl generation and speech synthesis in an information retrieval system
the cm manager cmm will report to the tipster se cm program manager
documented and approved cm practices and procedures are followed by all team members
NUM NUM referenced and cm documents the following documents were referenced in preparing this document
it will also facilitate the sharing of developed software between government agencies and offices
internal component processing capabilities do not come under the scope of v v testing
the erb reviews each class ii change and then takes appropriate action for its disposition
configuration management prepares publishes and incorporates the change pages in the defining documentation
incoming and outgoing shipments of cm controlled items will be handled by the cmm
this cooperation will be in the form of an engineering review board erb
the f h item is currently only a pilot item for the graduate record examination gre which administers approximately NUM NUM examinees yearly
characterization of police training data the training set responses have insufficient lexico syntactic overlap to rely on lexical co occurrence and frequencies to yield content information
third the user proofreads the translated sentences
as well for processes the template is not a static structure but changes for each type of process according to its roles which are retrieved from the underlying knowledge base
if however the pattern was the word those followed by any number of words followed by a word with a concept matching believe then the sentence would not be retrieved
various resources the upper model for instance play an essential role in providing knowledge to guide the development of the spl plan but were virtually inaccessible to all but the penman expert
the vast number of possible keywords and values coupled with the absence of any facility for storing and searching these items made the task of building spl plans very frustrating for the novice penman user
in addition having a template display the allowable components of a particular type of spl plan guides the user in exploring the penman system and in learning how the different parts of an spl plan interact
as well such a facility should provide access to a range of resources such as grammars and other linguistic resources that can aid the user in input development
the user can retrieve the plan for a particular token in the sentence bank and modify it or use it to aid in the construction of a new plan
processes can be thought of as verbs and other relational concepts objects as nouns and qualities as modifiers of processes and objects i.e. adverbs and adjectives
each word in the sentence bank is annotated with up to five levels of annotation spelling lexical item part of speech grammatical function and upper model concept
ideally it should also maintain a library of sample input specifications so that the user need not reconstruct existing specifications but can construct new ones from an existing set
therefore deriving a markov model by model merging is o l NUM in time
the criterion for selecting states to merge is the probability of the markov model generating the corpus
each path gets the same probability l u with u the number of utterances in the corpus
the trivial model assigns a probability of p sim NUM NUM to the corpus
thus the model found by model merging can be regarded generally better than the bigram model
it can not be skipped from the beginning because somehow the time complexity has to be reduced
the transition from one state to another is done according to the probabilities specified with the transitions
this paper investigates model merging a technique for deriving markov models from text or speech corpora
the fourth method is a variation of the third method and is also used for part of speech tagging
its time complexity is linear to the sequence length despite the exponential growth of the search space
the sentences in NUM exemplify two familiar alternations of give
classifying a sentence by its alternation type requires linguistic and world knowledge
properties such as liquid are used to define specialized entities
one task for which a terminological language is appropriate is flagging inconsistent rules
NUM a john gave mary a book b
once the account is represented the terminological system can flag inconsistencies
specialized verb classes are defined by their good and bad alternations
terminological languages can support three important functions in this domain
at present the ordering of roles in the list is not significant but it could be made so to constrain grammatical salience etc
one of the main steps in the text generation process involves content selection the selection of information from the speaker s knowledge base for presentation
object of negotiation speech acts can negotiate information questions statements etc or action commands permission etc
once we have specified what the speech act is doing and who the participants are we need to specify the ideational content of the speechact
for representing concepts which are domain specific e.g. bodyrepairer users provide domain models where domain specific concepts are subsumed to concepts in the um
i say less here because although wag has extended the level at which surface forms can be specified semantically there are still gaps
entities which are not believed to be shared require some form of indefinite deixis e.g. a boy called john eggs some eggs etc
other aspects are also defaulted for instance the relation between the speaking time and the time the event takes place realized as tense
the gde was developed as part of the alvey natural language tools project in the uk
it embodies a modified version of gpsc which is easier to implement than its theoretical counterpart
the data relative to cliticization and agreement have dictated the structure of the french vp and np
this leads to a description of the major structural differences between tile two grammars
l roperties as a ttest i in cxanq le sentences are exl loited as a clue l o the most likely NUM b leci
a ttested ones axe a lso stored on a pa r with actum co occurrence NUM atterns t o be used by the im rential routine of the system
taxonomical information is cm oded in lowerease intcrpretare h means that the verb interp wl a w interpret is the h yperonym of leg gfs he
we have provided methods for handling certain classes of unknown words and models for other classes could be provided as we have noted
by capturing a set of good terms for example relevant documents can be searched and retrieved from a large document collection
the overall ranking for the prototype system falls between somewhat useful to very useful depending on the topics
figure NUM the gui component described above is implemented using the c programing language and the osf motif graphical user interface toolkit
for the purpose of generating key terms for our prototype system we have adopted a learn data from data approach
once the significant terms of these four types are identified a comparison algorithm is applied to differentiate terms across the two samples
the friendly and interactive gui toolkit allows the user to visualize the key terms in context and explore the content of the original dataset
the gui component is divided into two main areas one for interacting with key terms structures and one for browsing targeted document collections
single word terms are represented as root nodes and multiple word terms can be positioned uniformly below the parent node in the term hierarchy
symbol represents a voiced closure while symbol represents an unvoiced closure
the extraction of diphones from the recorded words is performed in two stages
diphone inventory cotnprising NUM pitch labelled diphones was created
diphone inventory acquisition for the slovenian language was discussed
l igure i slovenian text to speech xystem architec
the discrepancies between manual and automatic segmentation are considerable
segmentation and labelling of slovenian diphone inventories
lb phonetically transcribe the logatom words we
results of the statistical evaluation of manual and automatic segmentation discrepancies are given
one diphone tor every allophone combination possible in a given language is required
the total divergence d lql NUM NUM
repeat until the weights no longer change
we can impose such a constraint by means of an attribute value grammar
throughout we will use the following stochastic context free grammar for illustrative purposes
two trees are missing and they account for the missing mass
eisele recognizes that this problem arises only where there are context dependencies
however it is a potential source of ambiguity in text analysis since a low frequency form in en where one may not have seen the stem of the word could potentially be either a noun or a verb
note however that the ratio of residual person names NUM is considerably higher than the ratios of residual location and organization names
this description and measurement of the data it would cover was not attempted due to time constraints and the complexity of the problem
our approach to this problem will be described elsewhc re
the frequencies and the occurrence counts are summed up respectively
similarity is based on NUM grams in common between the contexts
NUM NUM at i roaehes to featm e matelfing
l his abilily is also measm ed by developing the word sense dismnbiguator whic h
tures e.g. neither x or y is red
tame NUM xamph of abstracted triple
as mentioned above features extracted fi om the corpus will be represented using synsets concepts in ifsm
in our model each word is represented by an s bit number the most significant bits of which correspond to various levels of classification so given some word represented as structural tag w we can gain immediate access to all s levels of classification of that word
is the last character of the word a period
there are NUM NUM such cases in the training data
the contingency table was smoothed using a loglinear models
with a loglinear model on the other hand
theoretically every character can be considered as names rather than a fixed set
lexical association strength between the verb and the preposition
in contrast to studies investigating a single feature we investigate three types of linguistic devices referential noun phrases prosody and cue phrases
here we present the results of a study investigating the ability of naive subjects to identify the same segments in a corpus of spoken narrative discourse
so does an implicit argument as in figure NUM where the missing argument of notice is inferred to be the event of the pears falling
the performance of this learned decision tree averaged over the NUM training narratives is shown in table NUM on the line labeled learning NUM
are as shown in the first line of the figure or for a spoken language system where the value of cue prosody is complex
subjects were free to assign any number of boundaries and to label their segments with anything they judged to be the narrator s communicative intention
on average this gives us NUM tj NUM or NUM tj NUM boundaries for a NUM phrase narrative
however all NUM narratives show the same pattern of responses as illustrated in figure NUM certain boundaries are identified by large numbers of subjects
however it nlight suffer from the sparse data problem because the total number of word tokens for training is decreased from NUM to NUM
word frequencies were estimated by the viterbi reestimation a reesthnation procedure using the best analysis from an unsegmented corpus of NUM million words
we call p k the word length model and p cl ck i k the word spelling model
one potential benefit of our statistical model and segmentation algorithm is that they are completely independent of the target language and its writing system
to determine this i will consider the probability mass of the frequency classes v m f for f NUM NUM
the first one was no reestimarion it uses the word segmenter s outputs as they are when extracting new words
for max havelaar a sample comprising the first third of the novel was used for the other texts a sample consisting of the first half of the tokens was selected
the upper panels plot vu k left and nu k right the numbers of underdispersed types and tokens appearing in the successive text chunks
it is conceivable that a description with say affliction as a single role element could be maintained in the specific case of the contrast between wound and disease we find in metaphor further support for our decision to keep them separate
the right hand panels of figure i show the overestimation error functions e v n v n corresponding to the left hand panels using dotted lines
such locally concentrated clusters of words are at odds with the randomness assumption underlying the derivation of NUM and may be the cause of the divergence illustrated in figure NUM
the absence of a trend is supported by the proportions of underdispersed types and tokens shown in the second row of panels f NUM for both types and tokens
the panels on the third row of figure NUM show the frequencies of ahab left and vu k as a function of the frequency of ahab right
the expression for e v n given in NUM that we have used thus far does not allow us to extrapolate to larger sample sizes
further analyses of subsets of derived words syllables and digrams showed that the overestimation bias reappears in units derived from words when these words occur in normal cohesive text
it is seen that the values remain rather high still with a small variance
in this paper we describe our method with genetic algorithms and evaluated it by some experiments
a practical and high quality method of machine translation is important for the internationalization of japanese society
clustering similar phenomena is an obvious way of reducing the problems just outlined
occurring texemes only encoded in capital letters
translation exampies are randomly changed by nmtation at a rai e of NUM
the rule realizes an underlying t as a flap after a stressed vowel and zero or more r s and before an unstressed vowel
the calculation of alignment information adds a preprocessing step to the algorithm that requires o nm time for the dynamic programming string alignment algorithm
notice that in the correct transducer the arc from state NUM to state NUM is labeled with c and v while in the incorrect transducer the transition is missing six of the vowels
p wilti p unknownwordlti x p capitalfeature ti x p endings hypenationlti
however the pos tagging accuracy on the penn wall st journal corpus is roughly the same for all these modelling techniques
the parameters lcb p al ak rcb are then chosen to maximize the likelihood of the training data using p
it uses a rich feature representation like tbl and sdt and generates a tag probability distribution for each word like decision tree and markov model techniques
as a result no word classes are required and a trivial count cutoff sufrices as a smoothing procedure in order to achieve roughly the same level of accuracy
the testing procedure requires a search to enumerate the candidate tag sequences for the sentence and the tag sequence with the highest probability is chosen as the answer
this paper presents a statistical model which trains from a corpus annotated with part of speech tags and assigns them to previously unseen text with state of the art accuracy NUM NUM
for most words the specialized model yields little or no improvement and for some i.e. more and about the specialized model performs worse
the implementation in this paper is a state of the art pos tagger as evidenced by the NUM NUM accuracy on the unseen test set shown in table NUM
after the initial tree is constructed using the alignment information the above mentioned worst case bound still applies for the process of merging states it does not require that the initial tree be onward
for handling our task we have chosen tile so called version space method also known as the candidate elimination algorithm cf
we can trivially add the relation to the present learner but the other parts of such proposals seem beyond its immediate capacity as it stands
wu ie i i y the program only once and so they occtu just once in the expressions
in other words being generalization driven the generator never produces training instances which arc superfluous to the generalization pro ess
another direction for filture research is addressing the learning of word order expressed in more complex formalisms than i1 lp grammars
n num det num n num
below we discuss the basic aspects of tile implementation illustrating it with the ll grannnar wil h no lp restrictions given on figure NUM
in all ll i p grammar the two types of information constituency or immediate dominance and linear order are separated
a system is descril ed which learns fl om examples the linear precedence rules in an immediate dominance linear precedence grammar
figure NUM illustrates the lp rules space of a determiner of some grammatical nulnber singular or l lura l
across languages the keywords press conference retrieved a rich subcorpus of texts covering a wide spectrum of topics
this contrasts significantly with human performance data on a more complex information extraction task in muc NUM NUM
the scoring software performs two processes mapping and scoring
fifth resource sharing continues to play an important role in fostering technol null ogy development
in terms of the evaluation methodology a number of lessons were learned from this experimental evaluation
this overview paper is followed by three papers discussing the task by language
an additional contribution of mer was the basehning of human performance table NUM
the scores in tables NUM and NUM are the f measures obtained by the scoring software
the consistency scores are the f measures resulting from comparing the two analysts answer keys
preliminary results indicate that met systems in all three languages performed comparably to those of the muc NUM evaluatien in english
if the empty rule in figure NUM is eliminated by substitution a grammar identical to the one at the bottom of figure NUM results
the step of the gnf procedure corresponding to step NUM of the ltig procedure lexicalizes the a1 rule by substituting the a2 rules into it
if the ltig created in figure NUM is converted into a cfg as specified in theorem NUM the rules in figure NUM are obtained
a clatuse with the conjunctive p lrticle to express reason node too el can contaii subjective noun phrase and an auxiliary verb of past tense t d NUM while a clanse with the particle indicating attendant action agara d NUM can not as shown in NUM NUM
retention of the original document is still required
revision numbers shall be associated with correction annotations
tasks may be canceled or the mode changed
in the first type of case like that just illustrated it is used when it is not known how many instances of a category will be encountered
but this very fact will mean that the interpretation of all the adjps encountered will be constrained to have the same value for sere as the first one processed
it is also very often convenient to allow for macros expanded at compile time to represent in a readable form commonly occurring combinations of features and values
this paper describes an alternative approach towards such a combination via the compilation of apparently richer grammatical notations into expressions whose satisfaction can be checked by unification alone
the mother vp meaning is taken from the pp which will simply conjoin its own meaning to that of the daughter vp if the pp is a modifier
hierarchies of this sort when they have a top element have the defining property of lattices that every pair of types has both a glb and lub
this will cause an attempt to unify the first and last arguments of the term which being NUM and NUM will cause the unification to fail
the architecture shall provide for specific interfaces and protocols
in the human human setting there was a nontrivial but low level of accommodation
but what other justification could there be for saying that accommodation is taking place
then the subject who did not use an item first was accommodating
results discussed below will shed some light on the interaction of these two factors
since the interaction was also humanhuman social standing continued to be a concern
this result would also have the effect of further increasing lexical accommodation from users
because even lexical accommodation is rarely a conscious act speakers intuitive judgments are not helpful
thus lexical accommodation is an important conversational strategy for speakers who do not share linguistic conventions
lexical choice was an obvious strategy for establishing shared linguistic behavior and thus promoting effective communication
however most ie systems including fastus have pursued a deterministic strategy for merging and report only a single possible state of affairs
if there are NUM templates and NUM coreference configurations are possible then the answer derived by the greedy strategy would receive probability NUM
modeling at the level of coreference sets ensures that the probabilities are consistent when considering the global state of affairs being described in the text
NUM jsentencei and esentencej do not cross any sentence pair that has more than anc word correspondences
task performance was evaluated in terms of two metrics the amount of time taken to arrive at a solution and the quality of the solution
this suggests that while our robustness techniques were effective on average the errors do create a higher variance in the effectiveness of the interaction
we present an evaluation of the system using time to completion and the quality of the final solution that suggests that most native speakers of english can use the system successfully with virtually no training
we use a hierarchy of speech acts that encode different levels of vagueness including a tell act that indicates content without an identifiable illocutionary force
these results indicate that equivalent amounts of training data can be used with greater impact in the language model of the sr than in the post processor
we did not train with all of our available data since the remainder was used for testing to determine the results via repeated leave one out cross validation
i NUM NUM x iix x NUM xx NUM i NUM trains NUM words in training set
the channel model is trained by automatically aligning the hand transcriptions with the output of sphinx ii on the utterances in the speechpp training set and by tabulating the confusions that occurred
unlike other commercial systems the logos system relies heavily on semantic analysis
the second concerns the methods used if any to extend the lexicon beyond the static list of entries provided by the machine readable dictionary upon which it is based
in the second utterance of the storm is still raging and that s why the plane is grounded the demonstrative pronoun that illustrates an example of discourse deixis
our exhibit at the NUM th conference on applied natural language processing will offer live demonstrations of the logos translation express tm system
in addition to the easily extensible dictionaries with their underlying semantic foundation logos offers mechanisms that permit meanings to be deduced using contextual clues
our architecture coupled with experience in the commercial sector has made us a leader in providing solutions in our users translation work
using our proprietary semantico syntactic abstraction language sal the parser is able to achieve better results than syntactic analysis alone would allow
from an english vietnamese system produced in NUM to the latest releases of our software we have striven to produce the best machine translation mt software available
this intemet based system allows users to submitted formatted documents for translation to our server and retrieve translated documents without loss of formatting
all aspects of the sentence contribute to the result
although the logos system was originally developed on the basis of a transfer approach this has evolved in time to the present in which clear intedingual features are inherent in the system
our results showed that the algorithms performed quite differently from one another on boundaries identified by at least four subjects on a test set of NUM narratives from our corpus
gross NUM brundage et al NUM nunberg et al NUM that most mwls can not be treated like completely fixed patterns since they may undergo some variation
but an expert privy to the fact that NUM was among the next few words might be more inclined to select greater or higher
those examples like r j and which meet the character condition do not look like transliterated names because their pronunciations are not like foreign names
similarly when substitution nodes undergo contraction the algorithm has to ensure that the tree recognized dile by pr dicting a substitution is shared by the nodcs e.g.
computation of a is described below
NUM NUM correlation of segmentation with utterance features
passonneau and litman discourse segmentation table NUM additive algorithms
by allowing machine learning to use global pro
then if cue1 true then if global pro
if the value is sentence final contour
thus the main need for improvement is in recall
the instantiations for the variables x y and t in table NUM are obtained automatically from the training data
the lack of improvement implies that either the feature set is still impoverished or that the training data is inconsistent
the words ago chief down executive off out up and yen also exhibit similar bias
it then discusses the consistency problems discovered during an attempt to use specialized features on the word context
thus the constraints force the model to match its feature expectations with those observed in the training data
the maximum entropy maxent tagger presented in this paper combines the advantages of all these methods
multiplying together all the probabilities becomes less convincing of an approximation as the information sources become less independent
without the tag dictionary the search procedure generates all tags in the tag set for every word
therefore the model uses the heuristic that any feature less than NUM times in the training set
picard is currently being used to plan enghsh and spanish text in the mikrokosmos machine translation project
that system employs constraint satisfaction branch and bound and solution synthesis techniques to produce near linear time processing for knowledge based semantic analysis
the work builds on the hunter gatiterer analysis system beale
this paper NUM introduces a new line of research which ensures soundness and completeness in natural language text planners on top of an efficient control strategy
initially the frequencies would be generated using our hand tagged corpus examples eventually we hope to be able to train on the hand tagged examples and ultimately automate at least partially the tagging of instances at least for preliminary word sense disambiguation to be reviewed by a researcher
first it is clear that the size of the descriptions will increase rapidly as the annotation proceeds and we will need to find some explicit means of abbreviating representations of collapsing fegs in a principled way and of relating frames together both within and across semantic fields
some frames encode patterns of opposition that human beings are aware of through everyday experience such as our awareness of the direction of gravitational forces still others reflect knowledge of the structures and functions of objects such as knowledge of the parts and functions of the human body
iie i is happy universal quantification and implication are internally dynamic
measure NUM seems a natural choice when there are two coders and there are several possible extensions when there are more coders including citing separate agreement figures for each important pairing as kid do by designating an expert counting a unit as agreed only if all coders agree on it or measuring one agreement over all possible pairs of coders thrown in together
nonrandomness in word usage not only introduces a bias with respect to the expected vocabulary size overestimation when interpolating and underestimation when extrapolating it also affects the accuracy of the good turing estimates
the score is then norma ised by the nnrober of words used for the comparison equivalent to the numbers of degrees of freedom to give a measure we shall call cbdf chi by degrees of freedom
the general form of the multilevel smoothed bigram model is
buoy is an unseen vocabulary item in this test
we explain why the dice coefficient meets these criteria and why this measure is more appropriate than another frequently used measure mutual information
as explained above such a change should not be considered similarity preserving since NUM NUM matches are much more significant than NUM NUM ones
in this section we discuss the design of the separate tests and our evaluation methodology and present the results of our evaluation
si x y also suffers disproportionately from estimation errors when the observed counts of l s are very small
both of these measures depend on the individual probabilities or relative frequencies of the word groups 2xp x NUM NUM
ultimately such techniques would be more useful than those currently used because they would be able to extract knowledge from noisy data
thus the ability to compile a set of translations for a new domain automatically will ultimately increase the portability of machine translation systems
in cases where the database of texts includes documents written in multiple languages the search query need only be expressed in one language
we plan to refine our scoring method so that the length number of words involved of the events is taken into account
while it might seem plausible that oddities would in some way balance out to give a population that was indistinguishable from one where the individual words as opposed to the individual texts had been randomly selected this turns out not to be the case
there are at most o nk iterations through the states since at least one node of one state s decision tree must be pruned in each iteration
while more frequent words tend to be seen earlier in a corpus there is no reason to think that more frequent words provide better chances of successful state mergers
the process of pruning the decision trees is complicated by the fact that the pruning operations allowed at one state depend on the status of the trees at each other state
this algorithm has no domain knowledge about phonology and so is unable to classify together similar phones or generalize across phones that were missing in the input data
we hope in this way to begin to help assess the role of computational phonology in answering the general question of the necessity and nature of linguistic innateness in learning
the second property is merely a convention any transducer with multiple input symbols on an arc can easily be transformed into one with single arcs with one symbol each
generating a surface form from an underlying form is more efficient with a subsequential transducer than with a nondeterministic transducer as no search is necessary in a deterministic machine
in addition karttunen s transducers may have only zero or one symbol as either the input or output of an arc and they have no special end of string symbol
when building the initial tree transducer the alignment is used to ensure that no output symbol appears on an arc further up the tree than the corresponding input symbol
for the r deletion rule in NUM the algorithm induced a machine that was not the theoretical minimal machine NUM states as table NUM shows
if e is passive then for all active a such that e f vm NUM a to and combined score a e beam value insert a e combined score a e into the actual agenda
estimate of the probability of the infinitive is NUM NUM
i have argued that a detailed analysis of the distribution of key word tokens and types may shed some light on why the theoretical vocabulary size sometimes overestimates and sometimes underestimates the observed vocabulary size
baayen and sproat lexical priors for low frequency forms table NUM
in other words pauses are more fl equently inserted after the clause of the higher conjunction levels than those of the lower levels
statistical data should also be collected fi om human speech and reading in regard to the correlations between pause length and the ldg conjunction levels
taking the comma effect into consideration we can conclude that there is a solid correlation between the ldg conjunction level and the pause length
after that the discourse structure assumption module gives priorities to each possible syntactic structure using the modification prefl rence based on modality
the presumption function ibr sentence structure lexical discourse by ldg is applied to pre processing ahead of speech synthesis in a text to speech system
one is the difference in the character types of the two languages
this step adjusts asm using the am constructed by the above operations
for text NUM combined and statistics perform much better than dictionary
thus it is essential to decide sentence alignment on the sentence sentence basis
in this section we describe the statistics used to decide word correspondences
these newly found anchors make word correspondences more precise in the subsequent session
we will treat hereafter japanese english translations although the proposed method is language independent
our system iteratively aligns sentences by using statistical and on line dictionary word correspondences
bilingual dictionaries and glossaries have been developed for spanish arabic japanese and russian
because neither client nor interpreter had a dominant role in the conversation we could not predict the direction of accommodation
the glossary is clearly dependent on the kind of text included in the corpus being used
there is evidence for longrange mutual accommodation in that setting as compared to short range accommodation in the machine interpreted setting
the results discussed below did not differ for these two modes thus we will not distinguish them here
this explains why the rate for lexical accommodation in the machine interpreted setting is lower than that of the human interpreted setting
the machine interpreted setting only indirectly involves human human interaction all dialogue is mediated by the machine interpreter
figure NUM frequency of use of words in common for agent and client in each setting
coincidental overlap of course a certain amount of lexical overlap is inevitable as a simple artifact of cooperative conversation
as we conjectured above clients accommodated to the machine as part of a strategy for effective communication
the main source of np completeness is the following common structure of these problems they all search for an entity that maximizes the sum of the probabilities of processes which depend on that entity
first candidate translations were generated for each pair of aligned training sentences by taking a simple cross product of the words
drastically reducing the size of the training corpus has a much smaller impact on lexicon quality when these knowledge sources are used
the large number of quantitative lexicon evaluations required for the present study made it infeasible to rely on evaluation by human judges
i distributed NUM confidence intervals were estimated for each score using ten mutually exclusive training sets of each size
the best precision for the single best translation is achieved by a cascade of the mrbd cognate and word alignment filters
the scores for the cascade of all the filters the highest curve are close to the human performance of NUM NUM
between french and english this heuristic works quite well except when it comes to the order between nouns and adjectives
to overcome this problem the hidden state alphabet and the observation symbol alphabet should contain not only single characters single graphemes or phonemes respectively but also clusters
we have also made use of public domain word lists and consulted an on line electronic dictionary for more commonly used lexical items
the project is still at an initial stage and there are many other issues that need to be addressed such as performance and coverage of the mistake types
other features of the system include modularity and interchangeability of components rapid component integration and a debugging environment
we also thank our beta testers especially laura siegel and jinah park who have shared their time to test the system
patterns are described in mop by left to right enumeration of components with each component specifing at various levels of descriptive granularity
mop also allows for rapid integration of a variety of analytical modules such as part of speech taggers and parsers
the patterns are compiled into perl scripts which perform back tracking search on the input text
the system contains demonstrated advancements in part of speech tagging end of sentence detection and coreference resolution
the institute for research in cognitive science email lcb breck cdoran jcreynar niv srini hnc ci
nothing turns on the fact that it uses a primitive version of event semantics
these are the natural points of articulation in the domain of strings
and ran quickly on the one hand and john on the other
a parser is a transducer from strings to structures or logical forms
the best performance in terms of edge counts of the figures we tested was the model which used the most information available from the sentence the prefix model
while several parsers described in the literature have used such techniques there is no published data on their efficacy much less attempts to judge their relative merits
in the ideal model the p to term acts as a normalizing factor
in general this can lead to the creation of new more encompassing constituents which themselves are then added to the keylist
null the per word inside probability of the constituent nj k is calculated as we will refer to this figure as normalized NUM
to derive an estimate of this quantity for practical use as a figure of merit we make some additional independence assumptions
by exhaustive parsing we mean continuing to parse until there are no more constituents available to be added to the chart
in this paper we examine the performance of several proposed figures of merit that approximate it in one way or another
donkey sentence and the problem consists in providing an adequate semantic representation of the anaphoric links
let a k denote the node at address k where a is the non terminal labeling that node
corresponding to tj NUM subjects as in the previous section and those derived using a less conservative level of NUM
only inflected forms of polish words phrases are created in this phase
the language texts were supplied by the linguistic data consortium ldc at the university of pennsylvania
this method assumes that japanese function words such as conjunctive particles postpositions located at the end of each clause have modality and suggest global structures of japanese long sentences in cooperation with modality within predicates or auxiliary verbs
ltere japanese conjunction iltltllls such as toki lt when are nouns that can often be nsed just like col juncdve l articles when alley are attached at the end of clause
for example combining i evt c ixw i and i mrr cwxa z clauses yields a rule that tests for the left bigram contcxt
the performance of our hand crafted rule sequence is summarized in table NUM below which gives component scores on the mt3c NUM blind test set
for instm cc whcn the learner must break tics between identically scoring rule candidates it often does so in lhlguistically clumsy ways
in order to reduce the huge number of syntactic structures of japanese hmg sentences and give priorities to each possible structure the analyzing method based on ldg uses global modality structure focusing on lexic tl information
conjunctive particles which are located at dm end of clauses and which link them are classified according to the elemeni s that the clause can contain or to the correh tion between clauses
ms the boxplot shows the results from the loglinear model for the v np pp pattern do not show any significant improvement
a few english verbs may correspond to one polish verb depending on the type and the order of its modifiers
inventing analyses that cover specific phenomena is fairly easy
in t articular it should be
la lie has lived in bray for five years
NUM he had lived in bray for five years
with a well defined end point was in progress at some time in the past and that it is reasonable to suppose that this end point was eventually reached ihmrietta did cross the road
for the remainder of this section i will say that the relationship specified by an aspect marker holds between a tilne and all event type where an event type is nothing more
these tests show that the ideal human written letters are obviously thc best
these processes are the docu null ment input di the document processor dp the document management dm the management information system manager mism the problem queue manager pqm the system adaptation manager sam the administration manager am and the output manager function om
from the am gui the user can authorize others to print display search consolidate and delete the computer security audit log as well as add delete or re enable accounts by changing user permissions
once created sam allows the data administrator to test their mapping template changes against sample files of documents
for each document om walks through the annotations sgml tags accessing their associated sgml tag value
adept tags documents in a uniform fashion using standard generalized markup sgml according to oir standards
with odbc the sybase system NUM database can be substituted with any odbc compliant database on any platform
if the mapping template can not be identified the stream probably came from a source unknown to adept
the sgml tags delineate predefined document segments such as title publication date main body text etc
mism records the document s name source date time stamp and other relevant information when a document is received by adept document is successfully tagged problem document is identified and document is transmitted to main rose catcher
the most typical adjectives in the member subclass are authentic NUM fake NUM and nominal NUM
the set of all ucc node set is denoted by p
the part of speech labels proved useful in finding boundaries such as those between organization names and text which is not one of the met categories
if the two events x and y stand for the occurrence of certain word class unigrams in a sample say ci and cj then we can estimate the mutual information between the two classes
in many contexts automatic analyzers can not fully disambiguate a sentence or an utterance reliably but can produce ambiguous results containing the correct interpretation
this may be illustrated by the following diagram a p2 p3 where we take the representations to be tree structures represented by triangles
if interpreter is given it means that an expert system of the generic task at hand could not be expected to solve the ambiguity
attempts have also been made on french texts and dialogues and on monolingual telephone dialogues for which analysis results produced by automatic analyzers were available
second there is a matter of taste and consensus although different representation systems may be formally equivalent researchers and developers have their preferences
returning to g we might then say that the representation of u is the disjunction of all trees t associated to u via g
p1 p2 pm are all proper representations of u in r and pl p2 pn are the parts of them which represent v
for example anaphoric references and syntactic functions may be coded by the same kind of attribute value pairs but are usually considered as different ambiguity types
for example syntactic dependencies may be coded geometrically in one representation system and with features in another but disambiguating questions should be the same
we noticed that cumulative error is especially a problem in spontaneous speech systems where unexpected inpnt disfluencies out of domain sentences and missing information cause the deterio ration of the quality of context
but automatic recognition of speech in particular speech by non natives has only taken its first steps outside the laboratory and many language teachers still judge synthesized speech of insufficient quality to serve as a model for language learners
modern nlp techniques for the analysis and generation of written language in combination with graphical tools for visualizing and manipulating the structure of words sentences and texts afford excellent possibilities for creating integrated curricula for grammar and writing instruction
this too works against the profitable deployment of nlp tools in call because automatically generating non trivial communicatively interesting and instructive dialogues does not yet seem within reach of nlp technology let alone the evaluation of student responses from the point of view of successful interpersonal communication
if anything has brought about a metamorphosis in second language teaching practices it was the introduction of affordable video audio and other graphical and acoustic tools that under the control of flexible software can create an illusion of total immersion the supposedly ideal language learning situation
these tools let a teacher construct exercises that invoke specific linguistic concepts in the target language without having to deal directly with the nlp system
this requirement can also be stated as follows NUM NUM NUM
using a pedagogical strategy of situational immersion the system engages the student in meaningful multi media communicative acts in graphically depicted real world situations
synword ae vocalism measure pa el
partition partitions a two level analysis into sequences of lexical surface pairs each licenced by a rule
lexical alphabets tl set radical k t b
rules are then reasserted in the order of their precedence value
synword aa vocalism measure p al
this ensures that rules which contain the most specified expressions are tested first resulting in better performance
table NUM lists average agreement rates for data with thresholds ranging from NUM NUM to NUM NUM
length this attribute records the length given in character of a sentence
for which generally accepted proposals exist but whose ilnplelnentation in the context of par aim grammar development throws up quest ions as to their wider crosslinguistic feasibility
figure NUM relationship between precision and the kappa coefficient for the three text types
table i provides some statistics on the corpus from which extraction tests are constructed
experiments will show that the changes in representation engender a NUM NUM increase in accuracy raising the performance of the cbl algorithm from NUM NUM correct to NUM NUM
in general the restricted memory bias with random feature selection degrades the ability of the system to predict relative pronoun antecedents although none of the changes is statistically significant
in addition the linguistic bias approach to feature set selection relies on the following general procedure when incorporating more than one linguistic bias into the baseline representation NUM
NUM finally incorporate biases that discard features e.g. restricted memory bias but give preference to those features assigned the highest weights in step NUM
the first is a direct modification to the attributes that comprise the case representation and the second modifies the weights to indicate a constituent s distance from the relative pronoun
in the paragraphs below we describe these biases and show how they can be used to modify the case representation for the task of relative pronoun rp disambiguation
given as input a baseline case representation the method modifies the representation in response to a number of predefined linguistic biases by adding deleting and weighting features appropriately
it is generally acknowledged in descriptive linguistics that the kind of tone attributed to an information unit encodes a basic semantic speech act or speech function NUM NUM such as command question statement and offer even though this relation is not one to one
in information seeking dialogues that use spoken language for interaction intonation is often the only means to distinguish between different dialogue acts thus making the selection of the appropriate intonation crucial to the success of the information seeking process see e.g. NUM for english
for instance a dialogue move request depending on the context in which it occurs may be realized intonationally by using tone NUM tone NUM or tone NUM hence the selection of an appropriate tone is conditioned by factors other than just individual cor dialogue moves
they signal three different kinds of relation between grammar and intonation and thus indirectly con ext and hence realize different meanings a choice from the available alternatives has to be made for each of the phonological categories in order to realize sentence intonation
task independent computational dialogue modelling see e.g. NUM l0 t NUM seldom makes contact with natural language generation exceptions being e.g. NUM NUM NUM and much less so with speech generation synthesis
however the function of intonation is still restricted to what is called grammatical function more specifically the textual function of intonation without considering aspects like communicative goals and speaker s attitude i.e. the interpersonal function of intonation NUM
more important is the conditioning of the secondary tone by attitudinal options such as the speaker s atti tin this paper the following notational conventions hold marks tone group boundaries capital letters are used to mark the tonic element of a tone group
the komet penman grammar can generate two types of output a plain text which can be embedded into for instance a dialogue box in a graphical user interface and a text that is marked up with intonational features see section NUM NUM for an example which is passed on to the multivox text to speech system NUM and presented acoustically to the user
keeping in mind that we want our system to be user friendly we do not want it to realize this request as a yes no question do you want to go to heidelberg or a statement you want to travel to heidelberg since it would then exhaustively have to search through its knowledge base in order to find the right destination to include in its utterance
the loglinear model with nine features further improves this score
is the length of the word three characters or less
the model wa s constructed in the following way
next the lexical association method was evaluated on this pattern
it can be used to address the problem of sparse data
the remaining NUM NUM wsj pp cases formed the evaluation pool
summary the japanese systems showed excellent overall results despite a very compressed development cycle
determining when to segment and not segment the place sub component was a complicating factor in producing the proper tag type
each of us manually tagged the same NUM articles then looked at the resulting annotator variation
that review neither constitutes cia authentication of information nor implies cia endorsement of the author s views
the inability of systems to handle this complex contextual shift lowered the group f measure average for location
the final product formed ground truth or the keys against which the automatic participant systems were scored
evaluation of named entity identification algorithms in the tipster sponsored multilingual entity task met program
the participant japanese systems were developed in a fourmonth period of time and output results comparable to
enamex NUM org NUM person NUM loc NUM timex NUM date NUM time NUM numex NUM percent NUM money NUM
in this case there were two miti parent and telecommunications subcommittee child
there were frequent exchanges of information among the government and contractors with heavy use of email and by the end of the two years a sizable catalog of shareable resources had been developed
from this it was determined that an initial activity of phase ii would be the development of a common open software architecture for the implementation of text processing systems
notwithstanding prior arpa and commercial support for the development of information extraction technology and muc s positive impact information extraction had been applied to the database update task as largely a manual procedure
the workshop included parallel working sessions for discussion of specific issues in the different areas of research including details for addressing the different domains and different languages and the government s preparation of the data
this architecture was to facilitate sharing of the development tasks transferring technology to actual applications future r d into improved algorithms and continuous upgrading of systems which use the architecture
for each project a demonstration system based on the architecture and modules developed in the r d tier was developed installed and evaluated in the processing of actual operational data
document detection includes both routing which involves running static queries against a stream of new data and retrieval which involves running ad hoc queries against archival data
although a few automated information extraction systems had been deployed they tended to be expensive both in terms of development and ongoing maintenance and were task specific with little reusability
comparing naive and expert coding as kid do can be a useful exercise but rather than assessing the naive coders accuracy it in fact measures how well the instructions convey what these researchers think they do
in addition note that since false positives and missed negatives are rolled together in the denominator of the figure measure NUM does not really distinguish expert and naive coder roles as much as it might
here we use siegel and castellan s k because they explain their statistic more clearly but the value of c is so closely related especially under the usual expectations for reliability studies that krippendorff s statements about c hold and we conflate the two under the more general name kappa
however since one coder is explicitly designated as an expert it does not treat the problem as a two category distinction but looks only at cases where either coder marked a boundary as present
currently computational linguists and cognitive scientists working in the area of discourse and dialogue argue that their subjective judgments are reliable using several different statistics none of which are easily interpretable or comparable to each other
and if both coders were to use one of two categories but use one of the categories NUM of the time we would expect them to agree NUM NUM of the time NUM NUM NUM
we have shown that existing measures of reliability in discourse and dialogue work are difficult to interpret and we have suggested a replacement measure the kappa statistic which has a number of advantages over these measures
to give different random halves interpret result by comparing values for different corpora
this arg ment comes i from all quarters the second comes mainly from linguists
a measure of corpus similarity would be very useful for lexicography and language engineering
in general where a word is more common there is more evidence
we actually used all of the selected keywor ls as explained in the last section for our ca he model
itere df w is the number of do uments in whi h the wor l apl ears
one of the methods for increasing the t ossibility if improvement is to make n of n best larger thus including more
the absolute improvement using the sublanguage component over sri s system is NUM NUM from NUM NUM to NUM NUM as shown in table NUM
fhe top NUM n t st hypotheses ae ording to sih s score are r s or l
there are several parameters in these pro eesses and the values of the parameters we usetl for this experiment will be summarized at the end of each section below
NUM of north american business news which consists of dow jones information services new york times reuters north american business report los angeles times and washington post
ae or ling to the table the number of mne decreases rapidly for n up to NUM however after that point the number decreases only slightly
next to find strongly tot ie related words we extracl ed words which apl ear in at least NUM lilt if the NUM sublmlguage articles
to address this we turned to machine learning to automatically develop algorithms from large numbers of both training examples and features
ideational structure an ideational specification is a structure of entities processes t hings and qualities and the relations between these entities
using summed deviations as a summary metric ea s improvement is about NUM NUM of the distance between np and human performance
schabes and waters tree insertion grammar the foot if any is a lexical item
the path from the root of an auxiliary tree to the foot is called the spine
the algorithm is a general recognizer for tigs which requires no condition on the grammar
since this intersection is not a regular language l can not be a regular language
taking advantage of this sharing can counteract the exponential growth in the number of rules completely
then we assessed reliability by comparing the boundaries produced by partitions a and b on the four narratives using a boundary threshold of at least three subjects
ambiguity is lost in this transformation because both auxiliary trees turn into the same rule
this results in the creation of a right anchored ltig that uses only left auxiliary trees
also like the gnf procedure ambiguity can be reduced and the trees derived are changed
figure NUM shows typical recall precision curves
table NUM overlap of submitted results
in trec NUM the track was formalized
only one field was used i.e.
table NUM comparison of performance average precision
figure NUM shows the comparison of results between
since with lace there was no graphical command interface to mirror in natural language we opted instead to focus on database query also included in eucalyptus and the issuing of verbal onroad route instructions to a simulated tank unit
the reason is that the former two corpora are really in a homogeneous domain while the corpus of scientific journal is a complex of distinct scientific fields
japanese and english texts are analyzed morphologically and all content words nouns verbs adjectives and adverbs are identified
the purpose of this library is to allow construction of templates using pre defined objects
we also believe that the programs iml lemented and documented it this work provide the nucleus of a larger library of rensab e programs for computational semantics
the approach also gets rid of the need to create large data structures which include information which would be relevant for one choice of parameters but not the current choice
even the parsing stage can not be totally independent unless we generalise to the worst case the situation semantics fragment requires an utterance node as well as a sentence node
the user controls the semantic construction process by moving to particular nodes in the derivation tree and performing operations by using mouse double clicks or by selecting froln a pop up menu
the ct ears tool computational linguistics education and research tool in semantics was developed as part of the fracas project NUM which aimed to encourage convergence between different semantic formalisms
although formalisms such as intensional logic dr l and situation semantics look different on first sight they share many common assumptions and provide similar treatmeats of many phenomena
the clears tool allows exploration and comparison of these different formalisms enabling the user to get an idea of the range of possibilities of semantic construction
the first part of the paper shows the potential of the system for investigating the properties of different seinantic formmisms and for teaching students formal semantics
the glenn also allows a user to choose whether or not to perform quantifier storage or discharge and thereby pick a particnlar reading for a sentence
single lines the syntax semantics lnt erface of tile quantifier storage routine with the parameters picking up the correct piece of code at run time
cing the proposition as a role of the speech act rather than visa versa wag allows cleaner integration into systems intended for dialogic interaction
the idea is that the candidates m the beginning or at the end of sentences have larger probabilities to be personal names than they are in other places
thus tlii should have higher score than lcb l and the variation of a character should be considered in the formula
the left boundary of the organization is determined by the following rule we insert a single character to the name part until a word is met
in the experiment of the gender assignment NUM NUM of chinese personal name corpus is regarded as training data and the renmining l NUM is for testing
this approach also provides a means of handling certain eases of categorial stfift
the log may be turned on or off or particular levels of reporting may be selected e.g.
the similarity measure is used as the basis of their structural matching of parallel sentences so as to extract structural translation patterns
this formula is a modification of the dice coefficient weighting their similarity measure by logarithm of the pair s co occurrence frequency
enhancements can only be envisioned and designed when the base architecture is well defined
this grammar compiles into a dcg like grammar of approximately NUM rules
jackendoff NUM by chomskyadjunction of adjuncts to maximal projections
a probabilistic lr parser was trained with the integrated grammar by exploiting the susanne treebank bracketing
the appendix gives an indication of the diversity of the sentences in our corpus
NUM sentences now failed to receive a parse a decrease in coverage of NUM
the text grammar has been tested on the susanne corpus and covers NUM NUM of sentences
the requirement for a domain independent analyser favors statistical second author was visiting rank xerox grenoble
since the syntactic coarse rules only weed out the erroneous pos word chains some errors still remain
figure NUM effect of proportion of unknown words on overall tagging error rate
in a picturesque way we can say that discarded esl s are damned the hell is the right place while survived esl s are waiting for next judgment the limbo is the right place for this wait state at the end of the algorithm if there is a single winner esl it will gain the paradise
for example to the computation of the mcpi of esl reddito di persona contribute esl s like esl reddito di pro f essionista esl reddito di azienda where professionista persona and azienda are instances of human entitiy
strings su richiesta del ministro per le finanze il servizio di vigilanza sulle aziende di credito service of control of agencies of credit controlla l esattezza delle attestazioni
the posterior probability see algorithms in table NUM and NUM improves over the blind prior probability as much as it increases the confidence of correct eslls and decreases the confidence of wrong esl s
since it can act a s a snmothing device used to obtain cell estimates for every cell in a sparse array even if the observed count is zero bishop fienberg and holland
we also thank students of tsukuba university bunkyo university and nihon kogyo university for having spared the time to take the svmmarization tests
2these features use tile s unc mutual informationba ed measure of lcxic d a sso iation a s tim prc vious loglinear model for two possibh attachment sites which wcrc estimated from all nomin m azt l vcrhal pp att t hments in the corpus
in this section we will compare two models for pre e dicting the part of speech of an unknown word a simple model that treats the various explanatory variables ms independent and a model using loglinear smoothing of a contingency table that takes into account the interactions between the explanatory variables
on the evaluation samples a median of NUM of the pp cases were attached to the noun
association it seems natural that this pattern calls for a combination of a structural feature with lexical association strength
we also replaced the maximum likelihood bigram component with a series of NUM two level smoothed bigram models from a NUM plus15 smoothed bigram to a NUM plus NUM smoothed bigram
this simultaneously surreal and metaphysical sentence may be accepted by grammar systems that detect well formedness but it is subsequently considered just as plausible as the original sentence
an important difference between the two is in the bigrams boy seat and boys eat neither of which occurred in the training corpus
one limitation of hughes evaluation system is that fractured class distributions are not penalized if some subbranch of the classification contains nothing but number performance comparison
we can illustrate the danger of conflating the two sentence contexts by considering the nonsentences NUM b the boys eat the sandwiches is delicious
perplexity is related to entropy so our goal is to find models that estimate a low perplexity for some unseen representative sample of the language being modeled
other experiments with depending on the class at a certain depth of the previous word lead to smaller improvements and are not reported here
many thanks to mike collins and professor mitch marcus from the university of pennsylvania for their helpful comments on this work
as a result experimenters need only worry about what features to use and not how to use them
must be tested for each step of merging
these can be calculated in the following way
they are linguistically motivated and usually called parts of speech
correspond to lower probabilities and vice versa
figure NUM also shows the perplexity s slope
constraints same output until NUM NUM none after
merging starts with an initial very general model
table NUM number of states and log perplexity for
the models compute the probabilities of actions based on certain syntactic characteristics or features of the current context
the backed off contextual predicates should allow the model to provide reliable probability estimates when the words in the history are rare
compared with the split hindle sz rooth method the samples are a little less spread out and there is no overlap at all between the central NUM of the samples from the two methods
the model implemented as above showed some disadvantages it did not have enough detail
if four output candidates are allowed then this rate reaches NUM to NUM
this is because there is usually a one to one correspondence between phonemes and graphemes in these languages
phoneme based speech recognition systems incorporate a phoneme to grapheme conversion ptgc module to produce orthographically correct output
grapheme to phoneme conversion gtpc has been achieved in most european languagesby dictionary look up or using rules
by best we mean as usual those sequences having the highest probability
then we inductively compute the locally minimum distance NUM and the path as follows initialization
instead of rules or a dictionary the statistics of language connecting pronunciation to spelling are exploited
as described in section NUM NUM the available material for training were 300k word corpora for all languages
figure NUM shows that the ex null between advp and prt and ignore all punctuation
for example temporal pps such as in NUM where the prepositional object is tagged cd cardinal favor attachment to the vp because tile vp is more likely to have a temporal dimension
my own participation in this aspect of the session will be the presentation of the work we are doing in montreal on the building of a semantic tagset for a french dictionary of collocations
gm is a car manufacturer moreover in each of these patterns we would need to allow the occurrence of temporal locative and other adverbials
the metarule for the basic active clause as in the company resumed talks is into this rule and a new specific rule is generated
i believe we should postulate that nobody has got it and that we precisely need to meet and discuss the problem of semantic tagset construction in order to get closer to the most suitable solution
we assume that the user somehow provides a mapping from text strings to template entries and that the semantics of the rule is completely specified by such a mapping
symmetric verbs are verbs where an argument linked to the head with the preposition with can be moved into a subject position conjoined with the subject
these are two consistent management succession event descriptions so they are merged and in the course of doing so we resolve he to garrick
in the current version we determine what phase n NUM rule matches the entire string and then construct as general as possible a specialization of that rule
they also have a semantic part which specifies how attributes are to be set in the output objects of the phase
yet all of these variations are predictable and every time we want the first pattern we want the others as well
the first is simply to examine a large number of highly ranked false positives for a number of queries and to determine whether information extraction techniques can help
rather arbitrarily we have set the search window to ten sentences this is a parameter than can be experimented with
the concept of semantic tagset will be taken as referring to systems of core semantic units that give structure to the lexicons of natural languages
sentence length we would expect it to run in linear observed time with respect to sentence length
what would be your solution if any for dealing with all the potential problems within one single coherent system of semantic tagging
o what additional issues arise in evaluating more complex semantic tagging going beyond sense disambiguation as traditionally defined
the proportion p a of the times that the raters agree is given as the average of si across all objects def
as will be discussed a core functions to manifest the purpose of the segment while the embedded segments serve to help achieve that purpose
building on this similarity we sketch a partial mapping between the two theories to show that the main points of the two theories are equivalent
thus the application of rst schemas in the analysis of a text is recursive i.e. one schema application may be embedded in another
the range of possible rst text structures is defined by a set of schemas which describe the structural arrangement of spans or text constituents
because constraints may be needed in order to make progress on these issues we point out two approaches to constraining the definition of informational structure
a less common schema pattern known as the joint schema contains multiple spans with no nucleus satellite distinction among them joined into a single span
as shown in figure NUM the incompatibility arises because the nucleus and satellite of the intentional relation may be inverted in the rst informational relation
NUM in section NUM we argued that nuclearity in an rst analysis is an implicit claim about either relatum may be the nucleus when an instance of a domain relation is used
as we argue below this functional distinction between nucleus and satellite is an implicit claim about ils and is a crucial notion in understanding the correspondence between rst and g s
this will cause an erroneous pattern of word chain which increases a lot of unnecessary job to the parser
therefore this analysis involves re formation of the valency structure
a set of special non terminals is added one for each partial right hand side
thus for every stsg derivation there would be an isomorphic pcfg derivation with equal probability
the one i am concentrating on is the cross sententim anaphoric relation
unfortunately it was not possible to get a full specification of the algorithm
on ideally we would exactly reproduce these experiments using bod s algorithm
the k NUM NUM NUM text slices are displayed on the horizontal axis the progressive difference scores d k are shown on the vertical axis
in comparison an adult s dictiouary is more of a ref erence tool which assumes knowledge of a large basic vocabulary while a learner s dictionary assumes at limited vocabulary but still some very sophisticated concepts
it is instead the verb lifting that provides the best sentence internal indication of the weight sense of light in the example under consideration
however subcorpora in which most sentences exhibit phrasal substitution of antonyms are clearly not representative samples of the use of the target adjectives
for some of the indicators however generalization properly takes another course leading not to semantic but to syntactic cues for sense identification
it is often deleted and in such cases an anaphoric pronoun may replace the non anaphoric it resolving to a preceding or following clause
some role relationship nouns are used overwhelmingly in their role senses as with friend or in their personal senses as with doctor
in these cases disambiguation involves words other than the noun that the target adjective modifies standing in other syntactic relations to the target
it need not be assumed that all instances of the target word t are to be included in assessing the relative probabilities of different senses
we exclude all freezes from consideration as not being legitimate instances in which the adjectives actually have a definable sense see footnote NUM
it also indicates not new senses of old but in this case concrete is simply a special case of animate
we do not propose in general to extract automatically either semantic generalizations like those discussed above or the rules that use them
using these distributions we calculated x coefficients for each pair of labelers in each condition for the remaining eight tasks in our corpus
typically values of NUM or higher for this measure provide evidence of good reliability while values of NUM or greater indicate high reliability
comparisons of inter labeler reliability that is the reproducibility of a coding scheme given multiple labelers provide another perspective on the segmentation data
corpus comprises elicited monologues produced by multiple non professional speakers who were given written instructions to perform a series of nine increasingly complex direction giving tasks
previous research has found the second author was partially supported by nsf grants no iri NUM NUM no iri NUM NUM and no
the prosodic transcription a more abstract representation of the intonational prominences phrasing and melodic contours was obtained by hand labeling
results for each condition together with the lowest and highest average scores over the tasks are presented in table NUM
instead we find that the number of consensus sconts was significantly higher for text and speech labelings than for text alone p NUM
group t when the read and spontaneous data are pooled group s agreed upon significantly more sbeg boundaries p NUM
let cj be the total number of times that objects are assigned to the jth category n i.e. cj n j
tim low mibiguil y is pari ly tl ixibuted NUM o the llotlli dictionary that has 11o iitilleeessa ry entries not found at the documents
the proposed method gives better potential of sustaining the precision while improving the recall than other approaches by making use of probabilistic distributions of terms as the representation of meaning of the terms
one way to deal with the probleni is to nse tim i robability of each function word and choose the one with the highest vahie
one approach adopts a full scale morphological analysis to decompose a word into a sequence of the smallest morpheme units that are all treated as index terms
o tern disl ributions over th s tii e oelllil ltl sel
experiments on NUM documents show that our evaluation scheme gave results closet to the human intuition and maintained the highest precision ratio of tile existing methods
d h tl we select he re is oit decolllpositioll wit h th low est diverg nce
a word hypothesis w is a quadruple from to key score with j rcb vm and to being the start and end frames of w
for a t air of active and passive edges a l if a next i cat and lfivm a to insert edge
as yet the concept of architecture in vm has been used mostly to describe the overall modularization and the interfaces implied by the data flow between modules
a prosody hypothesis consists of a beginning and ending time and model probabilities for the boundary types which sum up to one
bigram c use it will be the transition from the last word covered by a to the tirst word covered by b
words to the right which could not be integrated into a parse were counted as deletions although they might have been correct in standard word accuracy terms
highly parameterizable and with control subtly spread over many interacting submodules understanding and then integrating such systems into a common control strategy can be a very daunting task
but we are swill making the simplifying assumptions that tile algorithm can be used increnlentally but there are algorithms m suitable for incremental processing e.g.
early experiments with a fully distributed chart showed that the effort required to keep the partial charts consistent was much larger that the potential gains of increased parallelism
such an cxperim ml similar NUM o seleclivo samplin q
but in this section we cornpare our tagger with hidden markov model based taggers
rather it merely requires a spanish lexicon and morphological analyzer that can tag words with all their possible parts of speech
the system requires few if any hand tagged texts to bootstrap itself
after one man month of tuning biases the accuracy of the french tagger increased to NUM NUM
we extcude l ih ill s algol itlnu in scwwal ways
thus in a sense his unsupervised learning experiments did take advantage of a large pos tagged corpns
there are clearly eight orthographic words in the example given but if one were doing syntactic analysis one would probably want to consider i m to consist of two syntactic words namely i and am
interestingly m overestimates the probability mass of unseen types
for vu k f NUM NUM NUM NUM p
a list of symbols is provided at the end of the paper
clearly key words are not uniformly distributed in alice in wonderland
but is this a desirable property for a measure of lexical specialization
the header of the electronic version of moby dick requires mention of e f
not surprisingly this affects the growth curve of heid itself
second how does cohesive word usage affect the good turing frequency estimates
the preprocess includes specialized phrase taggers
he will be succeeded by mr donner NUM
tagger throughput is around NUM words sec
the document profile d i is represented as a vector of numeric weights de wil wi2 wik wit where wik is the weight given the kth word shape token in the ith document and t is the number of distinct word shape tokens of the ith document
we implemented a content oriented categorization system to evaluate the word shape token based approach in comparison with the ocr based approach
because we have only to recognize simple graphical features from image this process is much faster than ocr
a word shape token is a sequence of one o1 more character shape codes which represents a word
however when used for mapping between word shape tokens and original words the ambiguity is much reduced
while most information retrieval systems have been designed for text files there are some systems proposed for images
n l there are many different languages in common t generation photo copy we transformed the document images into word shape tokens and ascii encoded words where we randomly took NUM inlages for each category NUM in total as training data to generate category profiles and tested the remaining NUM images NUM in total
these biases are so fundamental to generative phonology that they are left implicit in many theories
we presented pairs of underlying and surface forms to ostia and examined the resulting transducers
each time a transition is made exactly one symbol of the input string is consumed
labels on arcs are of the form input symbol output symbol
the next n symbols of the transduction s output are now marked as having been used
from the sequence of edit operations an alignment between input and output segments is calculated
then only n NUM merges must be tested to find the best merge
this work was supported by the graduiertenkolleg kognitionswissenschaft saarbriicken
then aligned corpora are statistically analized for finding the corresponding collocation patterns in the target language french
this model is also referred to as the trivial model
this table indicates that translation pairs of lengthy or unbalanced sequences are safely regarded some of typical word sequence pairs
NUM NUM NUM configuration control board ccb
NUM NUM configuration management in a tipster application lifecycle
in this area configuration management has many responsibilities
the above members form the core of the ccb
this document will also explain the discrepancy or deviations
figure a NUM tipster compliance based on modules
the terms and abbreviations listed below are used within this cm plan
tracking of rfcs rfcs will be formally tracked by their rfc number
its performance was then always at or above baseline
as yet even the most up to date advanced systems have not achieved the analysis in the deep structure therefore sentence structure presumption in the surface structure is essentim for a robust prosodic control system
in contrast when a subordinate clause does not have modality explicitly and modifies a clause with modality the readers interpret the subordinate clause as that with a kind of modality such as conjecture
there can be little doubt that ldg will be more etfective when two or nmre conjunct equivalents of different levels appear in one sentence since the ldg conjunction levels are closely related to the inter clausal dependency
these groups are difficult to allocate to a single level as dmy are used in expressing many factors such its parataxis cause means attendant circumstances and because they vary semantically and syntactically
based on this assumption ldg presumes the sentence structure before syntactic and semantic analyses on the ba sis of previously collected lexical information that characterizes the lexical discourse
consequently nagara NUM NUM is ranked at a lower h vel than nodr o NUM
another critical feature is that the japanese language is an almost pure head final language i.e. predicates and function words to signify the sentence structure appear at the end of the clause or sentence
thus all the necessary information is collected from the corpus database at this preprocessing phase with only one pass over the corpus file
n2 pform nil n2 coll h2 de une foule de filles a crowd of girls c n2 pform nil a2 qte h2 de trois filles three girls d
the tuple valued selectors feature needs to be added to these entries with the value already illustrated this can be done automatically given the declaration
we arrange things so that this value is threaded throughout the vp and returns as the value of the in part if no agent phrase is present
if it does not then it will be possible for the bitstring representing the lower bounds of the two types to be distinct from any row
this will only be a sensible thing to do if we know that the value of the cat feature will always be instantiated when types are checked
it is however possible to get a flatter tree structure more directly and also to overcome the problem with features used for semantic composition
this will arise because the selector on the np will unify the chosen variable with the position on the tuple identified by its shared variable x
this appear to be true not only for our own system but also for other systems we asked other groups participating in trec NUM to run search using our expanded queries and they reported nearly identical improvements
text can both added and deleted but care is taken to assure that the final query has the same format as the original and that all expressions added are well formed english strings though not necessarily well formed sentences
NUM phrase extraction we use various shallow text processing techniques such as part of speech tagging phrase boundary detection and word co occurrence metrics to identify stable strings of words such as joint venture
null the pairs stream is derived through a sequence of processing steps that include part of speech tagging lexicon based word normalization extended stemming null syntactic analysis with the qq p parser cf
table t the result of manum analysis about
the proposed alignment algorithm is based on dynamic programming
figure NUM an example of typical korean english alignment
as the product of the following thre c
english align merit at phrase level
the information in table NUM is the unique product of phrase level alignment
each time a state is entered except the start and end state one of the outputs is chosen again according to their probabilities and emitted
the parameters should be optimal in the sense that the resulting models assign high probabilities to seen training data as well as new data that arises in an application
the lower the perplexity and log perplexity of a test sequence the higher its probability and thus the better it is predicted by the model
this number is reduced to NUM NUM when using the unigram constraint thus by a factor of v
the derived models are not in any case equivalent with respect to perplexity regardless whether we start with the trivial model or the bigram model
we use two additional strategies to reduce the time complexity of the algorithm a series of cascaded constraints on the merges and the variation of the starting point
the model merging algorithm needs several optimizations to be applicable to large natural language corpora otherwise the amount of time needed for deriving the models is too large
note that the system correctly tokenized fbj e although it is not registered in the dictionary
in order to save memory we used a type of character bigram model that considers un we made two spelling models
in this paper we compare three segmentation models part of speech trigram word unigram and word bigram
finally we extract new words by filtering out spurious word hypotheses whose expected word frequencies are lower than the threshold
we then extract new words by filtering out erroneous word hypotheses whose expected word frequencies are lower than the predefined threshold
vania university as an unknown word because NUM a
thus summing over all derivations of a tree in the stsg yields the same probability as summing over all the isomorphic derivations in the pcfg
the dop model itself is extremely simple and can be described as follows for every sentence in a parsed training corpus extract every subtree
reduction of dop to pcfg unfortunately bod s reduction to a stsg is extremely expensive even when throwing away NUM of the grammar
we say that a pcfg derivation is isomorphic to a stsg derivation if there is a corresponding pcfg subderivation for every step in the stsg derivation
in order to perform our analysis we must determine certain details of bod s parser which affect the probability of having most sentences correctly parsable
previous algorithms are expensive due to two factors the exponential number of rules that must be generated and the use of a monte carlo parsing algorithm
in other words rather than using the large explicit stsg we can use this small pcfg that generates isomorphic derivations with identical probabilities
in the dop model a sentence can not be given an exactly correct parse unless all productions in the correct parse occur in the training set
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
as shown in figure NUM we developed a baseline segmentation algorithm based on a simplification of these results using the value of the single cue phrase feature cue1
on average subjects assigned boundaries at only NUM NUM of the potential boundary sites min NUM NUM max NUM NUM in any one narrative
the large regions of white space separated by very wide bars shows a striking consensus on certain segments white space and segment boundaries wide black bars
cue prosody which encodes a combination of prosodic and cue word features was motivated by an analysis of errors on our training data as described in section NUM NUM NUM
a comparison of results on two sets of boundaries those identified by at least three versus those identified by at least four subjects shows roughly comparable performance
pause is assigned true if pi l begins with x convention NUM or with w y for convention NUM false otherwise
it is also a simple task to list all new words unknown words namely the words in a given text that are not found in the system dictionary
conclusion we present a new word extraction method for japanese based on expected word frequency which is computed by using a statistical language model and an n best word segmentation algorithm
in order to decide the best configuration of the underlying japanese word segmenter we compared three segmentatio n models part of speech trigram word unigram and word bigram
this effort will be extended to sentence level collections such as phonetically balanced sentences speech dialogues and scenarios
we stepwise our data collection into three phases to scan with NUM dpi resolution one thousand sets of NUM high frequency syllables in the first year then of NUM syllables and NUM NUM syllables in the following years a at each phase we develop both the square hand haracters and free style characters
they do n t provide pools 3it is possible to compose up to NUM NUM syllables out of ea h korean alphabet but korea standard code ksc NUM prescribes NUM NUM complete codes for korean syllables
it also incorporates academic and research institutes and hidustries into comnion goals the etticient and imrmonious lriw toward research and development and establishment of long range policies and strategies for korean la ngu tge engineering
lexicon for morphological analysis the lexicon for korean morphological analysis is currently being built to have NUM NUM entries with oil line management tools and will grow to NUM NUM entries with on line tools after two more years
therefore we will obtain complete knowledge about the performance of the parser by the comparison of it on these two types of sentences
therefore the most likely parse tree under this score model is then this kind of matched constituent with the maximum probability score i.e.
the bottom panels of figure NUM show the corresponding plots of the progressive difference scores for the complete vocabulary d k
figure NUM gives an example of generated word shape token representation with its original document image
at least for a time lag of NUM this finds some support in the autocorrelation functions shown in the second line of panels of figure NUM
for moby dick however the chi squared statistic suggests a significant difference between the observed and expected vocabulary sizes x NUM NUM NUM p
given k different text sizes for which the observed and expected vocabulary sizes are known p can be estimated by minimizing the mean squared error mse
nevertheless in absolute terms the expectation may be several hundreds of types too high and may run up to NUM of the total vocabulary size
these z scores can then be used to ascertain which words are significantly underdispersed in that they occur in significantly too few text slices given the urn model cf
this is illustrated in figure NUM
this is illustrated in figure NUM
the proof proceeds in four steps
NUM NUM an earley style cubic time parser for tig
consider the tig in figure NUM
the subtree traversal rules control the recognition of subtrees
for example consider the trees in figure NUM
there are two reasons for this
each is a right auxiliary tree
two special codelet types namely breaker and answer will be explained in section NUM here we make a distinction between codelets and codelet type
we can not see both the goblet and the faces at the same time but we are able to switch back and forth between these two interpretations
for example at cycle NUM the activated word node causes the proportion of word codelets to increase to NUM
several techniques have been used in word identification ranging from simple pattern matching to statistical approaches to rule based methods
since the component characters of the second word can be free the breaker codelet concludes that this is an erroneous grouping
there are three types of objects that may exist in the workspace character objects word objects and chunk objects
since the system s behavior is more random at high temperatures it is able to explore diverse paths in the initial stage when little structure has been built
the sentence containing this fragment allows only one way of segmenting the word boundary which is shown in NUM
the mutual information score is derived from the ratio of the co occurrence frequency of two characters to the frequency of each character
how do we map word senses semantic tags from multiple knowledge sources into a single set in the static knowledge acquisition scenario
subject construction type i and type NUM cases can be analyzed using the processing flow described in section NUM because adverbial particle wa simply acts as a case marking particle
although several semantic relations are known at the present time alt j e can resolve only two of them based on semantic categories has a relation and is a relation
because this method cau correctly analyze the double subject construction the method helps the translation of this constrnction into an appropriate english construction
ttowevcr m my japanese adjeclives and some verbs can dominate two surface subjective cases within a simple sentence
if the lexicalization operation is to apply simultaneously the same anchor projects two elementary trees from the lexicon
the focused dataset is created by the domain expert either through a submission of an on line search or through a compilation of documents from a specific source
our goal in developing the annotation instructions was that they can be used reliably after a reasonable amount of training by taggers who are non experts but who have good language skills and the ability to pay close attention to detail
he sem h shol ld find other tokens of this and mso tokens of the l lural l orih llvres
he exact structure of the dictionary source files is confidential but it is well structured and allows uncomplicated t ccess
high frequency verbs include some very common word forms such as the auxiliaries hebben have zullen will kunnen can and moeten must
ceteris paribus plural nouns are less frequent than singular nouns on the other hand en for verbs serves both the function of marking plurality and of marking the infinitive
note however that the application of this overall mle presupposes that the relative frequencies of the various functions of a particular form are independent of the frequency of the form itself
these two opposing forces conspire to yield a downward trend in the percentage of verbs as we proceed from the high to the low frequency ranges
for low frequency forms such as aanlokken or bedraden one might consider basing the mle on the aggregate counts of all ambiguous forms in the corpus
when one considers forms that do not occur in the training corpus e.g. bedraden to wire the situation is even worse
the case of noun plurals is somewhat different from the preceding two cases since it is not strictly speaking a case of morphological syncretism
this can be explained by the observation that a good many of the underived nouns in er are high frequency words such as moeder mother and vader father
however in the finite forms in main clauses the particle must be separated for example wij zeggen onze afspraak af we are canceling our appointment
we applied the modified algorithm with variables in the output strings to the problem of the german rule that devoices word final stops
toward this objective the architecture should support more automation with interactive assistance to the user and developer in preparing these items
while chart parsing and calculations of j3 can be done in o n NUM time we have been unable to find an algorithm to compute the o l terms faster than o ns
the p n term is estimated from our pcfg as the sum of the counts for all rules having n i as their left hand side divided by the sum of the counts for all rules
we restricted sentence length to a maximum of NUM words in order to keep the number of edges in the exhaustive parse to a practical size however since the percentage of edges needed by the best first parse decreases with increasing sentence length we assume that the ira null provement would be even more dramatic for sentences longer than NUM words
ideally we would like to use as our figure of merit the conditional probability of that constituent given the entire sentence in order to choose a constituent that not only appears likely in isolation but maximizes the likelihood of the sentence as a whole that is we would like to pick the constituent that maximizes the following quantity
we calculate this quantity as follows k o i i l n k j3 n k we are again taking the geometric mean to avoid thrashing by compensating for the aj3 quantity s preference for shorter constituents as explained in the previous section
for each edge e in gj k we compute the the product of al of the nonterminal appearing on the left hand side lhs of the rule the probability of the rule itself and NUM of each nonterminal n s appearing to the left of nj a in the rule
recomputing the o l terms when a constituent is removed from the keylist can be done in o n NUM time and since there are o n NUM possible constituents the total time needed to compute the ol l terms in this manner is o n5
the p tj kltj NUM j term is just the probability of the tag sequence tj tk NUM according to a trigram model NUM technically this is not a trigram model but a tritag model since we are considering sequences of tags not words
outside probability a of a constituent nj k is defined as the probability of that constituent and the rest of the words in the sentence or rest of the tags in the tag sequence in our case
it is also interesting to note that while the models using figures of merit normalized by the geometric mean performed similarly to the other models on shorter sentences the superior performance of the other models becomes more pronounced as sentence length increases
in lhc slandards the double subject eonslruclion is listed as one of the conslruclions lhat arc difficult lo process successfidly
the predicate nagai long has only one valency element n1 with ga
the architecture shall provide a design that can serve as an efficient research framework
sample responses are illustrated in NUM
the lexical gap error characterizes cases in which a response could not be classified because it was missing a concept tag and therefore did not match a rule in the grammar
a sample of the lexicon is illustrated below
NUM NUM the scoring lexicon for the police item
collapse phrasal nodes xp cops police xp better better trained train xp self defense safety d
NUM sample concept grammar rules for types of self defense safety a xp police xp better train xp safety
due to the large volume of tests administered yearly by educational testing service ets hand scoring of these tests with these types of items is costly and time consuming for practical testing programs
holland points out however that lcss can not represent domain knowledge nor can they handle the interpretation of negation and quantification all of which are necessary in our scoring systems
the results of this run were the following
table NUM results of automatic scoring of responses
with macros generahzations about patterns that can occur for a whole class of mwls can be expressed
lexical variation one or more words can be substituted by other terms without changing the overall meaning of the mwl
however only a subset of 1part of this work was funded under lre NUM NUM by the eec
from house out where general rules would require an article between the preposition and the noun
however the use of these techniques is usually hampered by the unwieldiness in notation that these techniques usually lead to
when the user clicks on an unknown word in a foreign language locolex evaluates the context of the queried word
green wave may only vary in case but not in number or adjective comparison without loosing its idiomatic meaning
idarex allows to define various types of variables and to mix canonical and inflected word forms in the regular expressions
though part of the variability of mwls may follow from their semantic properties as argued in recent work e.g.
out of the blue or g um haaresbreite by a hair s breadth lit
therefore we may be left with a series of mass distributions defined over sets of coreference configurations that are in inherent conflict
the first approach we describe uses the pairwise probabilities as sources of evidence that inform the choice of model for the coreference sets
the variations and acronyms are algorithmlcally generated without reference to the text
there were approximately five missed aliases that involved corporations and their subsidiaries
in these cases the aliases were assigned to the wrong entity
these will be added to the set
scores reported here use the following abbrevi
the best match is considered the referent
headline waste management new name waste management lnc
likewise we view the evaluation as only a very preliminary first step
it forces an emphasis on encoding semantic and pragmatic features in the grammar
this work was supported in part by onr arpa grants n0004 NUM j NUM and n00014 NUM NUM NUM and nsf grant iri NUM
more details on the evaluation can be found in sikorski allen forthcoming
it specifically addresses the issue of robust interpretation of speech in the presence of recognition errors
the end result of parsing is a sequence of speech acts rather than a syntactic analysis
for example consider an utterance from the sample dialogue that was garbled okay now
natural dialogue involves a more spontaneous form of interaction that is much more difficult to interpret
the procedure was as follows the subject viewed an online tutorial lasting NUM NUM minutes
on the other hand it s important to recognize another module s expertise as well
however the time needed to develop plans was significantly lower when speech input was used
the subject was then allowed a few minutes to practice both speech and keyboard input
until now it has not been possible to carry out a several months evaluation of the expected therapeutic effects on the population presently a medical team just begins to use a prototype of the system with autistic like and psychotic children
users play by means of sentences on one hand they may compose orders to achieve a goal for example to move a piece of a puzzle or they may comment on the progress of the game
we describe here the erel system a therapeutic software devoted to the education rehabilitation and evaluation of language devoted to children suffering from language and then communication disorders and especially devoted to autistic like children
for more than ten years a large amount of research has been carried out in the field of communication rehabilitation for handicapped persons and technical aids known as communication aids have now been developed with some success
in the type of applications described here the object so designated must be identified by the system in order to act on it and the consequences of the action upon the object must be taken into account in the representation of this object in the context
concerning linguistic forms the system proposes for example graduated ways to designate objects at the simplest level an object is necessarily designated by its shape and its color the black circle and plurals are not allowed
in part NUM we mention the state of the art in the domain of communication aids for autistic children and we show why illico is relevant to the development of software devoted to the rehabilitation of persons suffering from language disorders
first of all we think that the two characteristics of illico described in part NUM are big assets for the development of a language rehabilitation software i in the guided mode the user is led step by step during the construction of each sentence
some organization names look like personal names e.g. j t
b cic2c3 and c1c2c4 are in the cache and both are correct
d c1c2c3 and c1c2 are in the cache and cic2 is correct
if some words around this punctuation are personal names the others are given bonus
if the former is larger than the latter then it is a masculine name
several single character words can also form an organization name e.g. jwt j ii
table NUM shows that our model is good except for section NUM and section NUM
b length some organization names are very long so it is hard to decide their length
teacch program widely used in usa written communication synthetic voices
this delineates the coverage of the dictionary
these are typical keywords concerning the non invasive approach to human brain analysis
all methods performed well on this text
the other is the grammatical and rhetorical difference of the two languages
these are determined by article boundaries section boundaries and paragraph boundaries
however to try to reduce the variation in annotation annotators were instructed to keep the repair as short as possible
in the second restart in example NUM it is not clear what is the rr for the rm and
the following shows an example dialog and table NUM shows the corresponding division into the four categories a i okay
our segmenthypothesizing algorithm NUM assumed that at any word we have two paths possible a transition to the next word
discourse markers have a wider distribution than explicit editing phrases but are unlike filled pauses in that they are lexical items e.g.
for conversational speech the most natural division would appear to be the turn when one speaker stops speaking and another starts
note that fillers such as uh in the above example are deleted as a separate process in cleaning the text
the test set was obtained from the switchboard lattices which served as the baseline for the NUM language modeling workshop at johns hopkins
in conversational speech it is possible to have incomplete sentences sentences across turns and complex sentences involving restarts and other dysfluencies
however there are fairly many exceptions documents written by captain john smith of plymouth plantation 1600s by benjamin franklin 1700s by americans writing in periods throughout the 1800s and 1900s documents written in australian british canadian and indian english and docnments featuring a
this training was affected in three ways a week of classroom training was followed by four months of daily email interaction between the treebankers and the creator of tile atr grammar and once this training period ended daily lancaster atr email interaction continued as well as constant consultation among the treebankers themselves
if the parse forest is unmanageably large the treebanker can partially bracket the sentence and again with the click of a button see the parse forest containing only those parses which are consistent with the partial bracketing i.e. which do not have any constituents which violate the constituent boundaries in the partial bracketing
initially a file consists of a header detailing the file name text title author etc and the text itself which may be in a variety of formats it may ontain html mark up and files vary in the way in which for example emphasis is represented
in this article we present the at r lancaster NUM reebauk of american english a new resource tbr natural language processing research which has been prepared by lancaster university uk s unit for computer research on the english language according to specifications provided by atr japan s statistical parsing group
but the major differences between this and earlier treebanks can easily be grasped via a corn current alfiliation renaissance technologies corp NUM east loop road suite NUM stony brook ny NUM usa consultant atr interpreting telecommunications laboratories NUM NUM NUM parison of the descriptions below with those of the sources just cited
this procedure ensures that differences in performance are not attributable to the random partitions chosen for the test set
as a result we require that all instances are described in terms of a normalized set of features
in addition we have presented an automated approach to feature set selection for case based learning of linguistic knowledge
in general the context features represent the state of the parser at the point of the ambiguity
to do this a small set of sentences is first selected randomly from an annotated training corpus
the second column shows the effect of incorporating the subject accessibility bias into the combined recency bias representation
the case retrieval algorithm is essentially a simple k nearest neighbors algorithm with minor modifications to handle symbolic features
the results presented are NUM fold cross validation averages using the same breakdown of training test set cases for each experiment
a number of studies in psycholinguistics have noted the special importance of the first item mentioned in a sentence
in particular we are currently applying the linguistic bias cbl approach to the problem of general pronoun resolution
the system extracts the common and different parts fl om the character strings of all translation examples
section NUM gives a general overview of empirical studies in discourse and describes an empirical research strategy that leads from empirical findings to general theories
figure NUM best trec NUM manual adhoc results
comparison of manual adhoc results for trec NUM and trec NUM
city inquery and cornell all did many experiments for trec NUM to first determine the correct length of a passage and then to find the appropriate use of passages in their ranking schemes
combining different retrieval techniques offers improvements over a single technique over NUM for the virginia tech group but the input techniques need to be more varied to get further improvements
there was no expansion in the cirri21 run
no topic expansion was done for this run
comparison of automatic adhoc results for trec NUM and trec NUM
as expected all groups had worse performance
therefore the trec NUM topics were made even shorter
two major issues were involved in this decision
as well splat provides an extensible bank of representative sentences and their spl structures from which the user can create new sentence plans
to accomplish these goals requires a facility that aids the user in creating and maintaining the desired input specifications in a principled and convenient way
the result of chunk detection shown in figure NUM is a forest of trees and serves as the input to the third pass
we used this table to learn the case frame tree in figure NUM and it suffered from the two problems
here we have two propositions related to the node set if and only if p is a traversal node set
we use the new notations in table NUM in addition to those in table NUM
the total nund er of ucc node sets in a tree is generally high
this is exactly the structured attributes problem that we mentioned in section NUM
at the beginning lasa NUM calculated the value for each attribute in the original table
table NUM shows our parameter estimates for the translation probabilities p y in
our task is to construct a stochastic model that accurately represents the behavior of the random process
in a no constraints are applied and all p c are allowable
only a small subset of this collection of features will eventually be employed in our final model
in the present example this information could include the words in the english sentence surrounding in
box NUM yorktown heights ny NUM t now at computer science department columbia university
NUM if y interchange and nounl systome f x y NUM otherwise
on the right are phrases such as saison d hiver for which the model strongly predicted an inversion
the french phrase taux d int ot for example is best rendered as interest rate
table NUM gives several examples of noun de noun phrases together with their most appropriate english translations
as shown below this heuristic method works well especially when the length of a compound noun is relatively short
in other words the relative magnitude of dependence between s and its candidate translations was used as a maximum likelihood estimator of the translations of s
a coded description consists of a specification of data in terms of a fixed set of attributes and a category to which the data are to be assigned
each line encodes a sentence as regards to textotype locationoin text similardeg ity text length attidudinal type within textotfidf location in paragraph and class in this order
based on summary extracts supplied by hum us we construct a collection of texts annotated with information on sentence importance
however as far as we know no question has ever been raised on the empirical validity of the extracts used
where f w s denotes a frequency of the word w in s and max f s the frequency of the most frequent word in s
the expected probability that a category is chosen at random is estimated as pi ci n k
a primary purpose of the paper is to demonstrate that the reliability of human supplied annotations on corpora has crucial effects on how well an automatic abstracting system performs
the traditional approach to automatic abstracting aims at providing a reader with fast access to documents by facilitating a judgement on their relevance to his or her information needs
the average performance of the generated models is then obtained and used as a summary estimate of the decision tree strategy for a particular set of evaluation data
figure NUM a partial decision tree figures in parentheses denote the number of hits left and misses right a particular path gives
if we restrict ourselves to html documents then explicit language lagging which represents the language of the text body will be introduced in a future version of html specifications
if the character set used in the text is known it might be good clue for identifying the language because some character sets strongly suggest which language s was used
results are shown in table NUM
figure NUM decision tree before pruning
tm the above analyses show that the algorithm fails quite rarely when the threshold is low and its performance can be improved with a sequence of increasing thresholds
resulted from a lack of generalization across segments
gildea and jurafsky learning bias and phonological rule induction figure NUM
a second problem is ostia s lack of generalization
figure NUM shows the subsequential equivalent of figure NUM
thus the order of their application is not significant
figure NUM initial tree transducer constructed with alignment information
we begin by briefly summarizing the decision tree induction algorithm
the application that dd l consider is the induction of english orthographic constraints that is inducing a field that assigns high probability to english sounding words and low probability to non english sounding words
as with equation NUM solving equation NUM is straightforward if l g is small enough to enumerate but not if l g is large
to say it another way our assumption that the corpus was generated by a context free grammar means that any context dependencies in the corpus must be accidental the result of sampling noise
i use the term feature here as it is used in the machine learning and statistical pattern recognition literature not as in the constraint grammar literature where feature is synonymous with attribute
we estimate weights using the erf method we estimate the weight of a rule as the relative frequency of the rule in the training corpus among rules with the same left hand side
fortunately solutions to the context dependency problem have been described and indeed are currently enjoying a surge of interest in statistics machine learning and statistical pattern recognition particularly image processing
in order for a stochastic grammar to be useful we must be able to compute the correct weights where by correct weights we mean the weights that best account for a training corpus
if we identify the features of a configuration with local trees equivalently with applications of rewrite rules the random field model is almost identical to the model we considered in the previous section
it provides a set of nlp tools making it possible to develop various applications such as intelligent natural language interfaces for databases communication aid systems computer assisted teaching or learning systems etc
a software for language education and rehabilitation of autistic like children elisabeth godbert pascal mouret robert pasero monique rolbert laboratoire d informatique de marseille lcb godbert mouret pasero rolbert rcb NUM im
generally speaking users will be able to express themselves on the subject proposed by the activity with the assistance of the system guided composition o f sentences or freely free composition
this multimedia nature of the software means that the different media are coordinated and organised in a coherent way and this seems to be a crucial point for persons with language cognitive and motor disorders
more particularly we have detailed through an example how the modularity of illico allows us to define several language rehabilitation exercises which have different levels of difficulty from a linguistic and cognitive point of view
the functionality of erel is described in part NUM part NUM goes into all the details of an activity proposed by the software and describes some specific elements of nlp required for its development
some forms need a more elaborated semantic processing and in particular definite descriptions like the black circle the pawn which is at the left of the square which contains the black triangle
they can use communication bords containing words whose quantity i.e. vocabulary depends on the capacities of the user graphic representations pictures and photos hierarchies of pictures e.g.
NUM NUM management information systems manager mism
appendix c sgml tag listing sgml tag description
collections act as the queues for the processes
adept will have the ability process more than one thousand separate sources from the five current oir providers at an average of NUM megabytes and a maximum of NUM megabytes per day currently
through a combination of menus customized panels and cutting and pasting operations the data administrator can specify the instructions to be used by adept to parse and extract data from incoming documents
all user interaction system adaptations and problem queue manipulation performed by the user are recorded in the am s audit log including a record of the change user identification and date time stamp
after successfully parsing and extracting document required information adept will transmit a sgml tagged document over a one way fiber to the rose catcher where the information will be archived and disseminated to the oir user community
error handling and document viewing adept maintains a problem queue and provides gui windows to aid the data administrator with both evaluating the source of problems data error or new changed format and resolving them
rose feed supplied documents are processed in the production collections
notes can created and saved for each document
do we tag phrases collocations idioms or just individual tokens
another source of errors is foreign names
both the recall and the precismn increase
title and punctuation are chics for boundary
otherwise it is a fenfinine name
it includes NUM NUM words and NUM NUM characters
it includes NUM NUM words and NUM NUM characters
finally the testing corpus is introduced
it includes NUM NUM words and NUM NUM characters
we will show that this mode is of great relevance to communication aids for the disabled
i a software for language education and rehabilitation of autistic like children
the corpora overall contain material drawn from widely disparate genres registers and are more complex than those used in darpa atis tests and more diverse than those used in mucs and probably also the penn treebank
these arguments show that the domain of polysemous usage is not lexical items but rather their referents
these heuristics are somewhat domain dependent different generalizations hold for names of drugs and chemicals than those identified for names of people or organizations
yet not having the partner title makes non lawyers working in law firms second class citizens said mr jordan of steptoe johnson
furthermore capitalization does not always disambiguate names from non names because what constitutes a name as opposed to a non name is not always clear
the resolution of structural ambiguity such as pp attachment and conjunction scope is required in order to automatically establish the exact boundaries of proper names
in addition given the current state of the art full parsing and extensive world knowledge would still not yield complete automatic ambiguity resolution
we have proposed a method of machine translation which acquires translation rules from translation examples using inductive learning and have evaluated the method
nl domain explanations in knowledge based mat
it is responsible for rule based analysis of texts and preparation of code to be passed over and executed by a blanking application zippity zap producing an exercise with deletions in places identified by the system as succeptible to orthographic errors
this resulted in the creation of simulations and text manipulation programs designed to expand students exposure to a foreign language outside of the classroom or to take over the teaching of chosen language skills lending themselves to computerization
as language learning theories shifted fi om belief in rote practice to communicative approaches call initially in consonance with the on going practice found itself increasingly at odds with teaching goals and consequently was placed outside of the classroom
future network based teaching learning environments may contain personalized information filtering systems determining contents and language levels of available materials
the fast development of the internet has increased the availability of resources for sell instructed language study
networking and multimedia seem to offer an answer to the issue of making call more compliant with communicative teaching by fostering human to human communication and creative endeavor as students engage themselves in learning driven by goal oriented tasks
skryba a module for self adaptive practice of polish orthography is part of a whole system of applications
following this sub division of thematic roles the clause sub grammar is divided into four orthogonal systems NUM transitivity which handles mapping of nuclear thematic roles onto a default core syntactic structure for main assertive clauses
it has features of both intelligent tutoring systems and microworld learning environments
fidelity is the accuracy of a presentation
the lailure of scw certainly does not mean we should all go home
real language teachers have given design advice and now use the system
a technical drawing may have good conceptual fidelity if it connects related concepts
the student interacts with the system by direct use of the target language
a system can be designed to be extendable within a language
our generative recombinative animation approach does not encounter this constraint
it is useful to consider a straightforward argument for according nlp a central role in call
NUM the proof of complete matdhtn gprinciple and the application of matching restriction schemes guarantee the soundness and efficiency of the matching algorithm
traditionally disambiguation problems in parsing have been addressed by enumerating possibilities and explicitly declaring knowledge which might aid most interesting natural language processing problems
NUM linearize the syntactic tree into a string of inflected words following the linear precedence constraints
cohmm NUM shows the precision and the recall of the baseline model
today a nllmber of large corpora are available and research using these resources has expanded well beyond the relatively small industrial research community
the dialogue history or context guides the selection of semantic choices i.e. pure initiating moves e.g. request correspond to exchanging initiating while responding initiating moves e.g. inform request correspond to exchanging responding in a first grammar traversal and exchanging initiating in a second
if we consider the appearance of a list on the screen a metaphor for actually handing over an object this situation corresponds to demanding goods NUM services i.e. the speech function is command hence we suggest that offers are realized as imperatives with tone NUM e.g. bitte ws hlen sie eins
assuming that intonation is more than the mere reflection of the surface linguistic form see NUM NUM NUM NUM and further that intonation is selected to express particular communicative goals and intentions an effective control of intonation requires synthesizing from meanings rather than word sequences as the discussed systems do
in this version we only present the request inform and assert moves in detail since the other moves are cast in the same format as the request and one only has to insert new move names e.g. promise promise k dialogue s etc
if the alternative is reasonably close in our example there is a time difference of NUM minutes which for this scenario might be considered a good alternative we find it appropriate to generate a yes no question with tone 2b interrogative yes no type informationseeking unmarked strong assessment
given that all of the parameters dialogue move type dialogue history speech function mood and key are logically independent and that different combinations of them go together with different selections of tone an organization of these parameters in terms of stratification suggests itself for it provides the required flexibility in mapping the different categories
a dialogue model guides the interaction between a user and an information retrieval system i.e. it calculates a subset of possible dialogue acts that the user action spoken or deictic could correspond to and on the system side it calculates those dialogue acts that would provide appropriate responses to a given user action
for the representation of constraints between dialogue moves on the dialogue side and speech function on the side of interpersonal semantics and mood and key on the part of the grammar this means that a good candidate for the ultimate constraint on tone selection is the type of move in context or the dialogue history
this is however an unexpected move on the part of the user hence we suggest that these requests again mapping to question on the speech functional level are realized as yes no question as opposed to declarative with tone NUM see above i.e. do you want to quit vs you want to quit
once alignment information for each input output pair has been computed an output symbol can be rewritten in variable notation in constant time
this is a rather pessimistic bound since pruning occurs after state merger and there are generally far less than nk states left
computational linguistics volume NUM number NUM automaton induction enriched in the way we suggest may contribute to the current debate on optimality learning
for simplicity some of the phones missing from the transitions from state NUM to NUM and from NUM to NUM have been omitted
because we used binary phonological features we obtained binary decision trees although we could just as easily have used multivalued features
testing each pruning operation against the entire training set is expensive but in the case of synthetic data it gives the best results
second depending on the particular training data this lack of generalization can cause the transducer to make mistakes on learning such rules
this is possible since such a transducer will accurately cover the training set as no english words contain six consonants followed by a t
ostia s induced transducer not only is much more complex between NUM and NUM states but has a high percentage of error
we test our idea by examining the machine learning of simple sound pattern of english s p e style phonological rules
clearly since the probability p is a number between NUM and NUM log p is a number in the range NUM ec
the authors wish to thank dr anastassios tsopanoglou and dr evaggelos dermatas for their comments on the paper and their valuable assistance in the final preparation of the manuscript
to benefit from the above transformation a fixed point arithmetic should be used floating point addition is as troublesome as floating point multiplication if not more
in tables NUM to NUM a summary of the conversion results is presented for the three sets of experiments carried out
the model performed worse for english than for the other languages mainly because the relationship between pronunciation and spelling is less regular
therefore a higher order hmm and a multiple output conversion algorithm were employed in order to overcome these disadvantages and achieve better results
since the results of the first order hmm system were encouraging we decided to develop an improved version of the system
rentzepopoulos and kokkinakis phoneme to grapheme using hmm let g lcb gl g2 gm rcb be the set of phonemes and
finally some conclusions are drawn about the system and topics for further research and hardware implementation are discussed in section NUM
typical esl classes express the following dependency relations noun preposition noun n p n verb preposition noun v p n adjective conjunction adjective adj c adj and others
most likely for each preposition means use the attachment seen most often in training data for the preposition seen in the test quadruple
the index p would of course also be internal to the sentences the young polish athlete ran fast the tall polish athlete ran fast etc
with the help of this ontology we have realized a typology for intransitive motion verbs
indeed verbs like sortir intrinsically suggest a location of which we have gone out
these new properties are only the result of the interaction of the verb with the preposition
the semantics of a motion complex is not the simple addition of the semantics of its constituents
note that all the geometrical possibilities are not lexicalized in french
these links called discourse relations are basic concepts on which texts are structured cf
these axioms are based on the lexieal semantics of col verbs and of spatial prepositions
on the contrary it is the result of a complex interaction between these properties
change of position verbs voyager to travel courir to run denote a change of position
we also compare with the english language and draw some conclusions on the benefits of our approach
reliability on intentional and informational relations was around NUM high enough to support tentative conclusions
finally for purposes of comparison with other studies of segmentation we report percent agreement
neither study presented any quantitative analysis of the ability to reliably perform the initial utterance classification
we present quantitative results of a two part study using a corpus of spontaneous narrative monologues
table NUM precision improvement in nlir
the predicate adjoin px x is true if and only if the restrictions governing adjunction in tig permit the auxiliary tree px to be adjoined on the node x
the entry for a given row and column holds two figures showing respectively the number of examples where the row variant produced a better translation than the column variant and the number where it produced a worse one
in fact as the detailed breakdown shows even this underestimates the effect on the main parsing phase when both pruning and ebl are operating processing times for other components morphology pruning and preferences become the dominant ones
here the basic idea is that a given small segment s of the input string may have several possible analyses in particular if s is a single word it may potentially be any one of several parts of speech
our algorithm estimates the probability of correctness of each edge that is the probability that the edge will contribute to the correct full analysis of the sentence assuming there is one given certain lexical and or syntactic information about it
to sum up the methods presented here demonstrate that it is possible to use the combined pruning and grammar specialization method to speed up the whole analysis phase by nearly an order of magnitude without incurring any real penalty in the form of reduced coverage
this property is desirable different sub paths through a chart may span different numbers of edges and one can imagine evaluation criteria which are only defined for some kinds of edge or which often duplicate information supplied by other criteria
in this example the lexicon contains the entries in NUM
the predicates are depicted top down in NUM
r4 is the obligatory vowel deletion rule
the algorithms presented below are given in terms of prolog like non deterministic operations
the invalidity of a partition is determined by invalid partition listing NUM
a clause is satisfied iff all the conditions under it are satisfied
in the folk wing sections we first present the l copyright NUM by ioughton miftlin company
itere we calculate the number of occurrence of each word within the definitions of nouns and verbs in our dictionary
again if matching is over the graph matching threshold perform integration and add the word to the concept cluster
using the criteria described in the previous section only the word message is a semantically significant word ssw
we describe a few elements of this relaxation process and illustrate them by an example in figure NUM
for our current investigation we propose this as the division between semantically significant words and semantically insignificant ones
instead of a single trigger word we now have a cluster of words that are related through the cckg
graph matching was also suggested as an alternatiw to taxonomic search when trying to establish semantic similarity between concepts
by the end of the analysis process justice departmen has a high negative score for person and a low positive score for organization resulting in its classification as an organization
the practice of law in washington is very different from what it is in dubuque he said some of these non lawyer employees are paid at partners levels
it may be one of the variants found in the document or it may be constructed from components of different ones as the links are formed each group is assigned a type
these two substrings are better balanced than the substrings of the food and drug administration where the left substring does not contain a strong scope np head while the right one does administration
nominator s other heuristics resemble those discussed above in that they check for typographical patterns or for the presence of particular name types to the left or right of certain operators
name identification requires resolution of a subset of the types of structural and semantic ambiguities encountered in the analysis of nouns and noun phrases nps in natural language processing
since pre modifiers can contain conj unctions japanese painting and printing museum the conjunction is within the scope of the noun and so the name is not split
absence of repetition human letters NUM NUM out of NUM better
i remain at your entire dlvposal should you require any jitrther assistance
the test cycle was performed six timcs
la livraison dtait diffdrde de deux semaines
the deliver was postponed by two weeks
the fact that the path sets generated by a tig can not be more complex than context free languages follows from the fact that tigs can be converted into tags generating the same trees
average all the automatic and human letters met the eliminatory criteria standards
proximity personalisation NUM NUM better than the semi automatic system
precision in the choice of vocabulary NUM NUM better
NUM NUM linguistic and template example chore madame
this will yield a feature structure corresponding to NUM
these enhancements greatly improve the performance of bayes over the naive bayesian approach
the impact on normal human behavior may be intended
greater clarity of sentence discourse and argument structure
in theory the dop model has several advantages over other models
unfortunately existing algorithms are both computationally intensive and difficult to implement
let aj represent the number of subtrees headed by the node a j
given our example there are two different ways to generate the parse tree
to show this reduction and equivalence we must first define some terminology
furthermore we believe bod s analysis of his parsing algorithm is flawed
there are eight cases one for each of the eight rules
thus every stsg tree would be produced by the pcfg with equal probability
the discourse processor is described in greater detail else where r osd et NUM
with welldeveloped generation grammars genkit results in very accurate translation for well specified iui
tile parser has no restrictions on the order in which slots ca occur
variables such as times and dates are extracted from the parse analysis and translated directly
at the current time phoenix uses only very simple disambiguation heuristics does not employ any discourse knowledge and does not have a mechanism similar to the parse quality heuristic of glr which allows the parser to self assess the quality of the produced result
on speech recognized input although the overall percentage of acceptable translations does not improve the percentage of perfect translations was higher NUM 2in a more recent evaluation this combination method resulted in a NUM NUM improvement in acceptable translations of speech recognized in domain sentences
the loglinear model for this task used the features preposition
in practice the architecture committee will appoint a small group of people to sit on the configuration control board ccb to examine in detail the documentation and justifications provided at the pdr and foc control gates
the cm review process described in detail below 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
the cmm is responsible for a implementing a cm system which is tailored for the unique features of the tipster text phase ii program and b developing cm procedures to assure control of documentation
the v v testing engineer controls formal csci test procedure development prior to those components being placed under formal cm control reports problems identified during formal csci testing to cm and actively participates in erb and ccb meetings
the tipster phase ii evaluation working group ewg is tasked with designing implementing and coordinating evaluations intended to demonstrate the effectiveness of tipster architectural approaches thus the ewg will be a user of the results of the cm process by using the appropriate architectural version in their evaluations
the membership of the ewg is composed of the following core personnel chairperson se cm representative NUM nsa representatives NUM cia representatives NUM bbn umass representative NUM mmc ge representative representatives from the other contractors the membership of the ewg is composed of the following adjunct participants evaluation representatives trec muc demonstration representatives sponsor contractor NUM persons
the rfc will contain the following status routing sheet submission form review sheet cawg comments or recommendations any relevant material such as email traffic the rfcs will be available on the internet on the tipster web page
the ccb reviews each class i change and makes a decision as to the disposition with the options of a approve the change b disapprove the change or c defer the proposed change
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 and the tipster application s design
the documents which will be placed under cm are tipster requirements document tipster architecture design document tipster architecture interface control document tipster architecture concept tipster cm plan tipster validation and verification plan tacad
NUM we see from the table that the specific mutual information scores fail to identify aujourd hui as the best candidate it is only ranked fourth
after carefully studying the errors produced we suspected that the dice measure would produce better results for our task according to the arguments given above
even though the stories probably use equivalent terminology totally different techniques would be necessary to be able to use such nonalignable corpora as databases
in addition running xtract on the french part of the corpus would allow for independent confirmation of the proposed translations which should be french collocations
for this purpose we stratified NUM the curves in figure NUM become noticeably less smooth for values of the final threshold that are greater than NUM NUM
considering the size of the corpora that must be handled by champollion special care has been taken to minimize the number of disk accesses made during processing
with corpora of this magnitude champollion takes between one and two minutes to translate a collocation thus enabling its practical use as a bilingual lexicography tool
for the remaining words in the list we need to compute their dice coefficient value so as to select the best ranking one word translation of the source collocation
let x and y stand for the source collocation and the french word under consideration respectively at some step of the loop through the word list
there are several different evaluation metrics one could use for finding the best parse
however we have not encountered any examples of nonfunctional but consistent descriptions that are not better expressed by a straightforward functional counterpart
filling in the gaps between these definitions we can see that many paths will be implicitly defined only by the empty path specification
the significance of the latter approach is that it reduces both kinds of inheritance to a single basic operation with a straightforward declarative interpretation
if the result is a global descriptor this is used to construct a new query which is evaluated in the same way
since a quoted node path pair completely respecifies both node and path its immediate inheritance characteristics are the same as the unquoted node path pair
the longest defined subpath wins principle amounts to conflict resolution built into the formalism however it does not deal with every case
2deg indeed datr s approach to default information always implies an infinite number of unwritten datr statements with paths of arbitrary length
definition by default introduces new datr sentences each of whose left hand side paths is an extension of the left hand side paths of some explicit sentence
note how mor itself takes its definition from since all the explicitly defined mor specifications have at least one further attribute
for a contingency table of dimensions m x n if the null hypothesis is true the statistic
and the desired compositional treatments requires that the information concerning the pronouns is to be found in the sentences uttered so far i.e. as included within the scope of a logical quantifier or connective
the experiments described here have been done in connection with the ls gram project which is concerned with the development of large scale grammars and thus foreseen the coverage of real life texts
dealing with cross sentential anaphora resolution in alep
in short groenendijk and stokhof are missing the compositional building of the semantic representation and also would prefer to use a more classical representational language like the one of first order logic
the phrase structure rule responsible for the building of the paragraph structure is simple
the mother node simply allows a binary branching of two sentential daughters
it is still to be investigated how sophisticated such a treatment can be
he hates it i negation and disjunction are static
languages project is funded by the cec under the number lre NUM
in this tool the sentence is defined as tile default linguistic unit
does this show that all those words have systematically different patterns of usage in british and american english
therefore we have developed a variant probabilistic lr parser which does not rely on subcategorisation and uses punctuation to reduce ambiguity the analyses produced by this parser can be utilised for phrase finding applications recovery of subcategorisation frames and other intermediate level parsing problems
we then mapped these entries into our pos grammar experimental subcategorisation scheme in which we distinguished each possible pattern of complementation allowed by the grammar but not control relationships specification of prepositional heads of pp complements etc as in the full anlt representation scheme
in addition to the core text grammatical rules which carry over unchanged from the stand alone text grammar NUM syntactic rules of pre and post posing and coordination now include often optional comma markers corresponding to the purely syntactic uses of punctuation
the various phases in the development and refinement of the grammar can be observed in an analysis of the coverage and apb for susanne and sec over this period see table NUM the phases with dates were NUM NUM NUM NUM initial development of the grammar
for example past participles functioning adjectivally as in la are fl equently tagged as past participles vvn as in lb so the grammar incorporates a rule violating x bar theory which parses past participles as adjectival premodifiers in this context
both these approaches achieve better coverage by constructing the grammar fully automatically but as an inevitable side effect the range of text phenomena that can be parsed becomes limited to those present in the training material and being able to deal with new ones would entail further substantial treebanking efforts
the table also gives an indication of the best and worst possible performance of the disambiguation component of the system showing the results obtained when parse selection is replaced by a simple random choice and the results of evaluating the analyses in the manually disambiguated treebank against the corresponding original susanne bracketings
the median error rate is lowered considerably and samples with error rates over NUM are eliminated entirely
this is due to the fact that in our grammar an adverb is an adjunct which modifies a vp
we present an algorithm which assigns interpretations to several major types of ellipsis structures through a generalized procedure of syntactic reconstrtiction
the algorithm generates appropriate interpretations for cases of vp ellipsis pseudo gapping bare ellipsis stripping and gapping
reconstruction account of bare ellipsis which adjoins an np in the antecedent clause to an np fragment by lf movement
they contain information which is not overtly expressed but which must be recovered through the identification of an antecedent
NUM construct a list arg list of the phrases which fill the subcat list slots of a
NUM the upper model concept attributed to the word is retrieved from the spl input
splat will retrieve all the sentences in the sentence bank which match the pattern
the grammatical function of a word is derived from the output of the penman generator
for each kind of template splat provides most of the roles necessary to construct this type of spl plan
the corresponding spl plan template can then be used as the model or component of the new spl plan being built
the spelling corresponds to the actual spelling of the word in the sentence and is retrieved from the generator output
the lexical item is the lexical unit used by penman to generate the word derived from the spl input
the sentence bank provides the user with a convenient method for saving spl plans and indexing them for later reuse
splat provides te mplate forms for each type of spl plan relations processes objects and qualities
the spl plan templates display the necessary keywords for the particular type of plan and splat automatically enforces correct spl syntax
a similar drop NUM was true for the inquery results even though the new algorithm resulted an almost NUM improvement in results for the trec NUM topics
all parser output must conform to this ill speeitication
it disainbiguates the speech act of each sentence normalizes temporal
these grades are used for both in domain and out of domain sentences
the evaluations are performed by one or more independent graders
although some variation between test sets is to be ex
this can cause added ambiguity in the segmentation of the utterance into concepts
a diagram of the architecture of the system is shown in figure NUM
this facilitates the easy adaptation of the system to new languages and domains
these segments of the input were ignored by the parser
the ianus phoenix translation module mayfield et el
the ultimate test of this approach is in how well it will scale up
i examine how terminological languages can be used to manage linguistic data during nl research and development
note that verb defines one verb whereas verb class describes a set of verbs e.g.
to assist in this task i provide two tests have instances of and have no instances of
this causes the event arguments to be filled with the appropriate gf fillers from the subcategorization
finally there is the problem of collecting complete sets of example sentences for a verb
in conclusion i discuss ways in which terminological languages can be used during grammar writing
because of this i created a test written in lisp to identify a legal linking
verb types subcategorizations are defined according to the gfs found in the sentence
together these functions provide tools for the lexical semanticist that are potentially very useful
that is the model does not account for surrounding english words when predicting the appropriate french rendering of an english word
such a model is a method of estimating the conditional probability that given a context x the process will output y
we denote by p the p where a p achieves its maximum and by the value at this maximum
another involves the possibility of integrating the two processes since the sentence realiser has access to the same knowledge as the multi sentential planner it can make decisions without requiring explicit informing from the planner
she is dealing with a set of objects which may potentially appear in the text at this point while i am dealing with the set of objects which most probably do appear in the text
the rhetorical structure thus organises the ideational content to be expressed selecting out those parts of the ideation base which are relevant to the achievement of the discourse goals at each point of the text
ideational unit i cdegnscidegus q human
this is not necessary in an integrated approach
knowledge about john and mary into the kb
now we are ready to express this knowledge
however problems soon arose with this approach
or perform some action in response to the utterance
an average of NUM terms were automatically selected from the top NUM documents retrieved only initial and final passages of these documents were used for term selection
the pircsl run was a result of more expansion but this was due to corrections of problems in trec NUM as opposed to changes needed for the shorter topics
instead the advance of topic expansion techniques caused major improvements in performance with less user input the concepts
but the more varied the individual techniques the more need for elaborate combining methods such as used in the rutfual run
the likely explanation is that the automatic term expansion methods are relatively uncontrolled in trec NUM and manual intervention plays an important role
in trec NUM as opposed to trecs NUM and NUM the manual query construction methods perform better than their automatic counterparts
the topics were expanded to create a query averaging around NUM terms and then were run using the default cornell smart system
figure NUM shows the recall precision curves for the NUM trec NUM groups with the highest non interpolated average precision using manual construction of queries
the westpl nan was superior to the inqio1 run for l i topics mostly caused by better ranking for those topics
figure NUM shows the recall precision curves for the NUM trec NUM groups with the highest non interpolated average precision using automatic construction of queries
the language produced by the learner is more often than not agrammatical yet this does not prevent the tutor to proceed with the dialog
these are metarules that allow the user to specify the simple domain relevant subject verb object s v o patterns and have the system expand them into all the linguistic variants with the same semantic content including passives gerunds infinitives and relative clauses
furthermore g can be chosen computational linguistics volume NUM number NUM so that all the auxiliary trees are right auxiliary trees and every elementary tree is left anchored
the effect of this adjunction is exactly the same as substituting the corresponding t e i in place of u and then substituting u for the first nonempty frontier node of t
we have chosen unlimited simultaneous adjunction here primarily because it reduces the number of chart states since one does not have to record whether adjunction has occurred at a given node
however it can be straight null schabes and waters tree insertion grammar forwardly converted to a parser by keeping track of the reasons why states are added to the chart
the time complexity of the parser can be reduced from o igi2n NUM to o igin NUM by using the techniques described in graham et al NUM
let t c i u a be an elementary tree whose root is labeled y and let be a frontier element of t that is labeled x and marked for substitution
each instance t of one of the new trees introduced is replaced by an instance of t with the appropriate initial tree u e i being combined with it by substitution
in particular if a decreased number of elementary trees is accompanied by decreased sharing this can lead to an increase in the grammar size rather than a decrease
in the worst case the number of elementary trees created by the ltig procedure above can be exponentially greater than the number of production rules in g
users can search the sentence bank to find examples of a particular sentence or partial sentence pattern
for the greater part of the frequency range there is a relatively stable proportion of participles to finite past forms
thus for the high frequency ranges the data is weighted heavily towards verbs
this yields the maximum likelihood estimate mle for the lexical prior probability
the overall mle computes a lower relative frequency for the infinitives compared to the hapax based mle
to answer this question we compared the accuracy of the overall and hapax based mles using tenfold cross validation
the probability of encountering an unseen word given that this word is a word in en is estimated by
at the left hand edge of the graph the relative frequency of the infinitives for the hapax legomena is shown
contrariwise the hapax based mle predicts that the nominal function would be more likely
a function of the natural log of the frequency of the word forms
much recent work in this flamework hm used written and spoken natural language data to estimate parameters for statisticm models that were characterized by serious limitations models were either limited to a single explanatory variable or
normally the corporate designator co corp would assist in identifying an organization
this tendency is strengthened by the use of comma after the phrase toki wa
the modality coincidence described in this section is the base for analyzing japanese long sentences
unfortur ately there were few such cases in the data used in this paper
second we state moditication l refi3renee of rcb apanese conjunctiw
filmlly we present evidence of tile h vels of aaplnlese function words
figure NUM shows the configuration of our japanese long sentence analyzer based on ldg
in these two selt NUM es brackets lcb rcb show subordinau clauses
two corpora we haw l esl ed olir niei hod on i wo i echnicm niedium size col pora
we are very gratefid to serge heiden eli who has developed g aphx ftp mycroft
the building of an ontology which is a timeconsuming task and which can not be achieved automatically can nevertheless be guided
this model assumes that japanese conjunctive particles convey modality and modality structure can basically be detected by lexical information
simple parsing of japanese long sentences inevitably produces a huge number of possible modification structures
indicates that the input symbol is emitted with no features changed
if any other symbol follows however the original transducer for word final stop devoicing
but it is by no means necessary to assume that this knowledge is innate
figure NUM onward tree transducer for bat batter and band with flapping applied
figure NUM and table NUM show ostia s failure to learn the simple flapping rule
we could then compare results with collins and brooks disambiguation system which was also tested using the penn treebank s wall street journal corpus
NUM NUM best analogue s funcl ion
resolving syntactic ambiguities with lexico semantic patterns an analogy based approach
the performance of the cbl algorithm is compared to that of NUM a default rule that always chooses the most recent phrase as the antecedent and NUM a set of hand coded heuristics developed for the same task specifically for use in the terrorism domain
word alignment system that utilizes both existing and acquired lexical knowledge
the system contains the following components and distinctive t atures
table NUM lists the connection in the final solution of alignment
instead only strongly associated word pairs are ibund and stored
this kind of generality is unattainable by statistically trained word based lnodels
the initial list of selected connection contains two dummy connections
this establishes the initial anchor points tbr calculating relative distortion
section NUM summarizes the results of inside and outside tests
connection candidates can be evaluated using various factors of confidence
given the freedom of the task and the use of untrained subjects a reliability test would be relatively uninformative it can be expected to range from very low to very high
in addition the group has advanced the state of the art for identifying co referential noun phrases
conforming to the tipster architecture allows various extraction and detection modules to work together
particular techniques for integrating extraction and detection technology through a series of experimental results
in text together with a set of tools for easily constructing recognizers for new objects
telephone NUM NUM NUM introduction the lockheed martin tipster ii project focused on several research areas
laboratory fidul the project will include tipster detection and extraction tools integrated with a common document manager
the best of these experimental techniques will be integrated in the joint lockheed martin ge cr d nyu rutgers submissions for trec NUM
in addition three demonstration projects are underway to exhibit the feasibility of transitioning the research into an operational setting
that review neither constitutes cia authentication of information nor implies cia i endorsement of the author s views
table NUM baseline performance on development set
i a maximum entropy model for part of speech tagging
table NUM performance of baseline model with specialized features
where the model s feature expectation is
figure NUM distribution of tags for the word about vs annotator
return highest probability sequence s l
the results of such an experiment NUM are shown in table NUM
testing the model the test corpus is tagged one sentence at a time
the count of NUM was chosen by inspection of training and development data
say and iq study of languages are discarded
the estimate of the unigram probability and the bigram probability can be obtained as the relative frequency of the associated events
recent years have seen several works on corpus based word segmentation and dictionary construction for both japanese and chinese
we used the word bigram model for word segmentation and expected word frequency for unknown word extraction
figure NUM comparison between the corpus segmentation left and the system segmentation right
the problem of japanese word segmentation is that people often can not agree on a single word segmentation
by using the word model we can create modified segmentation models that take unknown words into consideration
NUM NUM therefore NUM z11 NUM is definitely more likely
this is because most tin null sys raatched and not extracted new words std matched
the expected total number of tile words in tile sentence ci w is NUM NUM
to conduct such studies it is necessary to gather data that is to perform ambiguity labeling on texts and transcriptions of spoken dialogues
each identified field value is validated and normalized if required before being stored as annotations with the document via dm function calls
the latter two programs are currently under the develol ment and will be integrated later
tree tagged corpus this can be produced by applying syntactic tagset to the pos tagged corpus
it uses hypertext markup language html based on standardized generalized markup language sgml
this work is being coded on pc windows and will output the first draft version this year
fundamental technology deals with radical and theoretical researches collection and nlanipulation of data and standardization
for knowledge processing it will cover document paraphrasing indexing and retrieval computer based instruction education etc
according to the level of technologies kle partitioned its projects into ttiree classes
of target language expressions but offer basic meanings for entries together with some syntactic and morphological information
this resource can be used or speech recognition and synthesis applications
finally documentation preparation will also be accompanied with the project s progress
null NUM the next training instance is accepted
so we need to have a look at this method
clearly mutually depemhmt fleature values need to be eonsidcre l i.e.
the phrase will be reparsed in order to determine the linearization of constituents
holding a fixed we compute the unconstrained maximum of the lagrangian a p over all p e
table NUM compares performance on a suite of test data against a baseline noun de noun reordering module that never swaps the word order
had the algorithm terminated when the log likelihood of the withheld data stopped increasing the final model p would contain slightly less than NUM features
the dtla then evaluates the purity of a in terms of the entropy of the class distribution h a
since lfs capture the primary relations in a whole clause their frequency captures dependencies that traditional statistical approaches such as bigrams and trigrams would miss
to evaluate this quantitatively we assign a t enalty score s p to each node p in NUM a
there are several pos sible candidates for the penalty score function and we hose the formula NUM for this research
a when a computerized call is made to a former prisoner s home phone that person answers by plugging in the device
we first present an algorithm called t which can solve the sub problem for structured attributes and then present the whole algorithm of lasa NUM
then as described in section NUM a set of pruning rules based on centers and discourse relations are used to select the content of the summary
we have proposed a decision tree learning algorithm inductive learning algorithm with the structured attributes lasa NUM that optimally handles the structured attributes
the lasa NUM package still has some unmentioned features like the handling of the words unknown to the thesaurus and different a parameter setting
the average tree size measured by the number of leaves for dtla was NUM NUM which dropped to NUM NUM for lasa NUM
the tipster architecture can be divided into the following major functional areas
the user interface functional area is not part of the tipster architecture
each template should support a tabular or spreadsheet view of extracted information
the resulting specifics can be used to aid in the query formulation
the specific extraction methodology and algorithms are dependent upon the particular application
see reference NUM NUM verification method demonstration
specific annotation type s shall be assigned for user annotations
these are the word lists used by a part of speech tagger
it is understandable by a user and processable by the detection component
generally modules are composed of no more than NUM lines of code
on the other hand there is evidence that encouraging users to interact with machines as if they were humans may actually undermine the quality of the users speech from the point of view of language processing
by the inductive hypothesis the substitutions specified by lemma NUM result in trees that are either left anchored or have first nonempty frontier nodes labeled with a l where i j
the following objection to this definition might be made the fact that interactors use words that their partners have used does not necessarily mean that they are accommodating to the other s prior use of that word
this would assume that it is in the best interest of the government to deviate from the architecture in this particular case
this document presents the tipster text phase ii configuration management cm plan for identifying controlling and auditing the tipster architecture status and configuration definition
a vendor s product may be determined to be tipster compliant with the use of a tacad independent of actually being part of a tipster application
w lcb men y o xuf sh nghu6 d6 y6u yiyi we want learn life csc have meaning we want to learn how to lead a meaningful life
this complex structure consists of the multi agency executive committee and architecture committee architecture contractors demonstration and prototype development contractors cotrs and the se cm contractor
if a group of characters is enclosed by two rectangles for example the character NUM sh ng it indicates that a chunk object exists made up of word objects
to determine the proper use of two juxtaposed predicates such as j kai open and fa distribute in this case requires a careful study of serial verb constructions
cm conducts audits of the architecture baseline to ensure conformance with the tipster concept and to verify that the configuration management library system is functioning adequately
to summarize the macroscopic behavior of the system is not preprogrammed the details of how it emerges from the low level stochastic architecture of the system are given in sections NUM NUM and NUM NUM
the corresponding nodes in the conceptual network namely character affinity affix ta b n up to zi are set to full activation
the decision about which competing structure should win is decided stochastically as a function of two factors i the strengths of the competing structures and ii the temperature
this area can be thought of as corresponding to the locus of the creation and modification of mental representations that occurs in the mind as one tries to form a coherent understanding of a sentence
however the proportions plots on the second row show a final dip suggesting that at the very end of the novel a more than average number of normally dispersed new types appears
two of the mainlanders also cluster close together but interestingly not particularly close to the taiwan speakers the third mainlander is much more similar to the taiwan speakers
however it is almost universally the case that no clear definition of what constitutes a correct segmentation is given so these performance measures are hard to evaluate
an example of a fairly low level relation is the affix relation which holds between a stem morpheme and an affix morpheme such as meno pl
note that the sets of possible classifiers for a given noun can easily be encoded on that noun by grammatical features which can be referred to by finite state grammatical rules
in the numerator however the counts of n ts are quite irregular including several zeros e.g. rat none of whose members were seen
not surprisingly some semantic classes are better for names than others in our corpora many names are picked from the grass class but very few from the sickness class
roughly speaking previous work can be divided into three categories namely purely statistical approaches purely lexical rule based approaches and approaches that combine lexical information with statistical information
NUM throughout this paper we shall give chinese examples in traditional orthography followed immediately by a romanization into the pinyin transliteration scheme numerals following each pinyin syllable represent tones
we will say that individual words or phrases evoke particular frames or instantiate particular elements of such frames
we expect to be able to draw tentative conclusions about this based on what we find in corpora
the partitioning is necessary as it is difficult to accommodate the large ratio of the term hierarchy on the screen
it is widely believed that a set of good terms can be used to express the content of the document
the generated key terms are organized in a hierarchical structure and fed into a graphic user interface gui
the prototype system despite its prototype mode has proven to be useful and applicable in the commercial business environment
frequency of key term usage is the metric used to organize and partition the term hierarchy in an ascending numerical order
the right area of the gui component see figures NUM and NUM is occupied by the document browser
the browser provides the user with the ability to quickly navigate through the document collection to locate relevant key terms
figure NUM the user interaction is structured around term retrieval and navigation as the top level user interactions
they pointed out that the system is particularly helpful when dealing with a completely new or unfamiliar topic
the elements of such frames are the individuals and the props that participate in such transactions which we call frame elements the individuals in this case are the two protagonists in the transaction the props are the two objects that undergo changes of ownership one of them being money
we start from the top of this list and work our way downwards until we find a word that fails either of the following tests
and u h they come over and they help him and you know help ihiml pick up the pears and everything
quantitative evaluations of subjects annotations using notions of agreement interrater reliability and or significance show that good results can be difficult to achieve
finally while our results are quite promising how generally applicable are they and do results such as ours have any practical import
the cost of the dependency between two nodes is given by nsing mulual information between the lexical heads of ihe taxies fig
patterns in NUM NUM collect the evidence of an adjectival modifier modifiee relationship between an adjective or an adjectival noun and a noun
c3 cl c log2 n headl head n headl n head2
there are several reasons for this NUM an identical proper noun normally does not appear many times in the corpus
forty two percent of the error was caused by proper nouns NUM by time expressions and NUM by monetary expressions
our approach also encounters this problem although it turned out to be not serious as will be explained in section NUM NUM
the performance of our method accuracy of NUM is encouraging since most of the errors were caused by proper nouns
this paper presents a corpus based approach which scans a corpus with a set of pattern matchers and gathers co occurrence examples to analyze compound nouns
it is important that there is a feedbackloop from edb to wl through which newly tbund words can be a ted to wl
in the word unigram and word bigram models the joint probability p w t is approximated by the product of word unigram probabilities p wi ti and word bigram probabilities
how and by whom should they be developed
besides this aspect evaluation will also benefit from semantically tagged test corpora
it consists of a forward dynamic programming search to record tlle probabilities of all partial word segmentation hypotheses and a backward a algorithm to extract the n best hypotheses
in the part of speech trigram model p wi ti for an unknown word wi is obtained by definition from the word model p wi unk
in japanese illustration is transliterated into NUM characters NUM b NUM NUM which exceeds tile maximum unknown word length of NUM characters in our system
since NUM NUM the transliteration of illust which also means illustration in japanese is registered in the dictionary t NUM the
note that tile original forward algorithm and tile viterbi algorithin is the special case in equation NUM and NUM where p and q are fixed as p q NUM andr q i
there were NUM distinct character bigrams in the words in the training texts NUM athere are more than NUM some say nlore than NUM charters in japanese and their frequency distribution is skewed
first we count the number of words in corpus segmentation std the number of words in system segmentation sys and tile number of matching word segmentations m
given its high frequency NUM one would expect it to occur in all NUM text slices but it does not
computational linguistics volume NUM number NUM we know that constraints the domain of which are restricted to the sentence can be ruled out
there was still a small amount of error in the final transducer and in the next section we show how this remaining error was reduced still further
purely to make the diagram easier to read we have used c and v to represent the set of consonants and of vowels on the arcs labels
our model of faithfulness preserves the insight that barring a specific phonological constraint to the contrary an underlying element will be identical to its surface correspondent
although ostia is capable of learning arbitrary sfsts in the limit large dictionaries of actual english pronunciations did not give enough samples to correctly induce phonological rules
feature geometric theories traditionally proposed a unique language universal grouping of distinctive features to explain the fact that phonological processes often operate on coherent subclasses of the phonological features
the id3 algorithm is given a set of objects each labeled with feature values and a decision and builds a decision tree for a problem given
we add prior knowledge to the induction by adding language bias that is the induction language will use phonological features as a language for making decisions
the intuition that ostia is missing then is the idea that phonological constraints are sensitive to phonological features that pick out certain equivalence classes of segments
again this correct generalization all stressed vowels is expressible as a single node decision tree over the phonological features of the input phones
we took the arcs leaving each state of our transducers and used a decision tree induction algorithm to replace them by a smoother and more general set of arcs
consider the following sentence NUM george s cousin bought a new mercedes with her portion of the inheritance
each estimate averaged below NUM second on sentences of fewer than NUM words
figure NUM also demonstrates that its performance generally worsens as sentence length increases
edge creation is generally considered the best measure of cfg parser effort
we refer to this figure of merit as normalized o lfl
introduction chart parsing is a commonly used algorithm for parsing natural language texts
the other two estimates seem to continue improving with greater sentence length
figures of merit for best first probabilistic chart parsing
we call this quantity the left outside probability and denote it ai
let mi5 be the expected cell count for cell i j
table NUM a hypothetical agreement table
data reliability and its effects on automatic abstracting
in table NUM we can recognize the sharp decrease in incomplete matching rate from NUM NUM dtla to NUM NUM lasa NUM
there is no guarantee that our method is the best so it would be better to explore for a better criterion to decide these values
the penalty score n this research was designed so that we get the maximum generalization if the error term in formula NUM stays constant
the optimum attribute generalization given a tree whose nodes each have a score find NUM that has the minimal total sum of scores
one factor we have to consider is the possible combinations of the node set in t which we use for the generalization of the single attribute table
theoretically speaking when the partial thesaurus becomes deep and has few leaves the time complexity will become worse but this is hardly the situation
ih here are also example s of woms which could ha v dilteren granmm l ical
we oul line the design of an integrated system t o sul port the reading of french text by ul ctl speakers
the prototype w ts sul ticiently advanced in li ehruary ror n mgen communications stndellts to conduct an in vestigatiw user study
wheu the user selects word in a text for example NUM rit in he sentence ira
knowledge usa me for its solution
for instance template NUM constraints allow us to model how an expert translator is biased by the appearance of a word somewhere in the three words following the word being translated
input feature functions fl f2 fn empirical distribution x y output optimal parameter values i optimal model p
in addition candide performs during a preprocessing stage a reordering step that shuffles the words in the input french sentence into an order more closely resembling english word order
but both of these models offend our sensibilities knowing only that the expert always chose from among these five french phrases how can we justify either of these probability distributions
we take the probabilities p nle and p yr e as the fundamental parameters of the model and parametrize the distortion probability in terms of simpler distributions
holding a fixed and adjusting a to maximize the log likelihood involves a search over the darkened line rather than a search over the entire space of a a
in particular if in the empirical sample the presence of several led to a greater probability for pendant this will be reflected in a maximum entropy model incorporating this constraint
similarly in the second example the incorrect rendering of ii as he might have been avoided had the translation model used the fact that the word following it is appears
an elementary tree in c has only non terminals as internal nodes but may have both terminals and non terminals on its frontier
the rest of the proof is very similar to that in section NUM therefore the decision problem mpp is np complete
for any parse there are at most NUM tm second type derivations e.g. the sentence t t
the probabilities of the elementary trees that have the same root non terminal sum up to NUM
however NUM must fulfill some requirements for our reduction to be acceptable NUM
the authors would also thank the comments and insight of two anonymous reviewers which help improve the final draft
the alternating procedure provides a self organized way for the segmenter to detect automatically unseen words and correct segmentation errors
then we build an lm based on the second set and use the lm to segment again the first corpus
native speakers to segment manually NUM sentences picked randomly from the test set and compare them with segmentations by machine
the limited morphology includes some ending morphemes to represent tenses of verbs and this is another source of disagreement
special thanks go to dr martin franz for providing continuous help in using the ibm language model tools
the merit of the alternating procedure is probably its ability to detect unseen words
the resulting word based lm has a perplexity of NUM for a general chinese corpus
the major disagreement for human subjects comes from compound words phrases and suffices
however the new unsegmented corpus is a good source of automatically discovered words
suppose we wish to model an expert translator s decisions concerning the proper french rendering of the english word in
the length of b is limited to less than or equal to NUM in NUM NUM NUM NUM the same condition on b is used
for every word in wl a search for its collocational pattern is conducted and the results are stored in tile evidence data base edb
c is recorded as evidence of a straighttorward adjectival moditier nlodifiee relationship between k
as mentioned in the previous section this solution contains an over segmentation error which is the most likely error in the situation when unregistered words appear
the second line indicates the accumulated number of samples for which the col rect dependency structure was given as one of the minimum cost solutions
therefore the fact that reddito di persona is correct can not be captured even when comparing the generalized patterns reddito di human entity and imposta di human entity
on the other side the exportability of disambiguation cues acquired from a given noise free domain e.g. the wall street journal to other domains is not obvious
in the following we show examples of collision sets extracted from the ld an english word by word translation is provided for the sentence fragments that generated a collision set
let cs lcb el e2 en rcb be any collision set the test set and ncases be the number of test cases
when more than one esl remain in a collision set the system is not forced to decide and a further disambiguation step is attempted later
first the type of syntactic ambiguity phenomena occurring in real domains are much more complex than the standard v n pp patterns analyzed in literature
we show that our method achieves a considerable compression of noise preserving only those ambiguous patterns for which shallow techniques do not allow reliable decisions
the fundamental assumption of most common statistical analyses is that the events being analyzed productive word pairs or triples in our case are independent
obviously if that document is to yield information that default language must be one of the languages for which tipster modules and components exist
for predicting the lexical priors for the much larger mass of very low frequency types most of which would not occur in any such corpus the results we have presented suggest that one should concentrate on tagging a good representative sample of the hapaxes rather than extensively tagging words of all frequency ranges
for all ten runs the hapax based mle is clearly a far better predictor than the overall mle NUM
but language use is different fl om most its domains in a way that invalidates scw
such a capacity may well imply a grammar in the brain but not awareness and articulation of it
for each such vp the head verb first head noun preposition and second head noun were extracted along with the attachment decision NUM for noun attachment NUM for verb
in a typical domain a student shows progress by success in explicit stepwise reasoning to a solution
i will call it scw since it is simple clear and wrong
for example the visual fidelity of a photo or video exceeds that of simple graphics or animation
two aspects of fidelity that may rightly concern language educators are cultural authenticity and the situational continuity of a conversation
anyone familiar with ni p knowledge bases could hardly think seriously about promoting their direct use in explanations to learners
interactiveness also is difficult to achieve with video since there is a finite amount of video material produced in advance
diphone boundaries determination as the concatenation point of the diphones corresponds to the center of the phone it is somewhere in the steady region of the phone
thus input text is gradually transformed into its spoken equivalent null graphemc to phoneme transcription first abbreviations are expanded to t ornl equivalent full words using a special list of lexical entries
NUM spoken sentences were extracted fi om the slovenian speech corpus gopoi is l obrigek96 concerning airflight timetable inquiries in total duration of NUM minutes
as acquisition of a labeled diphone inventory or adaptation of a speech synthesis system to synthesise further voices is manually intensive an automatic procedure is required
after the recording phase logatoms were handsegmented and tile center of tile transition between tile phones was marked using information from both temporal and spectral representation of tile speech sip nal
here we describe the acquisition of an appropriate diphone inventory in a first version of our slovenian tts system which is supposed to serve as a reference system for future improvements
note incidentally ha t for reasons of computationa NUM elficieney core extraction does not apply to core patterns but to attested p ttterns only
if check decides yes then the proposed constituent takes its place in the forest as an actual constituent on which build does its work
training and evaluation data was prepared from the penn treebank
otherwise the constituent is not finished and build processes the next tree in the forest tn NUM check always answers no if the
the parser consists of the following three conceptually distinct parts NUM h set of procedures that use certain actions to incrementally construct parse trees
also the search heuristic is very simple and its observed running time on a test sentence is linear with respect to the sentence length
the important difference is that while a shift reduce parser creates a constituent in one step reduce a the procedures build and check create it over several steps in smaller increments
now we show that mppwg and mps are in np
if one solution is successful then the answer is yes
the rest of the proof follows directly from section NUM
this redefinition can be applied by off line computation and normalization
this is not the case for strong generative capacity i.e.
any possible assignment is represented by one sentence in wg
both this sentence and its corresponding parse have probability q
we conclude that mppwg and mps are both np hard problems
f to each non terminal ui and p resp
reduction the reduction constructs an stsg and a word graph
contextual predicates which look at head words or especially pairs of head words may not be reliable predictors for the procedure actions due to their sparseness in the training sample
classifier uwm using the loglinear model for unknown words
a definition of r along these lines is useful in a reusability scenario where an existing lfg grammar is augmented with the qlf contextual resolution component
proposition tim detinition specilies f structures that are omph e coherent and consistent NUM NUM how to qlf an f structure
contextual resolution monotonically adds to this description e.g. by placing fl n ther constraints on the meanings of certain expressions like pronouns or quantitier scope
the dciinition of w f s uses graphical rel resen lcb ations of t struct ure s
non recursive f structures are mapped to qlf terms and recursive f structm es to qi f forms by metals of a two place flmction r detined below
the proof is by induction on the complexity of NUM the correctness result carries over to the direct interpretation since what is eliminated is t
a child has a stock of objects that he can put on a checker board permute move or stow away
he gives orders to the system using natural language sentences and he can see immediately on the board the effects the sentences have
taken into account in a parallel like way in an algorithm which runs either in parsing or in synthesis
we used a regular expression pattern matcher on the part of speech tagged text to extract noun groups and proper noun sequences
we distinguish three levels of granularity
so go back and is this number three i
for instance text to speech requires less detail than translation
it is quite natural to consider this as ambiguous
they are much more frequent and problematic in dialogues
hence some ambiguities may remain after extralinguistic disambiguation
in some cases analysis results produced by automatic
our final definition is now simple to state
d shall i wait here for the bus
jp analyzers were available in others not
the mism process manages the quantitative mis statistical data used to monitor and evaluate adept
these results suggest that hindle r ooth s scoring function worked well in the verb noun1 preposition noun2 case not only because it was an accurate estimator of lexical associations between individual verbs nouns and prepositions which determine pp attachment but also because it accurately predicted the general verb noun skew of prepositions
we apply the linguistic bias approach to feature set selection to the problem of relative pronoun disambiguation and show that the case based learning algorithm improves as relevant biasses are incorporated into the underlying instance representation
in addition we assume that the learning algorithm is embedded in a parser or larger nlp system and hence has access to all knowledge sources that are available to the nlp system
the main difference is that additional global context features are included in the case representation namely the parser includes one attribute value pair for every constituent in the clause that precedes the relative pronoun
to combine the two implementations of the recency bias we first relabel the attributes of a case using the right to left labeling and then initialize the weight vector using the recency weighting procedure described above
the results in the first column baseline are just the results from table NUM they indicate the performance of the baseline case representation with various levels of the subject accessibility bias
the general idea behind the representation of context is to include any information available to the parser that might be useful for inferring the part of speech and semantic features of the current word
by adopting the automated approach to feature set selection for cbl of linguistic knowledge the same underlying instance representation can in theory be used across many linguistic knowledge acquisition tasks
in figure NUM for example in congress receives the attribute ppl in the right to left labeling because it is a prepositional phrase one position to the left of who
as shown in table NUM the combined recency bias outperforms the right to left labeling despite the fact that the recency weighting tends to lower the accuracy of relative pronoun antecedent prediction when used alone
table NUM summary of linguistic bias results
it is not easy to tell out a keyword and a content word
for telling out the error rates from classification we made another experiment
when a foreign name is transliterated the selection of homophones is restrictive
choose that tree xi that has greatest probability ql xi the issue of efficiently computing the most probable parse for a given sentence has been thoroughly addressed in the literature
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
typically a creole object will be a wrapper around a pre existing le module or database a tagger or parser a lexicon or n gram index for example
it is also a development environment that provides aids for the construction testing and evaluation of le systems and particularly for the reuse of existing components in new systems
it is a graphical launchpad for le subsystems and provides various facilities for viewing and testing results and playing software lego with le components interactively assembling objects into different system configurations
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
by increasing the set of widely used and evaluated nlp components gate aims to increase the eonfiden e of le researchers in algorithinie reuse
developing the muc system upou which vie is based took approximately NUM person months one significant element of which was coping with the strict muc output specifications
although a number of projects have addressed the provision of reusable algorithmic resources or tools takeup of these resources has been relatively slow
in other words the maximum entropy model subject to the constraints c has the parametric form NUM p of NUM where the parameter values a can be determined by maximizing the dual function a
requirement NUM NUM for multi lingual capability is applicable for this section and persistent knowledge items should be designed to apply to multiple languages
generally persistent knowledge is that knowledge which is retained from one run to the next of an application s
squibs and discussions assessing agreement on classification tasks the kappa statistic
the architecture shall provide for the use of a common pattern library that can support various document processing tasks in different applications
the architecture shall provide for the use of a common predicate argument dictionary that can support various document processing tasks in different applications
different stop word lists shall be applicable to different parts of a document to allow for different usage meaning of the same word
in these cases the routing process shall coordinate its operations with the process that controls the indexing of documents for search and retrieval
some of these attributes such as date of information author or source may be used directly by detection to select documents
allowing tentative conclusions to be drawn
we would add two further caveats
for instance consider the following arguments for reliability
when there is total agreement k is one
all four approaches seem reasonable when taken at face value
they do not describe any restrictions on possible boundary sites
however the four measures of reliability bear no relationship to each other
the application will also need a user interface document that defines user commands screen layouts and the sequence of user operations
if matching surpasses the graph matching threshold perform integration maximal join operation and add the word in the concept cluster
incorporating the length of the translation into the score
we call this first set of source collocations c1
in particular we compare it to the obvious method of exhaustively generating and testing all possible groups of k words with k varying from NUM to some maximum length of the translation m
on the other hand low values for the ri s i.e. a low threshold td will result in the actual number of candidate translations being close to the upper bound
these systems also need access to bilingual phrases
first it constructs translations for multiword collocations
these are expressions that have evolved over time
the correct translations are again shown in bold
the words appear together in NUM english sentences
champollio n requires an aligned bilingual corpus as input
ili lcb parser can skil words in the inpu sentence in order to find a partial parse for a sentence which otherwise would not be parsable
we evaluated the performance of our two methods by comparing them to two non context based ones a baseline method of selecting a parse randomly and a statistical parse disambiguation method
moreover because this technique samples different parts of the search space in parallel it avoids to some extent the problem of selecting locally optimal solutions which are not globally optimal
the second focusing score the j ocusing score i roper assigns a score between NUM and t indicating tow far up the rightmost frontier the inference chain attaches
finally we evaluate our performance and demonstrate that tile use of discourse context improves performance on disambiguation tasks over a purely non context based approach in the absence of cumulative error
however if the same speaker has just made a suggestion then it is more likely that the speaker is requesting a response from the other speaker by posing a question
ht contrast if the previous speaker has just made a suggestion then it is more likely that the current speaker is responding with an accepting statement than posing a question
we introduce the possible time constrmnt to he ok whether the temporal constraints conflict with the dynamic calendar or the recorded dialogue late when the inference chains are built
each type of ambiguity is categorized by coml aring either difl erent slots in alternative ll l s or dilt erenl values in ambiguous ii2f slol s
as in the first version of the discourse processor the chosen i i t is attached to the plan tree and a speech act is assigned to it
these routines are mainly concerned with translating from prolog syntax into the description string syntax used by the clig grapher
but with these parameterisation layers we provide natural points where the system can be extended or modified by the user
an illustrative subset of the parameters and their possible va lues is given below simple lexicon lexicon with features
a standard approach to modularisation is to split a problem into independent black boxes e.g. a grammar a parser etc
initial reactions to demonstrations of the educational tool suggest that it has the potential to become a widely used educatioual aid
the user can try out various options for semantic construction by using a menu to set various parameters
alterxlatively the user can choose to fully process a node in which case all readings are simultaneously displayed
currently the poleng dictionary consists of about NUM lexemes which corresponds to about NUM inflected forms mainly from the domain of computer science
this improves effectiveness because of a single rather than double attempt to expand the same non terminal symbol to the given string of terminals
the below four rules define the cases where give is to be translated to either the work was funded by the german federal ministry of education science research and technology bmbf in the framework of the vebmobil project
the process of creating a polish to english electronic dictionary destined to be used in computerised text translation was performed out in the following steps i
assume that the lexicon contains entries like those in NUM in which the italicized arguments to the semantic predicates are variables
table NUM lists the k values for subjects judgements on sentence importance averaged over texts
as NUM shows if only one genitive is present its prenominal interpretation may be as agent or as patient
the treatment presented here provides th basis needed for a thorougt erosslinguistic analysis of temi oral and aspectual phelmmena
one major goal of pall gram is the development of broad coverage grammars which are also modular and easy to nm intain
under this approach languages now only differ with respct to the categorial realisation of the flmction by ease marked np or pp
as part of a eool erative project we present an innovative approach to auxiliaries and multiph genitive nps ill german
a grammar is viewed as a set of correspondences expressed in ternls of projections fl om one level of representation to nilother
two fundamental levels of representations within lfg are the c onstitutent structure and the f unctional structure
if this can be achieved the prol h m faced by machine translation mt could tm greatly reduced
for instance in german temporal and conditional adjuncts mw be realized as pps dominating an np headed by a deverbal noun
this means that the attributes used are effective only for texts of certain types
each run used a separately and randomly sampled set of evaluation data
this difficulty is more marked in japanese than in english since there are more syntactically ambiguous structures in japanese
however in order to ham dle unknown words we have introduced a slight modification in computing the relative frequencies as is described in the next section
the major contribution of this paper is that we present a more accurate method for estimating word frequencies in an unsegmented corpus even if it includes unknown words
the one exception is lcb country county rcb for which bayes scores somewhat below baseline
this complementarity leads directly to a hybrid method tribayes that gets the best of each
the statistical method recently proposed for calculating n gram of m bitrary n can be applied to the extraction of uninterrupted collocations
but it was not easy to identify and extract expressions of arbitrary lengths and high frequency of appearance from very large corpora
in order to realize these translation it is required to identify phrases of high frequency and patterns of expressions from the corpora
interrupted collocational substrings were extracted for every two substrings which had appeared NUM or more times in the source text NUM
but in either situation the m gram string and n gram string merely overlapped and therefore they are need to be extracted separately
the number of matched characters are registered in the field of a nmc number of matching character in the record i
second using the results of the first method it also proposes a method that can automatically extract and tabulate interrupted couocational substrings
NUM order of substring appearance in the case of extracting interrupted collocations the order of appearance of substrings should be considered
statistical method for n gram assume that the total number of characters in a source text corpus is n
textual clues can give information about the figures that bear the metaphor which are easy to spot
the previous works in the domain studied the semantic regularities only overlooking an obvious set of regularities
this representation is designed to help the choice of a semantic processing in terms of possible non literal meanings
for natural language classification and prediction tasks the aim is to estimate a conditional probability distribution p h e over the possible values of the hypothesis h where the evidence e consists of a number of linguistic features el e2
a statistical model can be used to perforin prediction in the following manner given the values of the explanatory variables what is the probability distribution for the response variable i.e. what are the probabilities for the different possible values of the response variable
the accuracy of the combination of the loglinear model for local features and the stochastic pos tagger for contextual features was evaluated empirically by comparing three methods of handling unknown words unigram using the prior probability distribution p t of the pos tags for rare words
i use a perl script to convert the syntactic structures in this parsed corpus into a list of logical forms that roughly indicate the predicate argument structure of each clause in the text NUM we can generate a summary by choosing a subset of this list of lfs
text acomputerized phone calls which do everything from selling magazine subscriptions to reminding people about meetings have become the telephone equivalent of junk mail 4many restatements in the texts involve the most frequent cb which may serve as an additional heuristic
note that the parser probably would not have to resolve all syntactic ambiguities in the the summarization task because we can preserve the same ambiguities in the summary or delete some of the problem phrases such as pps in the summary anyway
a restricted type of the elaboration relation between sentences can be restated in centering terms elaboration on the same topic the subject of the clause is a pronoun that refers to the subject of the previous clause a continue in centering
for example in the sample text see section NUM about a new electronic surveillance method being tried on prisoners that will allow them to be under house arrest wristband occurs just as frequently as surveillance supervision however surveillance supervision is a more frequent cb than wristband and this reflects the fact that it is a more central topic in the text
then as described in the following sections the segments which axe about the most frequent cb s in the text are selected for the summary and then the discourse relations of elaboration and restatement are used to further prune and select information for the summary
their semantic representations contain the propositions call computer prisoner and plugin prisoner after anaphora resolution and inferences such as that call computer prisoner is equivalent to make a computerized call to a former prisoner s home
these stop after t2 NUM merges when all states with the same outputs are merged i.e. when a bigram model is reached
by estimating the topology model merging groups words into categories since all words that can be emitted by the same state form a category
qk of same length the probability that the model running through the sequence of states and emitting the given outputs is i
generally there are o l NUM hypothetical merges to be tested for each merging step l is the length of the training corpus
the initial model must have two properties NUM it must be larger than the intended model and NUM it must be easy to construct
the bigram model assigns a log perplexity of NUM NUM the merged model with NUM states assigns a log perplexity of NUM NUM see table NUM
figure NUM model merging for a corpus s lcb ab ac abac rcb starting with the trivial model in a and ending
for a larger training corpus the optimal model should be closer in size to the bigram model or even larger than a bigram model
matrix a contains the transition probabilities of the hidden states matrix b contains the probability of occurrence of an observation symbol given the hidden state and vector 7r contains the initial probabilities of the hidden state
the domain of application is another factor that strongly influences conversion performance a general dictionary can omit the specialized words of specific domains e.g. legal engineering or medical terminology and vice versa
according to the physical meaning given to the hidden states and the observation symbols of the hmm used there can not be hidden states graphemes that do not produce an observable symbol phoneme
the rules for the segmentation of a phoneme string to a sequence of symbols conforming to the above condition are manually defined off line according to the procedure presented below in an informal algorithmic language figure NUM
to overcome the disadvantages of the above mentioned methods a novel statistical approach to the problem of ptgc which is based on hidden markov models hmm has been investigated and is presented in this paper
finally the model gave extremely good results with italian and spanish reaching more than NUM success for the second order model and up to two or three output candidates for known and unknown text experiments respectively
it must be noted that in this case about NUM of the input symbols unit phonemes have been replaced by erroneous ones but still the score of the first four positions remains above NUM
summarizing we have shown that we only need to keep the locally at any time t in NUM t e best paths as we go along the possible state sequences for every possible state
an implication of this property is that the system does not try to match the input utterance to the closest word by some measure of distance contained in the dictionary but rather tries to find its most probable spelling
with french there is a special problem which does not occur with other languages there exist many homophones that are distinguished only by the presence or absence of various mute letters at the ends of the words
its input is a specification of the desired output content a patient document written in tezt source language tsl see subsection NUM NUM its output consists of one or more spl expressions
d1 c1 condition domain it remove actee i1 modality must range d1 dmarker condition range ordering first
interviews with government personnel were also a source
as an illustration of the kind of advantage structural tag language models can offer we introduce nine oronyms word strings which when uttered can produce the same sound based upon the uttered sentence the boys eat the sandwiches
we decided to build an interpolated language model partly because it has been well studied and is familiar to the research community and partly because we can examine the lambda parameters directly to see if weight is indeed distributed across multiple class levels
we constructed nine hypothesized sentences each of which could have produced the phoneme string we presented these sentences as input to a high quality word based language model the weighted average language model and to another smoothed structural tag language model
thus in big house big will assigu a high value as the filler of the property slot size of the frame for the me ming of house
abusivele is then tile eventive sense of the adjective formed from abuse v1 NUM and abusive is 1a the agentive sense of the adjective in the same sense of abuse
most of file linguistic scholarslfip focuses on tile taxonomies of adjectives on file differences between the attrihutive and predicative syntactic usages as well as other syntaclic mmsformations associated with vari
our analysis of adjectives with the goal of supporting semantic analysis shows that the issues important for adjective meaning representation are quite different from those debated in literature on adjectives
for those modifiers whose memfings are possibly sets of property value pairs the method is to insert file values riley carry into file same property slot in the modified
in our approach the representation solution for good would be to introduce an evaluation attitude with a high value and scoped over this property
the latter also involves other static microtheories describing world kalowledge and syntax semantics mapping as well as dynamic microtheories comlected with the actual process of text analysis
mikrokosmos combines findings from a variety of quasi antonomous microtheories of language phenomena world knowledge organization and procedural knowledge at the level of coinputer system arclfitectnrc
in lhis pawr we have illustrated a method based on ontology and text memfing representation of lreating such discretxmcies in dependency for adjectiwd modification
in other words we represent the meaning of big house without specifying whether big pertains to the length width height or area of a house
in vision learning problems for example the brightest object in view may be a highly accessible object for the learning agent in aural tasks very loud or high pitched sounds may be highly accessible
NUM finally in the enhanced alpni t implementation the storage of almost NUM roots and hundreds of patterns it separate sul lcxicons saved memory space but the l etouring operation that interdigitatcd them in rcaltime was inherently inelficient building and then throwing away many superficially plausible sterns that were not sahctioned by the lexicon codings
because the system described above recognizes all possible written forms of a word with varying degrees of diacritical marking it also generates all the possible surface forms of a word which may be less than useful in many applications q yi ically a user wants to see only the fidly vowcled form during generation
an easier to build but less useful system would simply deal with complete stems rather than roots and patterns
finite state rules rules map the idealized strings into surface strings handling all kinds of epentheses deletions and assimilations
suitable prefixes and suffixes are also present in the lexicon transducer added in the normal concatenative ways
we hope to extend our tinite state techniques to cover ilebrew and ther languages with exotic morphology
fully voweled the surface string for this reading would be darasat a33
NUM because the lexical letter trccs in a traditional kimmo style system are dccoratcd with glosses features and other miscellaneous information on the leaves they are not purc finite state machines can not bc combined into a single fsm can not be composed with the rules and have to be storcd and run as separate data structures
to be interesting in our applications the arabic morphology system had to have the following qualities NUM
centre ville montreal quebec h3c 3j7 canada po iguera ere umontreal ca this session will be devoted to the problem of building language specific semantic tagsets
l at le NUM shows the top be abstra ted tril les with respect to dw ir ewdualiot scores ltetns in the talje shows subject syllsel vert syllsel oh j0el synsel sttrfa e supl orl fr qtleltcy tll oc ll re lc
from the example triple so v28 NUM n9 v12a v224 NUM NUM ns a a chase run after dog hound cat kitty the following features are extracted for three of the synsets contained in the above data
for example tversky NUM proposes a mode based on huntan similarity judgements where similarity is a linear combination of shared and distinct features where each f atm e is weighted NUM ased on its itnl ortatme l w rsky s experiment showed the highesl eorrelalion with hunmn subjects feelings when weighted shared and dislinet features are taken into consi leration
the method requires little human involvement and is very promising for the implementation of practical systems by achieving efficiency and accuracy at the same time
since we want the dissimilarity between two dis tributions divergence that is a symmetric version of diserimhiation is nlore appropriate for our case
indiscreet use of the component nouns lnay bring about the improvement of reall but can lead to the significant decrease of i recision
in the following discussions we dest ril e the details of the algorithm to select useful coinponent nouns from eoliipound houns
he divergence gives about tile itfforination uncertainty el the two dist ribu
in this paper we address the problem of compound noun indexing that is about segmenting or decomposing compound nouns into promising index terms
the correctness of the translation pairs are checked by a human inspector
the details of this process are as follows
or are such biases ok what evaluation metrics are appropriate for the wsd ta k
a rich hierarchical structure between the set of relations is essential to the graph matching operations we use for the integration phase
lit is made for yom g l eople learning the structure and the basic w cabulary of their language
in such cases there is little hope to acquire a higher statistical evidence of the correct attachment
our processing of the definitions results in the construction of a special type of conceptual graph which we call a temporary graph
trigger forward find the semantically significant words fi om the cckg and join their respective temporary graphs to the initial cckg
then a global data analysis is performed to review local choices and derive new statistical distributions
null in subsequent studies trigrams rather than bigrams were collected from corpora to derive disambiguation cues
supervised methods for ambiguity resolution learn in sterile environments in absence of syntactic noise
table NUM a general feedback algorithm for noise reduction table NUM disambiguation algorithm learning phase
given the concept hierarchy relation hierarchy and graph matching operations we now describe the two major steps required to integrate all the temporary graphs into a cckg
but now let s consider a collision set with two esl s that almost constantly occur together
in fact in the learning step NUM we observed decreased performance of precision and recall
another important feature of the internal structure of kb relates to the existence of paradigmatic structures nouns an l verbs based on their distribntion in kb patterns norms which are subject of the same verb exhibit likewise objects of the same verb a sort of semantic similarity the same ca n
as a l suit the ini reutial role of paradign extension is reduce wil h resl ecl t o exl erilnenl i where ex i en le pa ra dignis play a inore proniinelh role in support trig possible infel enees
the and iguities sl emming fl om this e dom are ubiquitous and represent a problem fbr a ny ni p system dealing with ira jan a promem to whose resolution a wide wwiety of fa ctors bol h linguistic i.e.
in this working session we focus on both the question how semantic tagging can support the development of nlp applications and the other way round how nlp systems can support semantic tagging
these biases are intended to express universal constraints about the domain of natural language phonology
for the shallow rules examined in this paper finding the correct alignment is trivial
computational linguistics volume NUM number NUM input pairs bat batter band
in particular the algorithm we chose is able to learn from only positive evidence
the algorithm is also successful in learning the composition of multiple rules applied in series
each arc has an input symbol and an output symbol separated by a colon
thus the underlying form for any morphological rule must be a word of the language
our transducer induction algorithm is not intended as a cognitive model of human phonological learning
for the second set of cxperhnents we designed identical interfaces for english and jai anese
it is therefore possible to use the system for the education of children with different levels of development attention or reading skills
we have constructed a document image database to compare our categorization approach with the conventional ocr based approach
our technique would be useful for an automated incoming fax sorting by the content
finally the system measures the degree of similarity between the document profile and a category profile
the category profile dj is also represented as a vector derived in the same manner from a collection
in addition a comparative evaluation between the word shape token based and the ocr based approach is needed
the number of documents available on the network is increasing with the development of the computational infrastructure
the defined character classes and the members for the ascii character set are shown in table NUM
we have developed a technique which automatically categorizes document images into pre defined classes based on their content
thus it should be used in unsupervised systems with no human interaction required
also its word recognition accuracy was affected by image quality NUM NUM
figure NUM NUM organizational structure of tipster ii se cm support
their meanings are provided for the reader s convenience
all management and technical personnel are required to be familiar with and comply with the provisions of this cm plan as well as being responsible for certain configuration activities in support of cm functions
however the erb is a non voting board
this critical area is the responsibility of configuration management
provide for independent submission and review of proposed changes
NUM using illico for a rehabilitation system what is the state of the art in the domain of aac for autistic persons
all trees were stripped of their semantic tags e.g. loc bnf etc coreference information e.g. NUM and quotation marks and for both training and testing
in each auxiliary tree the root and interior nodes are labeled by nonterminal symbols
in our experiments the analysis which has multiple minimum cost solutions was considered to have failed
furthermore the parser returns several scored parses for a sentence and this paper shows that a scheme to pick the best parse from the NUM highest scoring parses could yield a dramatically higher accuracy of NUM precision and recall
that is why is the fact that dans or was chosen by the expert translator NUM of the time any more important than countless other facts contained in the data
in this section we introduce a method for automatically selecting the features to be included in a maximum entropy model and then offer a series of refinements to ease the computational burden
for some noun de noun phrases the best english translation is nearly word for word conflit d intor t for example is almost always rendered as conflict of interest
for our purposes however a safe segmentation is dependent on the viterbi alignment a between the input french sentence f and its english translation e
a word in the translated sentence e3 is aligned to words y3 and y4 in two different segments of the input sentence
before imposing this constraint on the model during the iterative model growing process the log likelihood of the current model on the empirical sample was NUM NUM bits
their method is based on the estimation maximization em algorithm a well known iterative technique for maximum likelihood training of a model involving hidden statistics
it is easy to check that the dual function a of the previous section is in fact just the log likelihood for the exponential model p that is
furthermore the customized estimation framework of the bigram parser must use information that has been carefully selected for its value whereas the maximum entropy framework ro null treebank as a function of n evaluation ignores quotation marks
the parser itself requires very little human intervention since the information it uses to make parsing decisions is specified in a concise and simple manner and is combined in a fully automatic way under the maximum entropy framework
NUM NUM probabilistic classification approach using multinomial distribution
if the hst two characters are ed remove them
thus in ninny respects our grammar follows closely the anlt grammar
computational linguistics volume NUM number NUM with this encoding unification will never fail since every pair of types has a glb even if this is btm
hence by computing an array based on the inverse relation one can use exactly the same technique for computing least upper bounds or the generalization of two types
if we could then the problem could be solved either at the lowest vp level or at the point where the vp is incorporated into the s
the use of selectors to encode position also solves some cases of the problem that our first attempt suffered from of allowing more than the correct number of complements
we can distinguish a particular feature say cat as individuating different types and associate with each different value of the cat feature a set of other dependent features
in some cases the results of macro evaluation may need to be spliced into a category for example when the result is a set of feature specifications
while in principle the encoding below would extend to non atomic but still finite types in practice the resulting structures are likely to be unmanageably large
in each row we put a i if the row element dominates the column element i.e. column is a subtype of row and a NUM otherwise
these forms could surely be found with appropriate edit distance thresholds but at the cost of generating many words containing more substantial errors
error tolerant finite state recognition enables the recognition of strings that deviate mildly from any string in the regular set recognized by the underlying finite state recognizer
using the above lemmas an ltig corresponding to a cfg can be constructed
this number decreases very fast from the first to the last position for most of the languages which shows that the system does not produce extreme spellings of the input words even though these may be allowed by the language
we do the same for matrix a this means that if the indices i j k indicate a zero transition probability then the algorithm proceeds without trying to calculate the overall probability thus eliminating a floating point multiplication
the prototype system which was implemented on a NUM based personal computer responded at an average rate of one word per second for exp NUM second order hmm and about ten times faster for exp NUM first order model
last but not least the fact that the system is not rule based but uses an algorithm based on probabilities makes it possible to implement the system in hardware resulting in a system adaptable to any real time speech recognition system
since for every state we need to add two distances one from matrix a and one from matrix b we must be sure that there will be no overflow after all the additions that must be made for each word
the final version of the conversion system uses the previously mentioned methods i.e. the second order hmm and the n best version of the viterbi algorithm along with a transformation that is necessary to speed up the execution of the conversion
i i lcb 2qr lqrflq r ot t l lo since qt si the underlined part of NUM is independent from p qx t
in the latter case experimentation showed that no more than two solutions output candidates are necessary to produce the correct output with an accuracy higher than NUM for most of the languages the system was tested on
in a word level implementation the algorithm must find the hidden state sequence i.e. word in its orthographic form with the best score given the model and the observation sequence o i.e. word in its phonemic form
the only language specific part of the system i.e. the algorithm for the segmentation rule rentzepopoulos and kokkinakis phoneme to grapheme using hmm definition is straightforward and does not need any special linguistic knowledge but only familiarity with the target language to be processed
the output is stored as an initial value in a list called wordlist wl
by design the tree building procedures guarantee that lcb al an rcb is the only derivation for the parse t then the score of t is merely the product of the conditional probabilities of the individual actions in its derivation
finally section NUM summarized the paper
reliability refers to reproducibility or inter coder consistency of data which is measured by the kappa statistic a metric standardly used in the behavioral sciences
figure NUM shows a sample parse of a typical atis sentence
probability of a location pp following an arrival vphead within an arrival vp constituent
the implementation and integration of these elements is far less conventional
these frames are the source of candidate constraints to be inherited
transition probabilities are estimated from a training corpus of augmented trees
processing proceeds in three stages NUM word string w arrives at the parsing model
these parses together with their probability scores are passed to the semantic interpretation model
both pre discourse and post discourse meanings in our current system are represented using a simple frame representation
the system consists of three stages of processing parsing semantic interpretation and discourse
in assessing the performance of information extraction systems we are interested in knowing the classes of errors made and the circumstances in which they are made NUM
the missing permutation r s c is not permitted because it violates what has become known as the unbound variable constraint
this paper describes an algorithm for generating quantifiers in english sentences which describe small models containing collections of individuals which are inter related in various ways
the clfcclaveness of the filtering re l or scoring hcuri6dcs ca n bc tl l a stlr l ttsilt tvc ch scly re
f ach triple is assiglted a evaluation score which is a snt of m rnmlized surface sul l rl score sll l f3e
null note that we neidter claim nor require t hat the features eonq letely charaelerize their mcepts or lhat inh ritan e of feal m es is sound
any dependency function can be partitioned by choosing a arbitrary subset of the mappings it contains as its focus the remainder being its complement
a similar problem plagues pathlength based algorithms causing nodes in richly structured parts of the ontology to be consistently judged less similm to one another than nodes in shallower or hess complete parts of the ontology
most existing relation hase l similarity methods directly use l he relat iotl ol o ogy of the semantic network to derive similarity either by strategies like link counting f a la NUM or tim determination of the depth f otnmon al slra lions kolod net g9
if a sentence a dog chased a cat appears in the corpus features representing chase cat and dog chase may be attached to dog and cat respectively
in our exl erime tts we deriw d the feature sets i NUM a distj ilmtiona analysis of t large t ol l tin
iu the r llowing sizes of at stract ed triple sets in the 379k level NUM 150b level NUM and NUM ewq NUM respectively
an experiment using our new concept abstraction method which we all the fiat probability grouping method over NUM NUM surface triples shows that the abstraction level of NUM is a good basis for feature description
the example in figure NUM contains both types of aggregation where fight and week are the unbounded and bounded aggregators respectively
the nodes in the elementary trees are visited in a top down left to right manner fig NUM
the conjoin operation applies to copies of the same elementary tree when the lexical anchor is in the contraction set
but the savings comes at a price for any particular feature f we are probably underestimating its gain and there is a reasonable chance that we will select a feature f whose approximate gain al f was highest and pass over the feature f with maximal gain al f
a korean word is usually a smaller unit than an english word and a word phrase is larger than an english word
described in the parentheses on the right of each korean word are corresponding english meaning and syntactic functions of the word
distortion prol abilil y that in about how the words are reordered in the l rench output
bilingual knowledge acquisition from korean english parallel corpus using alignment method korean english alignment at word and phrase level
figure NUM show the aligned results of a parallel corpus that was originally paired in a sentence level
for example in figure NUM english word the is ct and korean word ku is kt
section NUM summarizes the results of ex1 erinlents an l conclusion is given in section NUM
lected the english words that accounts for the top NUM NUM of the translation probability density given a korean word
in the preemptive aligmnent the previous selection can be rematched with the better selection found by later decision
the algorithm must also compute the correct span of the string for the nodes that have been identified via a contraction
the mutual information values were ordered by rank
right context enabling the algorithm to scan the elementary tree while trying to recognize possible applications of the conjoin operation
for example a model to predict part of speech of a word on the basis of its morphological affix and its capitalization might a ssume independence between the two explanatory variables a s follows
in this way a loglinear model provides a way to estimate expected cell counts that depend not only on the main effects of the variables but also on the interactions between variables
and NUM NUM such cases in the evaluation pool
the first set of experiments concerns this pattern
but we did not discuss an important question of whether the kappa statistic serves as a general tool for distinguishing good from bad data for training a learning algorithm
figure NUM summary of results for three attachment sites
the x axis is the number of subjects from NUM to NUM the y axis from top to bottom corresponds to the potential boundary locations with prosodic phrase locations numbered as in figure NUM
our decision to use a commonsense notion of intention as the criterion is aimed at giving the subjects the freedom to choose their own segmentation criteria and to modify the criteria to fit the evolving discourse
these initial evaluations allow us to quantify the performance of existing hypotheses to compare the utility of three very different types of linguistic knowledge and to begin investigating the utility of combining knowledge sources
the second part of our study section NUM concerned the algorithmic identification of segment boundaries based on various combinations of three types of linguistic input referential noun phrases cue phrases and pauses
NUM cochran s test evaluates the null hypothesis that the sums of ls in the columns representing the total number of subjects assigning a boundary at the jth site tj are randomly distributed
our work is motivated by the hypothesis that natural language technologies can more sensibly interpret discourse and can generate more comprehensible discourse if they take advantage of this interplay between segmentation and linguistic devices
we will give a simple small pcfg with the following surprising property for every subtree in the training corpus headed by a the grammar will generate an isomorphic subderivation with probability NUM a
there are bk non trivial subtrees headed by b k and there is also the trivial case where the left node is simply b thus there are bk NUM different possibilities on the left branch
figure NUM training corpus tree for dop example
consider first the possibilities on the left branch
figure NUM sample stsg produced from dop model
there is one more metric we could consider
dop does do slightly better on most measures
table NUM transformations from n ary to binary branching structures
bod randomly split his corpus into test and training
the fact that any context free language can be generated by a tig follows from the fact that any cfg can be converted into a tig
the string corresponding to the left auxiliary tree must precede the node and the string corresponding to the right auxiliary tree must follow it
NUM wrapping adjunction yields context sensitive languages because two strings that are mutually constrained by being in the same auxiliary tree are wrapped around another string
the arguments in favor of distinguishing the two structures are numerous
these rules can be largely precompiled out of the computational linguistics volume NUM number NUM algorithm by noting that the following states are equivalent
the size of our grammar is now as follows NUM id rules and NUM metarules
the latter account makes use of other devices such as the feature cooccurrence restrictions and the
the data relative to cliticization and agreement have shaped the structure of the np in various ways
for examlflc if a machine translation system uses a statistical model for target language word choice our approach could improve word choice by selecting more topic related words
taking just the two coder case the amount of agreement we would expect coders to reach by chance depends on the number and relative proportions of the categories used by the coders
NUM la table que paul a faite the table that paul made il les a vus he saw them in quantified nps we examine two cases NUM
grammars the anlt grammar was used as a model and initial source of inspiration for the design and implementation of the grammar of l ench despite obvious differences between tile two languages
the resulting structure is sufficiently equivalent from a theoretical perspective i.e. the past participle and its complements do not form a constituent and it allowed us to implement the bulk of our grammar
the grammar development environment is a computer workbench for the development and evaluation of computational grammars of natural language which are described in a style close to that of gpsg
n2 pform nil h2 de des filles girls 6we have omitted the description of the internal structure of this g2 de
similarly any tig that does not make use of adjoining constraints can be easily converted into a tag that derives the same trees however adjoining constraints may have to be used in the tag
second there can not be any internal nodes in any elementary tree labeled ak because there were none after step NUM and all subsequent substitutions have been at nodes labeled ai where i k
let t be the set of right auxiliary trees created by marking the first nonempty frontier node of each element of t as a foot rather than for substitution
the tree t must be different from the other trees generated when creating t because t contains complete information about the trees it was created from
simultaneous left and right adjunction is not an instance of wrapping because tig does not allow there to be any constraints between the adjoinability of the trees in question
tig does not allow a left right auxiliary tree to be adjoined on any node that is on the spine of a right left auxiliary tree
first whenever considering a node b for prediction at position j it should only be predicted if its anchor is equal to the next input item aj l
an important feature of the parser in figure NUM is that the nth child of a node need not be unique and a subtree need not have only one parent
there are nine distinct configurations here
john sent out mary a letter
the term corresponding to the living bitstring will be
which will not unify with any of these categories
assume the feature is called stem
thus all other combinations are excluded
john sent a letter to mary
everything else works just as before
table NUM contextual information used by probability models all backed off contexts are used t only backed off contexts that include head word of current tree i.e. 0th tree are used
as modifiers satisfy bolh restrictions on the noun memfing and case marking parlicles they are bound to n2 objective case NUM mid n3 objective case NUM respectively
finally type NUM and type NUM are differentiated according to the result of binding betwccn the modifiers in the input senteuce and thevalency elements inthe valency pattern
if modifiers with a case marking parlicle are preferentially processed as described in seclion NUM then the modifier with ga binds to subjective case n1 which is wrong in this example
this judgment is performed by checking whether the semantic calegory of the noun included in the modifier with wa is associated with time or not
example NUM shows wa as a proxy for pre nominal case marking p ulicle no in the sentence zou wa ham ga nagai elcplumls have long trunks
according to ordinary sentence analysis the modifier hana ga is bound to the valency element ni which means that the other modifier zou wa is left unbound
thus when the speech recognizer finds a particular pronunciation in the speech input the list of rules which applied to produce it can simply be looked up in the tagged lexicon
however a problem with the geig scheme and with structural consistency is that both are still weak measures designed to avoid problems of parser treebank representational compatibility which lead to unintuitive numbers whose significance still depends heavily on details of the relationship between the representations compared e.g. between structure assigned by a grammar and that in a treebank
this adverb is never used by itself and always modifies conjunctive forms such as toki nara to and so on and selects or emphasizes the supposition reading of the conjunctive forms
m oao6she e s ect t suke help ta past node because seikou succeed shit a past
these forms do not have any modal morpheme but when they which appear at the end of the sentence and are followed by a period they can convey modality that is attitudes or intentions of the subject or speaker
in order to confirm the encapsulation power of japanese function words we analyzed the speech utterances of a male announcer and found the correlation between a particle s encapsulating power and the pause length inserted after the clause with a conjunctive particle
we present an application of this to error tolerant analysis of the agglutinative morphology of turkish words
there have been a number of approaches to error tolerant searching
results of its application to error tolerant morphological analysis and candidate generation in spelling correction were also presented
these results indicate that such recognition works very efficiently for candidate generation in spelling correction for many european languages english dutch french german and italian among others with very large word lists of root and inflected forms some containing well over NUM NUM forms generating all candidate solutions within NUM to NUM milliseconds with an edit distance of NUM on a sparcstation NUM NUM
analysis and spelling correction kemal oflazer bilkent university
error tolerant finite state recognition with applications to morphological analysis and spelling correction
we present experimental results for spelling correction for a number of languages
this paper presents the notion of error tolerant recognition with finite state recognizers along with results from some applications
it is particularly suitable when the pattern is small and the sequence to be searched is large
in the best of all worlds we might identify a model that is consistent with the mass distributions provided by all the pairwise probabilities
these records may be fed as input to a downstream system for which the ie system is only one of several sources of information
in our case we can not often expect to find two graphs that contain an identical subgral h with the exact same relations and concepts
sonic aspects which are specific to text linguistic should be considered
one popular solution is to maintain upper t best cases instead of just one as following where max t denotes the t th max candidate
the l enn treebank pos tagset that is composed of NUM tags and NUM korean tagset is used in the tagging
in the above methods only one english word in reb t xl to one or n lq ench words
NUM v fl t a given an aligmnent a between e and f brown ctal
the flexible measure is intended to capture the dependency between bilingual items that can occur in different units according to different ordering rules
consequently word to word or word to word phrase alignment t etwcen korean and l nglish will suf fee from trait mistnatch attd low accuracy
in this section we propose a korean to english aligmnent method that aligns in both word and phrase lewds at the same t ime
based upon the named entity task guidelines NUM the task was to locate and tag with sgml named entity expressions people organizations and locations time expressions time and date and numeric expressions percentage and money in spanish texts from agence france presse in japanese texts from kyodo newswire or in chinese texts from xinhua newswkel
two groups new mexico state university computing research lab nmsu crl and mitre corp elected to participate in all languages sra in spanish and japanese bbn in spanish with fincen and chinese and sri nec uuiversity of sheffield and nit data in japanese
for this reason research into reranking schemes appears to be a promising step towards the goal of improving parsing accuracy
this can be achieved only if the complement is allowed to be an n2 head of the higher np and not a p2 there is no justification for agreement features on a pp in french
this is a reflection of phenomena which are characteristic of french cliticization present in is ench but not in english and agreement which is more limited in english titan in l y eneh
these involve a quantifier adverb pronoun collective or adjective whose complement contains respectively a determiner less np with de de filles or an np with a definite determiner de ces filles
NUM d il voit h s filles he sees gms il en voit he of theln sees il en voit des filles he of them sees girls in all structures described above we postulate an n2 de which may be cliticized
our grammar accounts for the absence of an explicit de in certain contexts i.e. trois filles as well as its presence in extraposed contexts fen ai trois de fllles and its contraction with the determiner des to produce simply de de filles r gle de caeophonie
although not all rules are compatible with each other in particular no direct object extraction rule can apply to the result of the passive rule the combinatorics are complex in a test grammar we found that the number of phrase structure rules corresponding to this id rule was of the order of NUM la or over NUM
their method for showing this is complex and of no concern to us here since all it tells us is that it is safe to assume that the coders were not coding randomly reassuring but no guarantee computational linguistics volume NUM number NUM of reliability
nonetheless this style of measure does have some advantages over measures NUM NUM and NUM since these measures produce artificially high agreement figures when one category of a set predominates as is the case with boundary judgments
for chinese although we had available a word segmentcr we had neither part o6speech tagger nor word lists nor even the elementary reading skills we had for japanese
the extent to which a candidate rule is rewarded for its specificity and penalized for its over generalization can have a strong effect on the final performance of the rule sequences discovered
in the part of speech application initial labeling is provided by lexicon lookup lexemes are initially tagged with the most common part of speech assigned to them in a training corpus
the rule sequence comprises i45 named entity rules iz rules for expressions of money and percentiles and 6i rules for geographical complements as in hyundai of canada
this generate and test cycle is contimmd until a stopping criterion is reached which is usually taken as the point where performance improvement falls below a threshold or ceases altogether
the proof proceeds inductivcly by constructing a finite state machinc bt that accepts exactly those strings which receive a certain label in the phrase finding process under a given rule sequence z
a rule also contains at least one action clause either a clause that sets the label of the phrase or one that modifies the boundaries of the phrase
a tree transducer constructed by this process is shown in figure NUM for comparison with the unaligned version in figure NUM
table z summariz es the contributions of these three error measures towards learning rule sequences for the muc NUM named entities task for task details see below
there are two substantively different kinds of rules to acquire rules that only change the label of a phrase and those that change the boundary of a phrase
there are still a lot of grammatical syntactic and semantic problems to be solved
this makes it possible to block backtracking in the rule d a e
the method of filters consists in checking negative rules first
the alphabet of the automaton is the set of identifiers of the words stored in the dictionary of single words
types and admissible orders of modifiers of a given verb are listed in the dictionary
the semantic features encode selectional restrictions most of which are domain independent
sixteen subjects for the experiment were recruited from undergraduate computer science courses
figure NUM length of solution by task
in addition there is little support for clarification and correction subdialogues
the predicate in a type NUM case has to cover both subjective and objective cases in its usage
that is sentence analysis camlot be completed as shown in the left bottom of figure NUM
notice that the binding process resolves what case marking parlicle the adverbial particle wa stands in for
here modifiers with wa and ga are often bound in type NUM
morphological analysis segments the input japanese sentence into its component words such as predicates and nouns
this paper has proposed a method for analyzing the japanese double subject construction that includes an adjective predicate
in the below the words surrounded by NUM are semantic categories
the valency structure represents what surface cases the modifiers dominated by a given predicate correspond to
therefore there are intrinsic limitations to the possibility of using purely statistical approaches to ambiguity resolution
a similar argument holds for the derived and underived adjectives
what should be the relationship between lexicai meanings of the language and semantic tags
the merits of this move will t e discussed shortly l br the imrpose of representing the intbrmation at out verbal colnt lements the fl ature vcomp is introduced
NUM no explicit word boundaries in east asian languages there are no explicit word delimiters corresponding to spaces in english in a sentence
accessing and using such a huge number of on line documents require intelligent document processing such content based search categorization information extraction and machine translation
an encoding sdtema maps each element of a character set into a sequence of number s that is used in communication networks
the class name of a word longer than n characters is the concatenation of x and the last n characters of the word
our system uses a unigram model rcb or both western european languages and east asian languages but the models for western european languages and the models for the east asian languages have different unigram units
eng deu ita eng english deu german ita italian in this table english eng is the most plausible language for european part with sufficient score
the problem tackled by them is similar to ours in the sense that the input is not a unique character string but a string that potentially corresponds to several different character strings
for example servers in western europe normally use so NUM NUM iso latin NUM whereas most unix servers in japan encode text using japanese euc extended unix code
the lex feature in the entry for werden ensures that a matrix verb is combined with its verbal complement before the verbal complement is saturated by one of its complements
through tcl wrappers and filters that interface the component with the temple representation stored as annotations in the tipster document manager
the user can select the other wanted tag push the search button and accordingly get the right dictionary entry and col pore examples on the sceen
in the second split the training set contained NUM messages giving rise to NUM coreference sets and the test set contained NUM messages giving rise to NUM coreference sets
more consistent and less ambiguous use of terms and phrases
rules on higher level than spelling and vocabulary have been hard to enforce
is the distortion of human language a phenomenon of a passing phase
damping of innovation humor and irony
more so as machines get better and more centrally placed in the community
null which are the long time effects of exposure to machinese or machinese like language
empirical evidence is scanty of course
large scale supervision has not been feasible
even al ar ron this l eople occasio a lly creat e new words
the procedures are applied in three left to right passes over the input sentence the first pass applies tag the second pass applies chunk and the third pass applies build and check
although this disk had been part of the general training data there were no relevance judgments for topics NUM NUM made on this disk of documents
the use of the interactive query construction method in trec NUM demonstrated interest in using interactive search techniques so a formal track was formed for trec NUM
the confusion track represents an extension of the current tasks to deal with corrupted data such as would come from ocr or speech input
for this automatic run expansion was done by selecting NUM terms from the top NUM subdocuments in addition to the terms in the original topic
the improvement from brkly6 to brkly7 is a NUM gain in average precision with NUM topics having superior performance in the manually expanded run
this huge variation seemed to have little effect on results largely because each group found the level of topic expansion appropriate for their retrieval techniques
for the trec NUM completeness tests a median of NUM new relevant documents per topic was found NUM increase in total relevant documents
when two sets of results were sent they could be made using different methods of creating queries or different methods of searching these queries
this shows an improvement of NUM for their expansion run crnlea over the trec NUM system and this is likely to be typical for many of the systerns this year
the construction of the corresponding base dataset is performed by pulling documents out of a number of sources such as news wires newspapers magazines and legal databases
all NUM NUM million words of parsed text in the brown corpus and NUM NUM million words of parsed wsj articles were used
a representation of an utterance a is hierarchically recent for a representation of an utterance b if a is adjacent to b in the tree structure of the discourse
if something has been recently discussed it was recently in the cache and thus it is more likely to still be in the cache than other items
this will require some processing effort yielding the prediction that there will be a short period of time in which the cache does not have the necessary information
thus the numbers suggest that in about half the cases we could expect the pronoun to function as an adequate retrieval cue based on gender and number alone
when an intention is completed it is not necessary to strategically retain in the cache information relevant to the completed segment
when the pursuit of an intention is momentarily interrupted as in dialogue a the conversants attempt to retain the relevant material in the cache during the interruption
in the meantime the notion of increased processing effort in the cache model explains the occurrence of a class of informationally redundant utterances in discourse as well
here we can assume that the word forms of the on are in a thesaurus we call this thesaurus the original thesaurus and we can extract the relevant part as in figure NUM we call this tree a partial thesaurus t NUM if we replace taro and hanako lwe are going to mainly use the terms attribute value and class for generality
total word count in thesaurus thesaurus node corresponding to p word count under p set of class under p class frequency of i set of class f c entropy of class distri mtion in a rile node set that satisfy the two definitions is called the unique and complete cow r ucc node set and each such node set is denoted by p
the following types of pairs are considered NUM a head noun and its left adjective or noun adjunct NUM a head noun and the head of its right adjunct NUM the main verb of a clause and the head of its object phrase and NUM the head of the subject phrase and the main verb
we suggest with some caution until more experiments are run that natural language processing can be very effective in creating appropriate search queries out of user s initial specifications which can be frequently imprecise or vague
two types of retrieval have been done NUM new topics NUM NUM were run in the ad hoc mode against the disk NUM NUM database l and NUM topics NUM NUM NUM actually only NUM topics were used in evaluation since relevance judgements were unavailable for topic NUM due to an error
run base xbase nyuge i nyuge2 base statistical terms only no expansion NUM xbase massive query expansion no phrases NUM nyugel phrases names with massive expansion up to NUM terms NUM nyuge2 expansion limited to NUM terms per query
it is important that all names recognized in text including those made up of multiple words e.g. south africa or social security are represented as tokens and not broken into single words e.g. south and africa which may turn out to be different names altogether by themselves
this situation may arise for several reasons NUM the term concept is not there NUM the concept is there but our system is unable to identify it or NUM the concept is not explicitly there but its presence can be infered using general or domain specific knowledge
run abase aloe mbase mloc iloc statistical terms only NUM aloc automatic phrases and names locality n NUM NUM mbase queries manually expanded no phrases NUM mloc manual phrases locality n NUM NUM iloc interactive phrases locality n NUM
this has been usually accomplished using statistical methods often coupled with manual encoding that a select terms words phrases and other units from documents that are deemed to best represent their content and b create an inverted index file or files that provide an easy access to documents containing these terms
this is a problem because a it is a standard practice to include both simple and compound terms in document representation and b term associations have thus far been computed primarily at word level including fixed phrases and therefore care must be taken when such associations are used in term matching
our information retrieval system consists of a traditional statistical backbone nist s prise system NUM augmented with various natural language processing components that assist the system in database processing stemming indexing word and phrase clustering selectional restrictions and translate a user s information request into an effective query
the description of lexical predicates within the framework of frame semantics provides a natural method for selecting and structuring appropriate tagsets
zin the context of the framenet project the question of how much text will be tagged is a practical one
a part of a generated decision tree is given in figure NUM
nametag overview nametag consists of a software engine that applies name recognition rules to text supported by lexical resources and limited lists of proper names
additional portions of the developing architecture will be evaluated for the appropriateness of insertion into the demonstration for example the document manager or text annotator
with respect to the jtf reference architecture sra s core data extraction product nametag will be integrated into the generic server layer with a customized data extraction prototype integrated into the application layer
this represents the primary focus of un and links the current sentence with the previous discourse
issues of multilinguality are treated by ustomizing the selection rules according to the output language
when elaborating the description of an object the focus of attention moves onto the objecl itself
c they i should use the enclosed envelope k
a computational model for generating referring expressions in a multilingual application domain
section NUM details the solutions implemented in the gist system
these relations may be grammaticmly lexically or graphically signaled
was generated from a list ordered by frequency giving the term s value for each word
the centering model was first conceived for english a language where pronouns are always made explicit
expressions for automatically generated multilingum english german italian instructions in the pension domain
is there an assignment of values true or false to the boolean variables such that the given formula is true
the reduction from the 3sat instance ins to an mppwg problem must construct an stsg and a word graph in deterministic polynomialtime
the probability p imd is defined as pt tl x x pt t
a target occurrence is put in the latter iff all words in the confusion set would have the same tag when substituted into the target sentence
we will use two parameters to evaluate system performance system accuracy when tested on correct usages of words and system accuracy on incorrect usages
table NUM performance of the hybrid method tribayes tb as compared with trigrams t and bayes b
bayes on the other hand learns features that allow it to discriminate among the particular words at issue regardless of their part of speech
the bayes component of the hybrid will therefore be trained on a subset of the examples that would be used for training the stand alone version of bayes
in other words the decision reduces to which of the two words wk and w is the more common representative of part of speech class t NUM
for this case an alternative feature based method called bayes performs better but bayes is less effective than trigrams when the distinction among words depends on syntactic constraints
to run microsoft word on a particular test set we started by disabling error checking for all error types except those needed for the confusion set at issue
the opposite behavior always suggesting a different word would result in scores of NUM and NUM for a confusion set of size NUM
the study and the first results we have here presented cover from lexical semantics to discourse structures with strong interactions between these two ends
we formalize with NUM axioms in a non monotonic first order logic the behavior of all possible kinds of verb preposition association for the french language
since words in a text are not random we know that our corpora are not randomly generated
what we need are tile meaning postulates that spell out the consequences of saying that a time and an event type are in the relationship specified by some aspect marker
it seemed better to use the space available to explore a small number of cases in some detail than to cover a wider range without being convincing about any particular case
the claim that a dmory is omt ositional however lacks bite if lexical and pre lexical items are allowed to mean dilrerent things ill difl erent contexts
one way to account for this is to argue that the simple past deals with the end point of the event whereas the perfective deals with the end of some related interval
the perfective is also open to the same ol tions NUM i had read the times for years but had gradually come to reeognise it as a capitalist rag
stm l anti end points of an instantaneous veltt are identi al depending on whether we regar l the time line as d mse
as such the present participle can not be taken as an indication that the cuhnination of my living in bray has not been reached since there is no such cuhnination to reach
somehow NUM conveys the message that mary habitually eats a peach for her lunch note in particular that it is not the same peach or the same lunch every day
each component of the report is allowed to make a very weak contribution and then the interactions between these contributions construct a larger and more subtle set of conclusions
there is also n dting to enable us to infer that there is no more ttmn one i will return to this below
here are some examples of sentences corresponding to different levels of difficulty permute the gray circle with the black triangle
this software fully uses the natural language processing techniques provided by illico and in particular the principles of modularity and guided composition
this checking is simultaneously done at all the levels of well formedness the constraints defined at the different levels are corouo tined i.e.
these systems use boards and picture communication symbols to compose picture sentences some of them use a set of predefined sentences
adaptation testing as well as documents from sample files are processed in the adaptation collections
the problem queue is a visual representation of all problem document information contained in the database
output manager om the om manages the output of successfully tagged documents for adept
after a successful evaluation oir will have the option to transition adept to their production environment
documents in the production category will run at a higher priority than those in the adaptation category
document input di the di process is the interface between adept and the rose feed servers
over an average month adept will operate seven days per week processing and expected NUM NUM documents
problem queue manager pqm the pqm is responsible for managing the problem queue of adept
a mapping template contains the directions on how to parse a specific data source
it is divided into three single characters by our word segmentation system
the name part of an organization can not extend beyond a transitive verb
part of speech is useful to determine the left boundary of an organization
c ambiguity some organization names with keywords are still mnbiguous
b no restriction on the length of a transliterated personal name
the other errors include place names organization names and so on
they are identified as proper nouns correctly but are assigned wrong features
if there are several left neighbors the one giving the highest probability is used
although the method described here does not handle erroneous cases where omission of space characters causes joining of otherwise correct forms such as inspite of such cases may be handled by augmenting the final state s of the recognizers with a transition for space characters and ignoring all but one of such space characters in the edit distance computation
match tagged nodes to concept grammar rules xp police xp better train xp safety
the category means basically follow expectations
using sentence connectors for evaluating mt output
the conjuncts 2a subclass of the adverbs el
table results of the first experiment
we decided that experiments needed to establish three qualities of this system
tal le NUM combined experiment results
the results are given in table NUM
but first we conducted a preliminary experiment
the prototype keeps a very detailed log of what the evaluator does exactly
the list of candidate names extracted from the mr jordan of steptoe johnson each candidate name is examined for the presence of conjunctions prepositions or possessive s
contradictory examples abound gates of microsoft and gerstner of ibm suggests stronger scope of and over o k the department of german languages and literature suggests the opposite
they take as their input text processed by nominator and further disambiguate untyped names appearing in certain contexts such as an appositive e.g. president of citibank corp
this efficient processing has been achieved at the cost of limiting the extent to which the program can understand the text being analyzed and resolve potential ambiguity
but even if an existing database is reliable names that are not yet in it must be discovered and information in the database must be over ridden when appropriate
the aba has steadfastly reserved the title of partner and partnership perks which include getting a stake of the firm s profit for those with law degrees
to illustrate new york s moma and the victoria and albert museum in london is first evaluated for splitting on in
when all the names have been collected and split names containing sentence initial words are compared to other names on the list
if the preceding word is an adverb a pronoun a verb or a preposition it can safely be discarded
in another sense however development of a module like nominator still requires considerable human effort to discover reliable heuristics particularly when only minimal information is used
connectionist approaches are able to learn the structure inherent in the input data to make fine distinctions between input patterns in the presence of noise and to integrate difl erent information sources
while kankei combines the statistics of multiple patterns to make a disambiguation decision collins and brooks model is a backed off model that uses NUM gram statistics where possible NUM gram statistics where possible if no NUM gram statistics are available and bigram statistics most items in this specification are optional
as a first attempt when the trains parser tries to extend the arcs associated with the rules vp vp pp adv and np np pp adv kankei will adjust the probabilities of these arcs based on attachment statistics
we distinguish between a set of explanatory variames
moreover since they assume a one to one mapping between syntactic structure and intonational features they can not account for those phenomena frequent to our domain where the same syntactic structure can be realized with differing intonations see example above
when looking at dialogue rather than monologue other factors coming into play are the history off the dialogue taking place and the expectations on the part off the hearer that are evoked at particular stages in the course of the dialogue
treatment of intonation in sfl the three distinct kinds of phonological categories i.e. tone group tonic syllable and tone contribute to the intonation a NUM NUM NUM NUM consider intonation part of phonology
komet grammar in this section we describe the syste m networks that have been introduced to the german grammar of the komet penman text generation component as to include specifications of appropriate intonation selections in its output NUM
we have taken two existing systems the cor dialogue model NUM and the k omet penman multilingual text generator NUM to build the backbone of an integrated dialogue based interface to an information system
this fact is acknowledged by NUM whose synphonics system is based on the assumption that prosodic featthe synphonics system i covers the incremen null tures have a function independent of syntax
from these systems only the choices in the tone systems realize an interpersonal function NUM that of indicating a speech function or the speaker s attitude e.g. NUM this interpersonal function is our present concern
if however the results had so far been presented at a different position on the screen the system would generate tone lb in order to place special emphasis on the statement lb die ergebmsse sina unten dargestellt
prior to tipster most analysts faced with fmding essential information from large volumes of data used search systems based on boolean keywords
within these two phases there would be separate focus on detection retrieval and routing and on extraction understanding
information extraction is a technology in which pre specified types of information are located within free text extracted and placed within a database
the techniques typically only worked for english and were difficult to port to new domains or even to extend within the current domains
initially two phases were planned two years of research and development into advanced algorithms followed by two years of development of prototype demonstration systems
i will focus on one aspect of information structuring namely the verbalization of the current mental representation of entities
hence a precise computation of the informational status is a crucial subprocess within the whole process of utterance production
information structuring creates conditions for producing the most felicitous utterance within the set of all possible utterances
the successful concepts of phase i were continued with close cooperation among the government agencies and the contractors regular workshops and corpora for development and testing
a twotiered program of NUM continued algorithm development and NUM transfer of technology into demonstration projects was defined
using the probabilities from table NUM s the probability assigned to a b d c would therefore be
the contributions of the pairwise models are conditioned not on the existence of other we use the notation c to indicate coreference
the value a is called the conflict between the mass distributions being combined it provides a measure of the degree of disagreement between them
our first task is to derive a model for determining the probability that two templates corefer conditioned on various characteristics of the context
the percentage for the whole training corpus was p NUM and in the third training set p2 NUM
we present the results of initial experiments with several approaches to estimating such distributions in an application using sri s fastus information extraction system
a system that assigns a probability of NUM NUM to correct answers is more successful than one that assigns a probability of NUM NUM to them
we can therefore use dempster s rule to resolve the conflict between the pairwise probability distributions to generate a distribution over the coreference configurations
the pros and cons of statistics and online dictionaries are discussed below
in the process of contraction over nodes in elementary trees it is the operation on that node either substitution or adjunction that is identified
the contents of the contraction set of a tree can be inferred from the contents of the set in the second projection of the elementary tree
we showed that tixed constituen y can be maintained at the level of the elementary tree while accounting for cases of non constituent coordination and gapping
for example the tree in fig NUM is now represented as eat lcb NUM NUM NUM NUM NUM
it builds the derivation by visiting the appropriate des during its tree traversal it the following order see fig NUM
such a case occurs e.g. two auxiliary trees with substitution nodes at the same tree address are coordinated with only the substitution nodes in the contraction set
a fimctional type was given to sequences of lexical items in trees even when they were not contiguous i.e. discontinuous constituents were also assigned types
in this paper we show that an account for coordination can be constructed using the derivation structures in a lexicalized tree adjoining grammar ltag
we present a notion of derivation in ltags that preserves the notion of fixed constituency in the ltag lexicon while providing the flexibility needed for coordination phenomena
template object is a group of associated slots
one or more linked objects form a template
this may be considered a far term requirement
this is accomplished through the use of dtds
the analytical methods and tools representative of the techniques to be applied to this functional area are given in appendix b however this list should not be considered either exhaustive or restrictive
these include creating a free text selection statement a shorter free text need statement example document or keyword boolean statements with negative operators to specify the desired documents or sub sets of documents
NUM NUM relationships of major functional areas in addition to the relationships identified in paragraph NUM NUM document management detection and extraction accept inputs from user information request and provide outputs to user information output
the architecture shall provide for the development of common shared logical modules components and interfaces that can be configured to support various text document processing tasks in different applications and natural languages
this variation could be achieved by interpreting p a as the proportion of times that the naive coders agree with the expert and p e as the proportion of times we would expect the naive coders to agree with the expert by chance
for instance he claims that finding associations between two variables that both rely on coding schemes with k k NUM is often impossible and says that content analysis researchers generally think of k NUM as good reliability with NUM k NUM
measure NUM falls foul of the same basic problem with chance agreement as measures NUM and NUM but in addition the statistic itself guarantees at least NUM agreement by only pairing off coders against the majority opinion
kid make no comment about the meaning of their figures other than to say that the amount of agreement they show is reasonable silverman et al simply point out that where figures are calculated over different numbers of categories they are not comparable
a parser is an example of a module
the lob brown differences cp nuot in general be interpreted as british american differences it is in the nature of language that any two collections of texts covering a range of registers and comprising say less than a thousand samples of over a thousand words each will show such differences
high scores correspond to heterogeneous corpora and dissimilar corpora the possible outcomes for various permutations of the scores for homogeneity of corpus NUM corpl homogeneity of corpus NUM corp2 and corpus dissimilarity dis are presented in table NUM
where NUM is the observed value e is the expected value calculated on the basis of the joint corpus and the sum is over the cells of the contingency table will be m distributed with rn NUM x n NUM degrees of freedom
the only difperences for the corpus similarity case are that NUM one subcorpus is taken from the first corpus and the other from the second and NUM the similarity value is to be interpreted by reference to the homogeneity measure for each corpus
there is no a pr ori reason to expect them to behave as if they had been sthe appropriate theoretical response as taken in the text encoding initiative is that texts are hierarchically structured so same text does not have a unique interpretation
biber s work see below shows how quantitative methods can be used to discover and capture register differences and some of the objects he counts are words others being gr mm ical constructions so his work provides some grounds for optimism
in the table where they make the comparison the x2 value for each word is given with the value marked NUM NUM or NUM if it exceeds the critical value of the statistic at any of three different significant levels so one might infer that the lob brown diiterence was non random
in a series of papers hinrichs and nakazawa argued for a special rule schema that combines the verbs of a so called verbal complex before the arguments of the involved verbs are combined with the verbal complex
the specified comps are attracted by tile matrix verb and the comps list of the matrix verb therefore does not contain any variables and our theory does not admit signs that do n t describe linguistic objects
he entry in the stem lexicon for tile fllt ure tense auxiliary we den will is shown in NUM
however the assumption that slash contains signs rather than local objects is a change of the basic hpsg formalism with far reaching consequences that is not really needed and that has some side effects
as erziihlen does not appear in any comps list it is not possible for the verb to count as an argument of the fronted verbal complex that is saturated in the mittel eld
the relation hierarchy was constructed manually
then we show the trigger forward phase
continue forward until no changes are made
those words form the concept cluster
it expresses a concept of writ
the cckgs could be either permanent or temporary structures depending on the
the temporary graph built from the trigger word forms the initial cckg
some relations like part of in from can be transitive
the set of relations used in temporary graphs come from three sources
based on them we are developing a set of graph matching rules
NUM the probabilities were derived ms maximum likelihood estimates from all pp cases in the training data
finally the four sets of candidate translations were pairwise compared in the cases where differing translations had been produced
when utterances had more than one correct reading a preference heuristic was used to select the most plausible one
as the experiments in the next section show the resulting improvement is quite substantial
utterance utterance unit imperative vp np lcb tel vp modifier rcb pp
there are two main parameters that can be adjusted in the ebl learning phase
the first topic we have been investigating is the application of the methods described here to processing of other languages
null the two methods constituent pruning and grammar specialization are combined as follows
theoretically it shottld not decrease the recall
the third score called the parser score is a heuristic combination of the previous two scores ldus other information such as the number of words skil ped
spsbado quince saturday the fifteenth or saturday building NUM if the speakers have already chosen a date and are negotiating the exact time of the meeting then only the meaning two to four makes sense
et al NUM only take one semantic representation as input at a time thus we had to extend the discourse processor so thai it can handle multiple hypotheses as input
NUM in general frames encode a certain amount of real world knowledge in schematized form
in our database these two frames might be represented as shown in figure i
table NUM examples of frame element groups fegs
prefix is a good marker for possible left boundary
here anaphora resolution and sentence aligmnent are presented
this section introduces some strategies used in the identification
keyword is a good indicator for an identification system
all unknown words disturb one another in segmentation
therefore the correct referential relationships can be wellestablished
some difficult problems should be tackled in the flmlre
first keyword is usually a common content word
that is varl reads as the meaning of the element to which the variable varl is bound
you know is the most frequent discourse marker and is used very frequently by some speakers as shown in example NUM
but rather than relying on these intuitions we apply a more careful analysis of the data to determine more closely what the differences are
there were three major kinds of annotations done as part of the dysfluency annotation of switchboard sentence boundaries restarts and non sentence elements
it is well known in the linguistic literature that sentences are not uniform from beginning to end in the kinds of words or structures used
in order to divide sentences into their given and new portions we devised a simple heuristic which determines a pivot point for each sentence
we have licensed xtract to several sites that are using it to improve the accuracy of their retrieval or text categorization systems
since such phrases can not be translated compositionally they indicate where concepts representing such phrases must be added to the interlingua
for the second test we compute an upper bound for the dice coefficient between the word under consideration and the source collocation
this translation is saved in a table c of candidate final translations along with its length in words and its similarity score
our work is part of a paradigm of research that focuses on the development of tools using statistical analysis of text corpora
for example to take steps is a collocation because to take is used here as a support verb for the noun steps
it is possible to modify the thresholds td and tf according to properties of the database corpus and the collocations that are translated
there are thus NUM single words that pass the thresholds at the first iteration NUM pairs of words and so on
we believe that such a transformation is more transparent to the sp builders enabling them to inspect and manipulate directly the pre spl ex4 in parallel here means in arbitrary order
templates may be of varying complexity from just single entities to message understanding conference muc type templates
the conversion shall be from a selected set of conversion tables and a set of conversion algorithms for the documents
other problems arise from the consonants which can be either single or double without any change in the pronunciation
initially the first order hmm and the common viterbi algorithm were used to obtain a single transcription for each word
figures NUM through NUM give an analytic overview of the results in each language
the performance of the algorithm varied widely depending on the language being tested
a more detailed presentation of the algorithm s behavior in the languages tested follows
in this paper the ptgc problem is approached from a completely different point of view
and although our first example of datr consisted entirely of extensional statements almost all the remaining examples will be definitional
their presence is a logical consequence of a second set of statements which have the concise generalization capturing properties we expect
and as we shall see in section NUM NUM below and elsewhere there is a subtle but important semantic difference
these candidate phrases need not be any more than approximations in partictdar it is not necessary for these candidates to have wholly accurate boundaries as their left and right edges can be adjusted later by means of patching rules
every time the interpreter changes the label of a phrase pairs of form z quot c are added to the lexicon where is a lcxcmc in and c is the label with which is tagged
to begin with consider that the initial phrase labeling proceeds by building phrases around lexemes NUM NUM fz n in a designated word list or by finding runs of certain parts of speech t NUM 7zm
multilingual evaluation mh after the muc NUM evahtation the namcd entity task was extended in various ways to make it more applicable cross linguistically predictably this was followed by a new round of evaluations mv r
the latter prcsent a problem or accurately estimating the improvement of a rule since sometimes the boundary realignment necessary to fix a phrase problem exceeds the amount by which a single rule can move a boundary namely two lexemcs
the machine that reproduces this initial labeling is thus pl rq p n n1 pl nm p n nm pl nl p n rq as usual node labeled s is thc start state and any node drawn with two circles is an accepting state
however by inverting the process and tabulating only those lexical contexts that actually appear in the training texts this search spacc is reduced to z i NUM unal t lcxical rules and NUM binary lexical rules
note howcw r that extending the fl amework with a temporary lexicon makcs it transfinite state lqnally as with all semi parsers the machines we construct in this way must actually be interpreted as transducers not just acceptors
what wc may legitimately report however is that wc have effectively reproduced or bettered our hand engineered english results in the spanish mid japanese t ks despite having no native speakers of either language and only the most rudimentary reading sldlls in kanji
the easy cases of dates mid times are identified by a separate pre processor leaving numeric expressions 1we have also measured performance on several syntactic constructs e.g. the so called noun group and on semantic subgrammars e o person title organization appositions
NUM an overview of thai morphological analyzer with a gradual refinement module instead of using a corpus based approach which requires a large amount of training data and validation data a new simple hybrid technique which incorporates heuristic syntactic and semantic knowledge is proposed to a gradual refinement module which gradually weeds out the alternative and or the erroneous chains of words caused by those three nontrivial problems
for example if there exist verb before modifier then flag NUM else flag NUM according to the above constraint pmij where i modifier and j postverb can be changed from NUM to NUM if flag equals NUM
just over NUM of susanne sentences contain some punctuation so around NUM of the singleton parses are punctuated
the multiple uses of commas can not be resolved without access to at least the syntactic context of occurrence
arguments are sisters within x1 projections x1 x0 argl argn
table NUM gives these measures for all of the sentences in susanne and in sec
text of susanne compares well with the state ofthe art in grammar based approaches to nl analysis e.g.
the results we report above relate to the latest version of the tag sequence grammar
monitoring this distribution is helpful during grammar development to ensure that coverage is increasing but the ambiguity rate is not
this grammar captures the bulk of the text sentential constraints described by nunberg with a grammar which compiles into NUM dcg tike rules
we have made good progress in increasing grammar coverage though we have now reached a point of diminishing returns
to examine the efficiency and coverage of the grammar we applied it to our retagged versions of susanne and sec
table NUM shows the proportion that a french word is paired with the correct english words checked with the top three and five highest candidates
a method for obtaining translation dictionary from parallel corpora was proposed in which not only word word correspondences but arbitrary length word sequence correspondences are extracted
the presented approach is successfully used in the compass project NUM to represent mwls in dictionary databases converted from standard bilingual dictionaries
because idaitex use8 a two level morphology words can be presented either in their base form at the lexical level or in an inflected form at the surface level
for instance for the french example above we in addition idarex allows the definition of macros to capture generalisations on the syntactic level
the local grammars can be written in a very convenient and compact way as regular expressions in the formalism idarex which uses a two level morphology
further macros are defined for german for mwls with a reflexive or particle verb to express scrambling of an idiom external pp complement or topicalisation
wovlarg fixs den schsnen schein vahren wovlhrg fix2 die ohren spitzen
welle the lezical form is followed by an idarex morphological variable specifying morphological features of the word and a colon e.g.
by hair s breadth and can thus be easily recognized with simple pattern matching techniques it is well known see e.g.
we thank annie zaenen lauri kaxttunen ted briscoe and irene maxwell for their comments on an earlier draft of this paper
the presented approach overcomes this problem instead of having to specify local grammars directly as finite state networks or as graphs e.g.
this functional division of labor can be seen in the conceptual complex expressed by any portion of discourse such as a single sentence
on the other side is the closed class or grammatical system comprised of elements that are relatively few in number and difficult to augment
efficient large scale acquisition and representation of lexical knowledge will be greatly aided by capturing regularities in the lexicon
how could we characterize an nlp system along the dimensions of size corpus coverage and depth
linguistic closed class forms such as prepositions schematize spatial relations between objects and vision appears to involve the perception of comparable schematic relations among objects
to illustrate with a vernacular example the sentence a rustler lassoed the steers contains three open class elements rustle lasso and steer
this model can be illustrated first with a pair wise comparison of the structural properties in the cognitive system of language and that of visual perception
further findings have uncovered certain principles that govern the kinds of concepts that can be expressed by closed class forms and the kinds that are excluded
but the present theory of closed class semantics recognizes the existence of a specific inventory of closed class concepts in which any process of grammaticization must terminate
multi lingual translation of spontaneously spoken language in a limited domain
sentence NUM could do it wednesday morning too
eighteenth and wednesday the nineteenth NUM couhl meet the whole day do you want to try to get together m the afternoon figure NUM a phoenix spanish to english transla tion examl h
we describe how our machine translation system is designed to effectively handle these and other problems hi an attempt to achieve both robustness and translation accuracy we use two different translation components the jllt
since lr performs much better when long utterances are broken into sentences or sub utterances which are parsed separately we are looking into the possibility of using phoenix to detect such boundaries
first the glr translation module performs better than the phoenix module on transcribed input and produces a higher percentage of perfect translations thus confirming the glr approach is more accurate
in many such cases i lh succeeds to parse only a small fragment of the entire utterance and important input segments end up being sldl t ed
phoenix translation modules NUM NUM strengths and weaknesses of the approaches
we present our most recent spanish to english performance evaluation results
these merges do not influence the probability assigned to the training part and thus do not change the perplexity
the thin line shows the further development if we retain the same output constraint finally yielding a bigram model
none of these models include explicit strategies or paradigms to tackle the problem of distributed control
time behavior is a special case of that g6rz and kesseler NUM
t linside x trans x a l
figure NUM l he interactive incremental intarc
some of the modules are very complex software systems in thelnselves
the follwing control loop implements a simple lri lattice parser
itence backward search becomes a part of the parsing algorithm
in this way the computational effort on the whole is kept as low as possible
the question of control in vm is tightly knit with the architecture of the vm system
for more details on this track see the paper the trec NUM filtering track by david lewis in the trec NUM proceedings
it was felt by many groups that trec uses evaluation for a batch retrieval environment rather than the more common interactive environments seen today
in trec the routing task is represented by using known topics and known relevant documents for those topics but new data for testing
fifty new topics numbers NUM NUM were generated for trec NUM with fifty additional new topics created for trec NUM numbers NUM NUM
the known documents used in trec NUM were on disks NUM and NUM and those used in trec NUM were on disks NUM and NUM
some testing of this consistency was done after trec NUM when a sample of the topics and documents was rejudged by a second assessor
there were NUM sets of results for adhoc evaluation in trec NUM with NUM of them based on runs for the full data set
w brace croft and daniel w nachbar used a version ofprobabilistic weighting that allows easy combining of evidence an inference net
the top NUM documents were used in a rocchio relevance feedback technique to massively expand NUM terms NUM phrases the topics
figure NUM also plots the development of the vocabulary of syllables in trouw bot tom left and the development of the vocabulary of digraphs in alice in wonderland bottom right
the bottom left panel of figure NUM shows that for the first frequency ranks f the good turing estimate met f m underestimates the probability mass of the frequency class in the population
NUM the type definition i have used throughout is based on the orthographic word form house and houses are counted as two different types houses and houses as two tokens of the same type
the vertical axis of the left hand panels shows the number of observed word types dotted line and the number of types predicted by the model solid line obtained using NUM
the two progressive difference score functions are shown in the bottom left panel of figure NUM and the residuals d k du k are plotted in the bottom right hand panel
the nonuniform distribution of ahab sheds some light on the details of the shape of the difference function e v n v n shown in figure NUM
these differences in turn shed light on the details of the differences in the patterns of estimation errors e v n v n that characterize the texts
this intra textual cohesion gives rise to a substantial part of the overestimation bias a bias that leads to significant deviations even when small text fragments of some NUM words are selected randomly from a newspaper
quency spectrum of the complete text the latter is determined by the text itself the choice of k influences only the number of points at which the divergence between the two curves is measured
however the remaining changes caused by putting the input in chomsky normal form are irrelevant to the basic goal of creating a left anchored output
this contrasts with the greibach and ltig procedures where the order chosen can have a significant impact on the number of elementary structures in the result
for those where j k we generate a new set of initial trees by substituting other initial trees for in accordance with lemma NUM
for example substituting a1 aa2 into z2 a1 yields the same result as substituting a2 a into z2 a2a2
a layer of an elementary tree is represented textually in a style similar to a production rule e.g. x y pz
at each step none of the modifications made to the grammar change the tree set produced nor introduce more than one way to derive any tree
the nonterminal symbols on the frontier of an auxiliary tree are marked for substitution except that exactly one nonterminal frontier node is marked as the foot
given the fundamental advantages of the rosenkrantz procedure over the gnf procedure this might lead to a result that is superior to the ltig procedure
the proof is based on a lexicalization procedure related to the lexicalization procedure used to create greibach normal form gnf as presented in harrison NUM
allhough an objective case is usually marked by case marking particle wo some adjective predicates have an onective case marked by case marking particle ga
perplexity markov models assign rapidly decreasing probabilities to output sequences of increasing length
model merging stops when a predefined threshold for the corpus probability is reached
their target is to find correspondences not only of word level but of noun phrases and unknown words
the method is tested with parallel corpora of three distinct domains and achieved over NUM NUM accuracy
in our example high frequency auxiliaries such as hebben cause the probability of sampling unseen types in en to be low newly sampled tokens have a high probability of being an auxiliary rather than some previously unseen word
the results of the simulation were very similar for the different values of no with no apparent pattern emerging as no increased
our goal is to provide a tool for compiling bilingual lexical information above the word level in multiple languages for different domains
figure h a subframe can inherit elements and se
for each candidate feature f e considered in step NUM we must compute the maximum entropy model p u a task that is computationally costly even with the efficient iterative scaling algorith ntroduced earlier
for the translation example just considered the process generates a translation of the word in and the output y can be any word in the set lcb dans en au cours de pendant rcb
de not given notice a letter to respect of the the fiscal year the same postal canada by the ordinary way y equal to the french word which is according to the viterbi alignment a aligned with in
the feature h mentioned above is thus derived from template NUM with o en and april the feature f2 is derived from template NUM with o pendant and weeks
computing the approximate gain in likelihood from adding feature f to ps has been reduced to a simple onedimensional optimization problem over the single parameter which can be solved by any popular line search technique such as newton s method
we next discussed algorithms for constructing maximum entropy models concentrating our attention on the two main problems facing would be modelers selecting a set of features to include in a model and computing the parameters of a model containing these features
if the processing time is exponential in the length of the input passage as is the case with the candide system then failing to split the french sentences into reasonably sized segments would result in an exponential slowdown in translation
berger della pietra and della pietra a maximum entropy approach by adding feature f to s we obtain a new set of active features s u f following NUM this set of features determines a set of models
this was true for the example presented in section NUM when we imposed the first two constraints on p unfortunately the solution to the general problem of maximum entropy can not be written explicitly and we need a more indirect approach
we use ntra n NUM NUM words of the verbmobil corpus
model merging starts with the maximum likelihood model for the training part
this behavior can be exploited by introducing constraints on the merging process
NUM conventional algorithm for n gram statistics before discussing the algorithm which satisfies the previous conditions for uninterrupted collocational substring let s consider the nagao and mori s algorithm propose for n gram statistics
unfortunately in this method so many fractional substrings that were grammatically and semantically inconsistent were being extracted that it was difficult to extract combi nations of expressions collocated at separate locations i.e.
case c in which substring a and NUM are separate from one another is a case of extracting interrupted collocations and cases a and co are not NUM
various types of applications are possible such as word chains syntactic element chains obtained from results of morphological analysis or semantic attribute chains which consist of each word being converted to semantic attributes
the memory capacity were NUM mb
fig NUM relationships between extracted substrings
this effect increases as the increase of substring length
extracted in chapter NUM in the order of extractions
each step would require the test of NUM NUM merges
the syntactic tagset is being studied using NUM NUM sentences out of pos tagged corpus and the resultant tree tagged corpus using a tree tagger will appear at the end of this year
because of the big size of each text to be stored and lots of keywords to be indexed and searched for each text it requires special storing and managing mechanisms
templates dictionary features text descriptors and relations among those specifications for text dictionary editor and format translator have been also being designed and low level design is being undertaken
its functions include indexing and searching word phrases morphemes or unigrams applying logic operations and or no2 to them and sorting the results
hi fhis work is fimded by ministry of science and NUM clmology and ministry of education and athletics as a part of a contract by center for korean language engineering
figure NUM shows the conceptual diagram of ip
we admit that this is a somewhat informal argument
first one can find the most probable derivation
null parsing using the dop model is especially difficult
note that the number of changes made was small
we will call this non terminal ak
how many subtrees does it have
this parse tree is most probable
in contrast to ltig which derives the correct trees in the first place this transformation requires a separate post phase after parsing
however the rosenkrantz procedure typically produces grammars that are less compact than those created by the ltig procedure see section NUM NUM
if one abandons the requirement that the grammar must be left anchored one can sometimes reduce the number of elementary trees produced dramatically
for instance the tree in figure NUM is represented in terms of four layer productions as shown on the right of the figure
as in tag but in contrast to cfg there is an important difference in tig between a derivation and the tree derived
we will concentrate here oil quantitative structures such as beaueoup d enfants many children and partitive structures such as beaucoup de ces enrants many of these children
aggregation is an important component of text or sentence planning
in the study we manually investigated in total NUM texts
different subsets of an information collection may give rise to many and varied opportunities for aggregation
in fact human authored text contains aggregations throughout as our corpus study shows
the texts was scanned automatically for cue words and we found the ratio bounded lexical aggregation cue words total sentences to be NUM NUM i.e. we have at least NUM NUM bl aggregations because the ones with no bl aggregation cue word are not visible or easy to find when scanning a text automatically
since the workspace contains only character objects the only relevant concepts are character affinity affix and each character of the sentence
the middle character can also combine with the next character to form the word x2 gongzud work leaving the first character alone
the chinese characters in figure NUM not enclosed by rectangles namely the characters s4n and i g are character objects
the undirected arc connecting the characters hdi and zi in figure NUM represents a statistical relation and statistical relations are undirected in our representation
an issue that arises from the nondeterministic feature of the system is will the word boundaries of a locally ambiguous sentence vary at different runs
this demonstrates that its mechanisms are able to strike an effective balance between random search and deterministic search imbuing it with both flexibility and robustness
both start with a high temperature allowing all sorts of random steps to be taken and slowly cool the system down by lowering the temperature
however people have a fascinating ability to fluidly perceive groups of characters as words in one context but break these groups apart in a different context
table NUM lists the average precision ratings for the nine data sets
in NUM for instance the np the beans in i he right node raising construction has to bc shared by the two eleinentary trees anchored by cooked and ate respectively
NUM calculating the likelihood of the decoded string for each language
p c l is estimated from text corpora in language i
recent advances in information infrastructure have made an enormous number of on line documents accessible
the algorithm is an application of an automatic language identification using statistic language models
this paper presents an algorithm that identifies the coding system and the language of a given text
it covers NUM coding systems and NUM languages used in east asian countries as well as western european countries
as compared with western european languages east asian languages have the following properties NUM
a large number of characters east asian languages use over NUM NUM ideographic or combined characters
step NUM this step decodes each part and identifies its language s
another approach is to uncover the coding system from the received byte code string
the first task for alignment is therefore to divide the text stream into words
thus it is impossible in general to apply the simple feature based methods to japanese english translations
sato s method was then reiterated using both the acquired word correspondences and the hand crafted dictionary
this experiment was performed on a sparc station NUM model tis21
the first operation deals with word correspondences in the bilingual dictionary
section NUM concerns related works and section NUM concludes the paper
first section NUM offers an overview of our alignment system
the leftmost nmnbers in figure NUM are
let the input japanese character sequence be
NUM iterate through the adjustment steps until the maximum difference e between the marginal totals observed in the sample and the estimated marginal totals reaches a certain minimum threshold e.g. e NUM NUM
where k is the length of the character sequence
there are NUM possible word segmentations in this example
table NUM the amount of training and test data
the expected word unigram counts in the corpus are
it contains NUM unknown word types
we also made two length models
function is the generalization of the dictionary
reestimations were carried out three times
similarly the term ul2 ij denotes the deviation of the mean of the expected cell counts with value i of the first variable and value j of the second variable from the grand mean u
coocnada and cooead n are similarly defined
a i olll NUM NUM words
this could mean that the results of the lexical association method can not be improved by adding other features but it is also possible that the features that could result in improved accuracy were not identified
the experimental results show that with the same feature set inodeling feature interactions yields better performance such nmdels achieves higher accuracy and its accura y can be raised with additional features
these are flexible collocations exhibiting variations in word order
the highest accuracy was obtained by the loglinear model that includes all two way interactions and consists of two contingency tm les with the following features pos all upper case
the intent of this requirement is to allow new applications to adopt and modify existing tipster lexicons where suitable
the document sets can have access controlled through access control that limits usage to the specified user or user group
since persistent knowledge items will require frequent access the format and access design shall place a high priority on efficiency
this includes sharing by different components in an application and by different applications built in accordance with the tipster architecture
this shows that the loglinear model is able to tolerate redundant features and use information from more features than the simpler method and therefore achieves better results at ambiguity resolution
the size function and complexity of an item is a guide to calling the item a component
results from detection extraction or user actions may be used to modify the items in the persistent knowledge repository
the architecture shall facilitate this co operation by allowing status information to be updated and passed between components NUM
document list is an ordered list of document identifiers and optional document attributes or computational result for each list entry
the pre spl expression created by the discourse structuring module reflects the choices made regarding sentences NUM to NUM above in the type and number of roles to introduce the role filler information etc
the loglinear model provides a posterior distribution that is properly conditioned on the evidence and maximizing the conditional probability p hie leads to minimum error rate classification duda and hart
the diagram shows that the loglinear model leads to better overall tagging performance than the simpler methods with a clear separation of all samples whose proportion of new words is above approximately NUM
to avoid thrashing typically some technique is used to normalize the inside probability for use as a figure of merit
at any point in the algorithm there exist constituents which have been proposed but not actually included in a parse
they proposed a heuristic solution of penalizing shorter constituents by a fixed amount per word
thus using j3 alone assumes that a and p tom can be ignored
this estimate represents the probability of the constituent in the context of the preceding tags
the y axis has been restricted so that the normalized NUM and trigram estimates can be better compared
the chart is a data structure which contains all of the constituents which may occur in the sentence being parsed
one approach is to take the geometric mean of the inside probability to obtain a per word inside probability
the extraction component shall determine how to fill the template based upon these criteria
our concept grammars described in section NUM NUM are in the spirit of bergler s notion of a meta lexical layer that provides a mapping between the syntax and semantics of individual responses
this is quite a different scenario from natural language understanding systems which can be designed using large corpora from full text sources such as the ap news and the wall street journal
therefore assuming that the accuracy of this method could be improved satisfactorily automated scoring would appear to be a viable cost saving and time saving option
this paper discusses a case study in which lexical semantic techniques were used to implement a prototype scoring system for short answer free responses to test questions
however we believe that by using the augmented lexicon and our concept grammars to automatically score the NUM independent data we can get a
the fourth error type accounted for NUM percent of the cases in which there was significant conceptual similarity between two categories such that categofial cross classification occurred
these were cases in which a response could not be classified because its concept structural patterning was different from all the concept grammar rules for all content categories
for this study a domain specific concept based lexicon and a concept grammar were built to represent the response set using NUM of NUM responses from the original data set
to decrease the algorithm execution time and storage needs we introduced the following improvements a
the dashed lines refer to a first order hmm experiment while the solid lines refer to a second order hmm experiment
NUM NUM verification method demonstration and inspection
note NUM this document presents requirements for the tipster architecture
the architecture shall allow extraction to provide abstracts or document summaries
NUM NUM verification method demonstration and inspection
NUM NUM verification method demonstration and inspection
the specific quality and content of the abstract are application dependent
compiled queries operate for both retrospective retrieval and routing applications
NUM NUM NUM verification method inspection
editing includes modification or deletion of any tags or links
annotations may be created and or used by the detection component
template NUM features consider only the left noun
instead we must resort to numerical methods
al s f l psu l ps NUM at each stage of the model construction process our goal is to select the candidate
the basic translation model has worked admirably given only the bilingual corpus with no additional knowledge of the languages or any relation between them it has uncovered some highly plausible translations
the algorithm generalizes the darroch ratcliff procedure which requires in addition to the nonnegativity that the feature functions satisfy ifi x y NUM for all x y
a second linear constraint could determine p exactly if the two constraints are satisfiable this is the case in c where the intersection of c1 and c2 is non empty
we have a sample of his work in the training sample x y and we measure the worth of a model by the log likelihood l p
at the point when overfitting begins however the new constraints no longer help p model the random process but instead require p to model the noise in the sample pr itself
template NUM features give rise to constraints that enforce equality between the probability of any french translation y of in according to the model and the probability of that translation in the empirical distribution
we merely replace the simple context independent models p yi e used in the basic translation model NUM with the more general context dependent models pe y x
sincej is counting in terms of characters the increment ofj one NUM is one whether the characters in p are single or two bytes
gb big5 and unicode have been designed to represent a selected subset NUM NUM NUM which requires two or more bytes to represent
as a result our method is more robust than dynamic programing techniques against the shortage of word correspondence knowledge
text NUM is easy to align in terms of both the complexity of the alignment and the vocabularies used
although conventional evaluations can make only one error from the chunk three errors may arise by our evaluation
let anc be the minimal number of corresponding words for a sentence pair to be judged as an anchor
the initial asm has little effect on the alignment performance so long as it contains all correct sentence correspondences
NUM jsentencei and esentencej does not cross any sentence pair that has more than anc word correspondences
given that the multi byte string p is translbrmed into a single byte string l existing algorithms can be used to construct the automaton
figure NUM the trains NUM system architecture
a robust system for natural spoken dialogue
for p ols we build a channel model that assumes independent word for word
similarly the results of the second experiment are shown by the middle curve
of the NUM tasks attempted there were NUM in which the stated goals were not met
all subjects were given identical sets of NUM tasks to perform in the same order
solution quality for our domain is determined by the amount of time needed to travel the routes
in fact the user decides to accept the proposed route and forget about going through cincinnati
finally the outcome of the third experiment is reflected in the uppermost curve
for example there is no general rule for pp attachment in the grammar
in fact it appears to be more efficient in this application than keyboard
these problems reveal a need for better handling of corrections especially as resumptions of previous topics
since the computer can not determine whether t NUM is the first or second byte of the NUM byte character it can not use the delta tables to determine the next matching states
a sentence pair containing more than a predefined number of corresponding words is determined to be a new anchor
it should be noted that if we use only the leaves in t for genermization there will be no actual change in the table and this node set is included in NUM
we put equal emphasis on the two terms in formula NUM and fixed a so that the traverse via the root node of tand the traverse via leaves only would have equal scores
during the design of a kb accessing system the domain knowledge engineer selects the core concepts and flags these concepts in the knowledge base
for example in biology processes such as development and reproduction play central roles in many physiological explanations
for example some of the actors in the photosynthesis process are chloroplasts light chlorophyll carbon dioxide and glucose
the backbone of the biology knowledge base is its taxonomy which is a large hierarchical structure of biological objects and biological processes
to mirror the structure of expository texts an edp contains a hierarchy of nodes which provides the global organization of explanations
note that content specification nodes may have elaboration nodes as their children which in turn may have their own content specification nodes
atomicity permits discourse knowledge engineers to achieve coherence by demanding that the explanation planner either include or exclude all of a topic s content
its first action is to obtain the children of the edp s exposition node these are the topic nodes of the edp
the subject has a pointer to identify itself with the subject of the main clause while the object contains a typical noun phrase
various strategies are proposed to identify and classify three types of proper nouns in chinese texts
for fair evaluation large scale test data are selected from six sections of a newspaper
there are many company names and some of them are similar to chinese personal names
the formula used to calculate p ci is similar to equation NUM
how to calculate the score of a candidate is an important issue in this identification system
this is because many characters have high probabilities to be a chinese personal name without pre segmentation
it finds the character string that meets the character condition syllable condition and frequency condition
for each candidate we check the syllable of the first the last character
if there is a large enough transliterated name corpus the syllable orders can be learned
according to the results of table NUM r NUM NUM was selected for the better trade off between recall precision and coverage
this does not mean that they do n t work but simply that their applicability to real domains is yet to be proven
after each learning step the upgraded plausibility values provide newer mcp1 scores that are more reliable because the hell esfs have been discarded
to smooth the weight of ambiguous esl s in lexical learning each detected esl is weighted by a measure called plausibility
this analysis demonstrates that syntactic disambiguation in large can not be afforded by the use of knowledge induced exclusively from the corpus
NUM apply class based disambiguation operators to reduce the initial source of noise by first disambiguating the non persistent ambiguity phenomena
a baseline is also inmxlueed and compared
the rest of the procedure is straightforward
the baseline we used is leftmost derivation
NUM NUM co occurrence data collection by direct text scanning
figure NUM shows two possible dependency structures in a three word compound noun
this problem can be solved using a preprocessor explained below
the frequency of these word lengths is about the same in the corpus
these rules construct the basic framework of the dependency structure of a compound noun
well i suspect that it is possible
table NUM switchboard corpus divided by pivot point
table NUM totals for NUM most frequent words
the heuristic finds the first strong verb and if there is none then the last weak verb and places a pivot point either before the strong verb or after the weak one
we also build language models using these two NUM this tendency is overridden in marked syntactic structures such as cleft sentences it was suzie who had the book last
in this paper we explore how the addition of information to the text in particular part of speech and dysfluency annotations can be used to build more complex language models
some sentences have a rather long introduction with restarts as in sentence NUM and NUM whereas others have just a single word and a long after portion as in sentence NUM
however given only a speech signal during recognition with no text cues available for segmentation there will be an inherent mismatch between the linguistically segmented training data and the acoustically segmented test data
lcb p so rcb he he s pretty good about taking to commands this is a category for asides that interrupt the flow of the sentence
there are five types of non sentence elements filled pause lcb f rcb editing term lcb e rcb discourse marker lcb p rcb conjunction lcb c rcb and aside lcb a rcb
obviously this opens a whole new can of worms in that the interface has to be designed the kind and order of questions etc we will get back to that later in ss NUM
we have sought to accomplish this by instructing the evaluator to link sentences linguistically more specifically wc have opted to instruct the evaluator to choose a conjunct NUM to be inserted between every pair of consecutive sentences
the evaluator s task would involve so much juggling with relations and attaining such a deep understanding of the text that it would in the end have a negative effect on the reproducability and evaluator independence of the results
the basic idea is to have a human evaluator take the sentences of the translated text and for each of these sentences determine the semantic relationship that exists between it and the sentence immediately preceding it
the mean of the evaluators choices was computed by transforming the results into numbers if NUM out of NUM evaluators chose category x NUM chose y and NUM z then this would result in the values lcb NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM rcb and inputting these numbers into the following formula
these profiles can then be compared to give an indication of translation quality if we assume that the original text s profile is perfect then the degree to which the profile of the translated text resembles tile perfect profile will correspond in theory at least to the quality of the translation
indeed the problem with categories and definitions is that the evaluator will always have to depend to a certain extent on his own personal understanding of these definitions and the more categories there are the greater the chance that their definitions will not always be clear and fixed in his mind
both topic and comnlent were only loosely defined in truth the topic and comment are not important as such rather their extraction was intended as a means to force the evaluator to get a clearer picture of the meaning of the sentence under consideration though we did not tell them this
a backtrack function was implemented which allowed the subjects to come back on decisions made earlier in the dialog
semantic interpreters generators unlike the previous modules the one in charge of the bidirectional mapping of the syntactic structures onto the knowledge structures of the microworld is very sensitive to factors such as discourse universe tutorial strategies student profile etc
it is debatable whether the system should communicate with the student only in the target language or whether under specific circumstances such as error correction mode it should also be able to generate texts also in the student s mother tongue
despite some spectacuhu progress made at the level of interface several fundamental language learning principles are only partially met
again there are several nl generators most of them head driven available in the public domain
however few corpora if any have been gathered with respect to register
generators in order to communicate with the student the call system should be able to produce natural language output
however the community of language learning seems to ignore this development most of the existing language learning systems drawing their enhancements from other sources such as hypertext nmltimedia interactive video information retrieval
user modelling subsystems tuned to the language learning problems could provide valuable support in dealing with notorious difficult problems discovering student s misconceptions tailoring explanations to the level of the student s expertise etc speech synthesizers and prosody processors speech technology is definitely a valuable candidate i010 for call tools
in spite of the current gap between speech technology and natural language processing language learning is a very promising area where tile two fields could meet one could easily imagine a scenario where the student is asked to utter a word or a sentence which are then compared and corrected against the tutor s pronunciation
the potential for using information theoretic constructs to measure corpus similarity is a topic for current research
in particular the type of fd that it accepts as input specifies a process in the systemic sense it can be an event or a relation
NUM propagate agreement features provid null ing enough input to the morphology module e.g. after the agent and process thematic roles have been mapped to the subject and verb group syntactic roles
the hierarchy of general process types defining the thematic structure of a clause and the associated semantic class of its main verb in the current implementation is compact and able to cover many clause structures
NUM mood which handles departures from the default core syntactic structure triggered by variations in terms speech acts e.g. interrogative or imperative clause and syntactic functions e.g. matrix vs
component for nlg natural language generation has been traditionally divided into three successive tasks NUM content determination NUM content organization and NUM linguistic realization
it is based on two powerful concepts encoding knowledge in recursive sets of attribute value pairs called functional descriptions fd and uniformly manipulating these fds through the operation of unification
independent efforts to define such an input have crystalized around a skeletal partially lexicmized thematic tree specifying the semantic roles open class lexical items and top level syntactic category of each constituents
the use of specific lexical processes to complement general process types is an example of the type of theorv integration that we were forced to carry out during the development of surge
the client provides the feature tpatl ern et before sc specifying that the event time et precedes the speech time st
for example generating the sentence lames
if on the other hand all alternatives remain viable to the end then the sp has produced more than one valid locution from the input
it may be the case that the architecture does not include a standard for some pre existing markup which would be useful for some other part of the application
for example formatting markup for bolding which may not be specified in the architecture might be used by the application specific user interface component
when new modules must be implemented the standards for the interfaces will be pre established so that a minimum of investigation about related application modules will be required
examples of a form NUM document might be the portion of the news broadcast which was saved digitally or transcribed the ocr d note the scanned photograph
because the original document is always retained all pre existing markups are available to the application whether or not they are converted to the architecture standard
these templates would be objects and each of the slots in the templates would be linked to the parts of the application that are used to fill it
it encompasses ideas which will be realized during the tipster text phase ii program as well as ideas which will be developed over a longer period of time
an architecture also includes information on the technologies interfaces and location of functions and is considered an evolving description of an approach to achieving a desired mission
the more applications use and contribute sharable modules and data the better and more extensive the architecture will become and the more cost savings will be realized
the tipster architecture will help the cotr choose an application developer team since a review of previous applications and modules will identify developers that provided support a particular technology
according to r b and r c l t NUM is recorded as an unregistered word and stored in wd which invokes a search of the patterns around it
we imagine that an english sentence e generates a french sentence f in two steps
the concept of maximum entropy can be traced back along multiple threads to biblical times
in this paper we describe a method for statistical modeling based on maximum entropy
one obvious clue we might glean from the sample is the list of allowed translations
consequently a variety of numerical methods can be used to calculate
as the algorithm progresses l p thus increases monotonically
this can yield errors when candide is called upon to translate a french sentence
where n ei denotes the number of french words aligned with ei
among these models we are interested in the one that maximizes the approximate gain
the only parameter distinguishing models of the form NUM is c
these are entities that although generic in nature can be interpreted us specific whet they are considered in the specific communicative situation in which the actual applicant reads the instructions to complete the pension form he has in his hands
the proposed extension to the centering model unfortunately the centering model does not capture completely the reader s flow of attention process since it fails to give an account of the expectations raised by the role the clause plays in the discourse
multilingual pension forms ore work on the specification for the referring expressions component started from an analysis of the collected multilingual english german italian corpus of texts containing instructions on how to fill out pension forms
according to the centering rules it would not be possible to use a pronoun to realize ek since the main center of utterance d the envelope is different from the main center of utterance c the spouse
this means that the rhetorical information an project the default ordering of the elements in he potential focus list cf c onto a new order tha reflects more closely the content progression
the plausibility of this anchoring operation is confirmed by the fact that the linguistic realization choices made for anchored entities definite tbrms singular indefinite forms resemble very much the linguistic choices made tbr specific entities
the choice of this solution has emerged from the observation that anaphora plays two roles in the discourse it is not sufficient that a pronoun identifies unambiguously its referent but it has to reinforce the coherence of the text as well supporting the user s expectations
these semantic features depend on the entity type whether generic anchored or specific and on the relationships between the entity and the context whether the entity is new with respect to the context presenting or its identity can be recovered presuming
the texts of syllables and digraphs preserve the linear order of the texts from which they were derived
for example japanese euc and korean euc can not be discriminated by this simple method because most of their code values overlap NUM
distinguishing these sets permits us to model the empirical distribution better since the old field assigns them equal probability counter to the empirical distribution
very roughly we would like to choose weights so that the expectation offi under the new field is equal to NUM f
the fact that attribute value grammars generate constrained languages makes gibbs sampling inapplicable but i show that sampling can be done using the more general metropolis hastings algorithm
let us continue to assume grammar g2 generating the language in figure NUM and let us continue to assume the empirical distribution in NUM
on the face of it then we can transplant the methods we used in the context free case to the av case and nothing goes wrong
the natural extension of the method we used for context free grammars is the following associate a weight with each of the six rules of grammar g2
the frequencies number of instances of features NUM and NUM in dags generated by g2 and the computation of dag weights and dag probabilities q
the random sampling method that dd l used is not appropriate for sets of dags but we can solve that problem by using the metropolis hastings method instead
in order to re merge processes must be in synch which is to say they can not evolve in complete independence of one another
the above three models can be extended to single character names
it avoids the problem of very high score of surnames
table NUM shows the precision and the recall for every section
table l stunmarlzes the identification results of chinese personal names
the following shows two rough classifications and discusses their feattues
the identification system scans a segnlented sentence from left to right
thus keyword ix an important clue to identify the organizations
the transliterated personal name z rcb
the threshold is determined in the similar way as section NUM NUM NUM
a generic textual clue can thereby be described by the two following attributes the surface syntactic pattern representing the syntactic regularity with a label on the item where to find the lexical marker the lexical marker itself typically the word metaphor itself can be used as a lexical marker in expressions such as to extend the conventional metaphor pruning such a tree means to generalize
indeed the semantic anmysis proposed for dealing with metaphors were processed depending on the results of another say a classical one NUM 2we prefer to call it a classical rather than literal meanings processing because it can deal with some conventional metaphors even if not explicitly mentioned
in order to take into account spatial expressions like above the square b4 or at the left of the pawn which we have specified which objects can be categorised as a place and which ones can not
since most of the lexical ambiguity resolution power of stochastic pus tagging comes from the lexical probabilities unknown words represent a significant source of error
for example l l and ll are found to correspond to trade secret and business hour respectively
NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
since high similarity value is supported by high co occurrence frequency a gradual strategy can be taken by setting a threshold value for the similarity and by iteratively lowering it
in the same way a word sequence w of length i NUM is taken into consideration only when its prefix of length i has been extracted and w appears at least twice in the corpus
kumono hirakawa s objective is to obtain english translation of japanese compound nouns noun sequences and unknown words using a statistical method similar to brown s together with an ordinary japanese english dictionary
in texts of expertise a number of word sequence correspondences not word word correspondences are abundant especially in the form of noun compounds or of fixed phrases which are keys for better performance
though our method does not assume any bilingual dictionary in advance once words or word sequences are identified in an earlier stage they are regarded as decisive entries of the translation dictionary
NUM a threshold for minimum frequency of occurrence f in is decided and the following process is repeated every time decrementing the threshold by some extent
for korean language engineering it is necessary to develop systematically all the projects of each area and integrate them into a uniform frame called an information platform ip NUM kle programs each project according to its priority and state of the art technology
and in NUM enter for korean language engineering kle was founded to serve as a centrm organization for korean language engineering which aims to plan and progranl related projects an i works in a consistent systeinlttic way with long teiun gems
ti ms goals are twofold to provide customi able information extraction indexing search tools and managerial functions for text data base and to provide an environment for dictionary deveb opment and management as well as converting or merging existing dictionaries to the intended one according to user s specification
rel etition of the above mentioned processes corresponds to generational replacement of the whole of system and the system continuously evolves to higher quality translation
the correct translation t requcncy the number of uses x NUM NUM third tile system performs tile selection process using the fitness value
as shown in figure NUM a chromosome corresponds to a translation example which consists of english and japanese sentence and a gene corresponds to a word
therefore we applied genetic algorithms to a method of machine translation using inductive learning to automatically produce ncw translation examples which are similar to other translation examples
the ineffective translation results are grouped into three categories NUM the translation result has a different character string than the proofread translation result without unregistered words
thus we consider that this new mcthod can get the higher accuracy rate of translation and produce the higher quality translation results than that of other machine translations
NUM processes in the new method NUM NUM outline of tile translation method figure NUM shows the outline of the new translation method
first NUM NUM translation examples were used for the learning process and NUM translation exanlples were used for evaluation of the translation
the japanese sentence which is produced is the translation result figure NUM shows an example of how the translation result is produced
the system produces a japanese sentence for the english sentence when the english sentence has the same character string as the source sentence
the development phase of erel has shown that the particularities underlying this type of applications can easily be incorporated inside illico
the ability to accomplish something like that is desirable but it is not something to which we are presently committed
however we do not mention the arguments here because of the lack o1 space
the non nonlinal dictionaries aye made usually hy manual work
such bilingual phrases are also useful for other multilingual tasks including information retrieval of multilingual documents given a phrase in one language summarization in one language of texts in another and multilingual generation
NUM in this section we refer to missed valid translations or failures using these terms to describe candidate translations that are above the dice threshold but are nevertheless rejected due to the non exhaustive algorithm we use
on the second iteration the word pair officielles langues is selected out of NUM pairs that pass the threshold with a score of NUM NUM
there are many other possible measures of association and the general points made in this section may apply to them insofar as they also exhibit the properties we discussed
we also thank ofer wainberg for his excellent work on improving the efficiency of champollion and for adding the preposition extension and ken church and at t bell laboratories for providing us with a prealigned hansards corpus
our second simulation experiment tested this expectation for various values of the final threshold using a lower initial threshold equal to a constant NUM times the final threshold
while all similarity measures will be inaccurate when the data is sparse the results produced by specific mutual information can be more misleading than the results of other measures because s is not bounded
the dice threshold ta currently set at NUM NUM is the major criterion that champollion uses to decide which words or partial collocations should be kept as candidates for the final translation of the source collocation
in this way we can ignore the context of the collocations and their translations and base our decisions only on the patterns of co occurrence of each collocation and its candidate translations across the entire corpus
then insert dq c else insert dq hi l table NUM top k bfs search heuristic
null the bigram parser is a statistical cky style chart parser which uses cooccurrence statistics of head modifier pairs to find the best parse
it differs from the maximum entropy parser in how it builds trees and more critically in how its decision trees use information
likewise the input to the third pass consists of k of the best distinct chunk and pos tag assignments for the input sentence
at the lexicographic level of description we could simply list the full set of fegs for a given lexical unit
for q l to p if completed dq ultrasparc processor and 256mb ram of a sun ultra enterprise NUM
it uses simple and concisely speci fled predicates which can added or modified quickly with little human effort under the maximum entropy framework
to gauge the effectiveness of these techniques we developed the two panel evaluation methodology and employed it in the evaluation of knight
cawsey analyzed the system s behavior as the dialogues progressed interviewed subjects and used the results to revise the system
in all but two nonzero cases grammatically well formed sentences are assigned a higher raw probability by the new model and vice versa for ungrammatical sentences
then for each member of this cluster a partial score is calculated that rates our classification of the word against its distribution of lob classes
a third reason and one which we consider to be important is that multilevel class based language models may perform significantly better than two level ones
a multilevel smoothed bigram model is NUM better than a baseline maximum likelihood model and NUM NUM better than the best two level class based bigram model
the model that uses word frequencies exclusively differentiates between the two hypothesized sentences by examining the unigrams boy seat boys and eat
hughes does not use the classification system provided with the lob corpus instead he uses a reduced classification system consisting of NUM class tags shown in
the algorithm is designed so that at a given level s words will have already been re arranged at levels s NUM etc to maximize the average class mutual information
mcmahon and smith improving statistical language models where ci and cj are word classes and ms t is the average class mutual information for structural tag classification t at bit depth s
after an iteration through the vocabulary we select that t having the highest m t value and continue until no single move leads to a better classification
as suggested earlier we can look at the spread of values used by the smoothed bigram component as a function of the class granularity and frequency of the conditioning word
this phase was carried out by lexicographers aimed by an interactive computer application
NUM converting the dictionary into two modified finite state automata
NUM the phase of generating the sgml type
a polish to english text to text translation system based on an electronic dictionary
a specific algorithm is used for parsing verbal phrases
the first automaton stores single words
the second automaton stores the lexical phrases
this phase is executed automatically in order to optimise the access time
the grammar assumed in parsing polish expressions consists mostly of dgc rules
the solutions of these two problems will be sought in the near future
currently the temple prototype provides automatic raw nps1ish translations from documents in several languages spanish arabic japanese and russian
the commercial products for internationalization are designed to support the marketing of a tool in a specific set of foreign countries where the menus buttons error messages and text all need to be displayed in the appropriate foreign language
but dependency on a particular domain and type of text is a natural limitation of machine translation systems and a gbmt is no exception
a multilingual document editor the tipster editor for documents developed at crl under the norm projeco used to browse documents and their translation
the temple translator s workstation design is iginal in that it combines the best features and eliminates the weaknesses of competing alternatives
each target tree s lexical token is then sent to the morphological generator which produces the surface inflected form of each lexical token
finally gender is joined it is used when two successive characters are candidates of surnames
tagsets for semantic annotation would be derivable from a database of frame descriptions like the ones in figure NUM above
NUM while it fits the conception of chart parsing given at the beginning of this paper our generator does not involve string positions centrally in the chart representation
point NUM will have some importance for us because it will turn out that the indexing scheme we propose will require the use of distinct active and inactive edges even when the rules are all binary
note that reliability on narrative NUM NUM is good despite the small number of subjects
partitioning q by the NUM values of tj shows that qj is significant at the p NUM
when the edge for ran fast is moved the possibility arises of creating the phrase tan fast quickly as well as ran fast fast
for the static case at knowledge acquisition time we will consider the dif culties of merging unrelated knowledge sources
for information passing the dynamic predicate logic dpl results from an investigation of a dynamic semantic interpretation of the language of first order predicate logic and is intended as a first step toward a compositional non representational theory of discourse semantics NUM
using also the simple unification technique a resolution of the pronoun can then be tried out parts of the content information of the pronoun are going to be compared unified with specific parts of the content information of the possible antecedent
a recnrsion is defined on the right daughter the value of constr being a disjunction of punct att describing a sentence terminated by a fall stop and paragraph describing thus the recursion
the dynamic aspect resides in the fact that for this approach the meaning of a sentence does n t lie in its truth conditions but rather in the way it changes the information of the interpreter a
but before explaining the motiwltion of the grammar design on this point and the reasons for postponing the semantic untill tile process of refinement the semantic framework which has been choosen for the modelling of the cross seutential anaphora should be presented
where a linguistic description ld defining the constr uction type of a paragraph is associ ated with the tag p symbolizing the text type paragraph
ts is rule id lcb syn syn lcb constype phrasal lcb max yes constr paragraph rcb rcb rcb rcb p
this is possible because the interpretation of a sentence does n t lie in a set of assignments but rather in a set of ordered pairs of assignments where those pairs represent the input output states of a sentence
even if this first implementation is somehow primitive this will permit us to formulate some remarks about the allowed degree of modularity of grammar descriptions within alep and also about the way in which such descriptions can be extended
the breakdown columns show the percentage of examples that fall under each condition
additionally follow bos nextcats returns the set of category symbols at the beginning of strings and cos e nextcats indicates that cat may occur at the end of strings
this will allow us to determine the tag that we actually compare the per word geometric mean of the sentence probabilities
t bayes b and tribayes tb
table NUM shows the performance of the baseline method for the NUM confusion sets
for bayes this happens when none of its features matches the target occurrence
yet two level formalisms fell short from providing elegant means for the description of non linear operations such as infixation circumfixation and root and pattern morphology rcb as a result two level implementations e.g.
in order to minimize accumulator passing arguments we assume the following initially empty stacks parsestack accumulates the category structures of the morphemes identified and featurestack maintains the rule features encountered so far
NUM NUM synword clvc2vca pattern NUM synword ktb root measure m
where each probability on the right hand side is calculated by a maximum likelihood estimate mle over the training set
NUM in a few cases however trig rams does not get exactly the same score as baseline
category forms in the form of a list of lists where the ith item must unify with the feature structure of the morpheme affected by the rule on the ith lexical tape
our experiments showed that performance of the case based learning algorithm steadily improved as each of the available linguistic biases was used to modify the baseline case representation
in addition the best performing representation now outperforms the hand coded relative pronoun disambiguation rules NUM NUM vs NUM NUM at the NUM significance level
the approach takes a baseline case representation and modifies it in response to one of three linguistic biases by adding deleting and weighting features appropriately
the linguistic bias approach to feature set selec null tion simplifies and shortens the process of designing an appropriate instance representation for individual natural language learning tasks
in addition each case is annotated with one or more pieces of class information that describe how the ambiguity was resolved in the current example
this is in fact the case the final column in table NUM shows the effect of restricted memory limitations on the combined recency representation
while it appears that our existing linguistic bias set will be of use we believe that the cbl system will benefit from additional linguistic biases
in this section we will discuss how these factors relate to the selection of tone
the parser skips parts of the utterance that it can not incorporate into a well formed sentence structure
two trends have been observed from this evaluation as well as other evaluations that we have conducted
this also indicates that glr performance should improve with better speech recognition and improved pre parsing utterance segmentation
the source language input string is first analyzed by a parser which produces a language independent interlingua content representation
we analyze the strengths and weaknesses of each of the translation approaches and describe our work on combining them
the parser can ignore any number of words in between top level concepts handling out of domain or otherwise unexpected input
figure NUM shows an example of a speaker utterance and the parse that was produced using the phoenix parser
each concept irrespective of its level in the hierarchy is represented by a separate grammar file
the results were evaluated only for in domain sentences since out of domain sentences are unlikely to benefit from this strategy
the parser conducts a search for the maximal subset of the original input that is covered by the grammar
the interlingua is then passed to a generation component which produces an output string in the target language
the parsed speech recognizer outpnt is shown with unknown and unexpected words marked
each sentence 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
example NUM myhouse adverbial school case marking nea NUM pro title wa particle ga 1c wa is br ni
in type NUM processing adverbial particle wa is a proxy for pre nominal case marking particle no and the modifier with adverbial particle wa must be analyzed as the phrase which modifies the snbjective case with ga
t particle wa particlc ga ime expression with wa acts as an adverbial phrase june rain subjective much adverbial case marking phrase particle ga
from the association between a predicate and its modifiers in the dependency structure sentence analysis tries to determine the wflency strncture i.e. it determines for the valency pattern for the predicate which valency element ead modifier corresponds to
first the mmlysis tries to bind modifiers with case tam king parlicles we and ni to tile wdency elements in the wdency pattern for the predicate shoukai sent introduce which is oblaincd iyom lhc valency pattem dictionary
example NUM shows that kanojo she with ga is an objective case and kare he with wa is a subjective case in the sentence kare wa kanojo ga snkida he likes her
sentence having an adjective predicate in the japanese i mguage adjectives function as predicates in sentences as do verbs qlmrefore in this paper lhe sentence in which m adjective acts as a predicate is called a senlence having an mieclive predicate
the adverbial particle wa c m stand in for case marking p wticlc ga which is the non bound wfiency clcment mid the noun km e he satisfies the scmantic restriction on the subjcctive case n1 i.e. agent
the algorithm is embedded in a system that calculates the best classifications for all levels beginning with the highest classification level
the classification system has revealed some of the lexical structure of english as well as some phonemic and semantic structure
a linguistically significant layer around the word boys is one containing all plural nouns deeper layers contain more semantic similarities
the algorithm is an order of magnitude less computationally intensive and so can process many more words in a given time
similarly phrases like five pounds ninety nine pence could lead to different patterns of collocation for number words
the values are estimated as before using the frequency of the previous word to partition the conditioning context
we chose bigram models in this experiment so that we could make some comparisons with similarity based bigram models
for inst mcc file sense of smooth as in smooth silk will be a r mge on file texture scale
ill german it is possible to front non maximal verbal projections rcb NUM a erz hlen wird er seiner tochter tell will he his daughter ein m rchen
ll vform elided suhcat l eomp dtrs ll
phon john gives mary flowers and chocolates tool
analyses display the root pattern and all other affixes together with feature tags indicating part of speech person number mood voice aspect etc
using standard ol crations availablc through the lexc compiler and other finite state tools the analysis can be constructed according to the taste and necds of the linguists
after composition of the relevant transducers the intermediate levels disappear resulting in a direct mapping between the upper and lower levels shown
all valid stems currently about NUM NUM of them are automatically intersected at compile time at one level of the analysis
it had to perform efficient and accurate gen null eration of valid surface forms when supplied with the component root and relevant feature tags
forest of lexicon letter trees trees are connected by continuation classesy a letter path through the trees is an abstract word
a single underlying arabic word may be spelled many ways on the surface depending on how coinplctely the writer specilies the diacritics
a single system had to handle undiacriticized words and yet be able to take advantage of any diacritics that might be present
the system is based on lexicons and rules from an earlier kimmo style two level morphological system reworked extensively using xerox finite state morphology tools
if v represents a vowel then the intersection of the root ten plate and vocalic elements yields the same result
what patterns of informational relations are employed in realizing various kinds of intentions and what analysis provides a reliable means for identifying such patterns
in such a case the speaker intends that the hearer recognize a purpose but does not supply an utterance that manifests that purpose
to answer the question practically one would consider whether distinct intentional relations are useful for computational systems that generate and or interpret natural language
in fact the moser and moore discourse structure practical application of these intentional relations may be quite different in generation and interpretation systems
speakers intend for the intentions behind their utterances to be recognized and for that recognition to be part of what makes their utterances effective
whether or not such an informational structure is useful or is related in an interesting way to ils is a question requiring further research
here we argue that the problem is due to the inclusion of nuclearity in the definition of rst subject matter informational relations
the entire segment may be a single rst span with the g s core as nucleus and each subsegment as a satellite of that nucleus
the question may be approached from either an empirical or a practical perspective and the two perspectives may lead to different answers
the reason is that the same domain relation call it cause effect links a cause and effect regardless of which is the nucleus
this paper describes measures for evaluating the three determinants of how well a probabilistic elassifter performs on a given test set
the results are reported in table NUM
NUM examples of simple collision sets
6class based approaches are widely employed
figure NUM incremental learning experimental results
table NUM disambiguation algorithm learning phase
bigrams the accompanying noun determiner etc
their mcpi tends to acquire exactly the same value
the general test algorithm is defined in table NUM
the general feedback algorithm is illustrated in table NUM
where the probability is evaluated over the space of collision sets with cardinality NUM
first the system evaluates the translation results using the translated sentences which have been proofread
these probabilities are derived as follows
the probability is normalized by taking the k th root k is the length of the sequence
the best case for a model of size n is the division into n NUM classes of size NUM
it is NUM NUM thus again lower than the perplexity of the bigram model see table NUM
the algorithm covers vp ellipsis illustrated in NUM pseudo gapping in NUM bare ellipsis involving sequences of bare arguments adjuncts or both in NUM and gapping in NUM
the algorithm we propose implements the second view of ellipsis by characterizing ellipsis resolution as the specification of a relation of possibly partial correspondence between the lexically unrealized head of an elided clause and its arguments and adjuncts as one term of the relation and the realized head of the antecedent clause and its arguments and adjuncts as the second term
p g plays john d vj ff plays john ff m r and g j plays j ohn beautifully
the bare np chocolates is the head of the elided clause in the second conjunct of NUM the generalized ellipsis reconstruction algorithm will identify gives as the head v of the antecedent clause in the first conjunct and then will fill one of the positions in its subcat list with the local features of chocolates
as we have seen the proposed generalized reconstruction algorithm does handle bare ellipsis structures like NUM in cases like NUM the algorithm will substitute the head v of the antecedent for the empty verb of the elided clause and the bare pp adverb will modify the vp headed by this verb
let an ellipsis fragment be a phrase which i occurs outside of a lexieally realized sentence and ii is interpreted as an argument or an adjunct of the head verb of a non elided sentence
as it is not possible to posit an unbounded number of free adjunct function variables in the semantic representation of a verb vp it seems that the higher order unification analysis can not deal with these cases
NUM the assumption that all input samples retain their viterbi path after merging
we consider constraints that divide the states of the current model into equivalence classes
again we find the discontinuity at the point where the constraint is changed
several steps of merging between model b and c are not shown
i would like to thank christer samuelsson for very useful comments on this paper
the thin lines show the further development if we retain the the same output constraint until
when applying model merging one can observe that first mainly states with the same output are merged
it assigns a log perplexity of NUM NUM to the training part and NUM NUM to the test part
using terminological knowledge representation languages to manage linguistic resources
1in the examples that i will consider and in most examples used by linguists to test alternation patterns there will only be one verb this is the verb to be tested
the have instances of test for an alternation searches a corpus of good sentences or bad sentences and tests whether at least one instance of the specified alternation for example a benefactiveditransitive is present
a bad sentence with all the required verb arguments will classify as an alternation despite the ungrammatical syntactic realization while a bad sentence with missing required arguments will only classify as a subcategorization
the have no instances of test for a subcategorization searches a corpus of bad sentences and tests whether at least one instance of the specified subcategorization for example transitive is present as the most specific classification
for example the property non consumable small capitals indicate classic concepts in my implementation specializes a liquid entity to define paint and distinguish it from water which has the property that it is con sumable
consider the sentences and descriptions shown below for pour NUM a mary hi poured tinaobj a glass of mflkio
the usual practice in investigating the alternation patterning of a verb is to construct example sentences in which simple illustrative noun phrases are used as arguments of a verb
consider the tasks that confront a lexical semanticist
here i explore additional linguistic data management tasks
the interactive track has a double goal of developing better methodologies for interactive evaluation and investigating in depth how users search the trec topics
dipt one units are most corn
automatic speech segmentation procedures are powerfill tools for including new synthetic voices and for updating and supplementing existing diphone libraries whereas manual diphone segmentation is a tedious time consuming task prone to errors
once the appropriate phonetic symbols and prosody markers are determined the final step is to produce audible speech by assembling elemental speech units computing pitch and duration contours and synthesising the speech waveform
we expect the whole process of creating a new wfice to be semi automatic with manual correction of stopconsonant boundaries allowing the synthesiser to be retrained on a new voice in less than NUM days
we start with a brief overview of the different modules el the slovenian tts system then we go on to describe how the existing diphone inventory was obtained
we begin in a with a single
iterate until the field can not be improved
verbs and nouns were lemmatized to their root forms if the root forms were attested in the corpus
the distribution ql is the best compromise possible
these two trees are given in figure NUM
could we improve the fit by increasing ql
however something has actually gone very wrong
some derivations fail but throwing away failed derivations has the effect of renormalizing the weight function so that we generate a dag x with probability p0 x as desired
it can be shown that proposing items with probability p and accepting them with probability a x yields a sampler for q
the next question is how to estimate weights
we proceed to expand the newly introduced nodes
as natural language generation systems become more complex and sophisticated the mode of input to these systems is becoming correspondingly more difficult to specify and manage
the expected numbers of tokens of infinitives and plurals for types unseen in the training set using the overall mle are denoted as eo no inf and eo no pl the corresponding estimates using the hapax based mle are denoted as eh no irlf and eh no pl
this trend is captured by the solid nonparametric regression line an explanation for this trend will be forthcoming in section NUM it will be noted that in figure NUM the variance is fairly small for the lower frequency ranges higher for the middle ranges and then small again for the high frequency ranges anticipating somewhat we note the same trends in figures NUM and NUM
the same applies to measurement of rms maximum
is indicated with o the curves of the previous experiment are shown in thin lines
the NUM corpus is a multiqayered structured corpus constructed on top of the frameix d knowledge representation language
the inherited feature similarity measure ifsm is another integrated approach to measuring simila rity
the man can be used to express any descendant of the concept man
fhus the fiat probability grouping method can precisely control the lew j of abstraction
lesnik inlroduees a class probability associated with nodes synset s in
here we adopt an automatic extraction of features and their weightings from a large text corpus
fi altu es and i ases similarity on otnl aril g
measure for prelimit ary ewjtmti m of otu al ljroaeh
a set of typed surface triples are extracted from a corpus with their frequencies
these include phrases like let s see let me see i do n t know when they occur with no overt or implied verb phrase argument
for example ficu assignment depends in part on filtering out clausal interjections utterances that have the syntactic form of clauses but that function as interjections
results using top down prediction of possible word hypotheses by the parser work inspired by kita et
in the h llowing we use record notation to refer to subcoml oncnts of an object
since we process best first inside the beam the maximum is known when the first triple is inserted into an agenda
these two arguments are similar in nature but diflhr in the architecturm levels that they apply to
the problem even gets worse if what is to be fine tuned is the interaction between several complex modules
however pause and cue rely on cues that are relevant at the local as well as the global level and consequently assign boundaries more often
an edge i consists of flora the start vertex and to a list of end vertices
however nps and pronouns are treated differently based on the assumption that the referent of a third person definite pronoun is more prominently in focus cf
this paper reports on research done in our group which belongs to verbmobil s subprojeet on system architectures tp15
see figure NUM for fallout and error
else cue prosody has the same values as pause
there are NUM phrases hence NUM boundary sites
the two authors coded independently and merged their results
computational linguistics volume NUM number NUM NUM NUM NUM discussion
then we propose and evaluate methods for combining them
after sentence final contour sentence final contour
we also evaluate a simple method for combining algorithms
so phrase NUM NUM is not coded for np features
we will discuss the complexity implication of each of our enhancements to the algorithm
this is the method used in the first column of results in table NUM
second we manually collected the body potion of NUM newswire articles for each category NUM documents in total
as an alternative technique to ocr there is word shape token processing which converts images into a shape based representation
as a result the process of word shape token generation from images is much faster than current ocr technology
relations can not be a ssigned unambiguously on the basis of morpho syntactic informa ion only in the sentence il bambino leggc il libro the child reads the book agreement information is not decisive for soa sin y both nominal constituents agree with the w rb
fumo s smoke s marea s tide s
the patterns considered so far do not exha ust
patterns general sing over a etua lly
a core NUM a ttern is extracl ed
knowledge of e v 2n the significance of the divergence for the second NUM measurement points is not available
the results were demonstrated in muc NUM where the modified system achieved excellent results particularly in the coreference task
this work will be focussed on improving the user s ability to identify news articles which contain information such as what a certain person has said about a certain topic who of significance has traveled to specific places who has specified kinds of relationships with certain kinds of companies
nyu uses a modified version of nist s prise engine in its experiments
finally we intend to continue to contribute to the contractor architecture working group and other common efforts and complete the tipster application systems
systems the intelligence analysts associate iaa under funding from rome laboratories for naic and the overture program for oir
enhancements to extraction nyu s proteus extraction system was modified to use combined syntactic and semantic pattern based methods
that review i neither constitutes cia authentication of information nor implies i cia endorsement of the author s views
sra engages in an active program of research and operational support in both multilingual data extraction natural language processing more generally and text retrieval integration
evaluations sra participated in the sixth message understanding conference in three of the four areas named entity template element and scenario template
a simple solution to eliminate inter annotator inconsistency is to train and test the model on data that has been created by the same annotator
and maintains as it sees a new word the n highest probability tag sequence candidates up to that point in the sentence
the model can be classified as a maximum entropy model and simultaneously uses many contextual features to predict the pos tag
a simple consistency test is to graph the pos tag assignments for a given word as a function of the article in which it occurs
in order to conduct tagging experiments the wall st journal data has been split into three contiguous sections as shown in table NUM
furthermore p wilti for unknown words is computed by the following heuristic which uses a set of NUM pre determined endings
specialized features for a given word are constructed by conjoining certain features in the baseline model with a question about the word itself
and the vocabulary of content words is umch larger than that of functional words
about the situation described by the clause
and the individual NUM NUM rules resulting fron linding a consistent generalization are asserted in the ii bp granmm r
aux name vp we will be in trouble when attempting to locate the misorderings in any negative example
for nominals it must be provided directly in terms of svntactic roles
analysts averaged eight minutes per article for annotation including review and correction
saic created language versions of the scoring program and provided technical support throughout
the met results have been quite instructive from a number of different angles
so it is possible to have either generalization or unification but not both within the same feature system at least with this encoding
exactly what this is does not matter if there is no intuitively available notion it can be decided on an ad hoc basis
unfortunately however anding of bitstrings is not the kind of operation that is directly available within the unification formalism we are compiling into
if it were then we should expect the following combinations to yield the same result where g and u represent generalization and unification
in order to do this we take advantage of the fact that our formalism allows us to write rules with variables over lists of daughters
this paper describes various techniques for enriching unification based grammatical formalisms with notational devices that are compiled into categories and rules of a standard unification grammar
but if all the possibilities are excluded then we have an impossible structure and we want this to be reflected by a unification failure
computational linguistics volume NUM number NUM now a sequence det ad31 adj NUM adj NUM n will be parsed having the following structure null
firstly the various different orderings are not properly encoded here because p q is logically equivalent to q g p
what we need to do is to make the np select the appropriate prepositional semantics representing all the choices within a single lexical entry
instead we let the pool be as large as practically possible
additionally if necessary the v v test engineer will determine that shared components and modules interface as required both in isolation and in combination with the other components and modules
the three elementary trees for conjunct ck have the same internal structure root ck with three children that correspond to the disjuncts dkl dk2 and dk3 in each of these NUM
the stsg has start symbol s two terminals represented by t and f non terminals which include beside s all ck for mula does not contain repetition of conjuncts
another solution might involve assuming memory based behavior in directing the search towards the most suitable parse according to some heuristic evaluation function that is inferred from the probabilistic model
for each boolean variable ui NUM i n construct two elementary trees that correspond to assigning the values true and false to ui consistently through the whole formula
for example tgi e and igi n a for n NUM are comparable but for n NUM the polynomial is some NUM times faster
and the probabilities of the elementary trees rooted with s are adapted from the previous reduction by substituting for NUM every NUM NUM the value
thesaurus and is thus expected to yield a better estimate on the genermity of node p tile idea is shown in figure NUM the coefficient a is rather ditlicult to handle and wc will touch oil this issue ill section NUM NUM
having a relatively small number of trains NUM dialogues for training we wanted to investigate how well the data could be employed in models for both the sr and the speechpp
when a speech act is planned for output it is passed to the generator which constructs a sentence and passes this to both the speech synthesizer and the display
a review of the transcripts for the unsuccessful attempts revealed that in three cases the subject misinterpreted the system s actions and ended the dialogue believing the goals were met
these rules allow robust processing in the face of partial or ill formed input as they match at varying levels of specificity including rules that interpret fragments that have no identified illocutionary force
the final interpretation of an utterance is the sequence of speech acts that provides the minimal covering of the input i.e. the shortest sequence that accounts for the input
another area where we are open to criticism is that we used algorithms specific to the domain in order to produce effective intention recognition disambiguation and domain planning
for instance one rule would allow a fragment such as to avon to be interpreted as a suggestion to extend a route or an identification of a new goal
when there exists a strong relationship the word candidate has high probability to be a content word
when both have low weights the score of the second character of a name part is critical
the average recall tells us that the tri g ger to the identification system is nsefnl
the following sections show how to modify the basic idea if a large scale corpus is not available
for example j may denote a city or a former american president
the company name i t is a typical example
the difference shows that the former is easier to be used as the other words than the latter
i p w gn p gn p wign
text provides many useful clues from three different levels say character sentence and paragraph levels
using the features in figure NUM
discourse segmentation by human and automated means
and that sound was really prominent
reliability labeling from text alone is NUM
since reliability was lower than the NUM
figure NUM shows two bar charts
all subjects assigned boundaries relatively infrequently
our first goal is purely exploratory
NUM NUM characterizing the notion of a segment
NUM NUM NUM NUM NUM email hercules dsv su se NUM usc information sciences institute NUM admiralty way marina del rey ca NUM NUM usa ph
including the two last texts the ratio syntactic aggregation cases total sentences was approximately NUM i.e. one third of the sentences included syntactic aggregation
an example of bounded lexical aggregation with a cue word is retail sales excluding auto dealers have remained practically unchanged since last june statistics canada said
the total amount of words in the first nine texts were NUM NUM words and the ratio syntactic aggregation cases total words was NUM NUM
we call the elements that will be aggregated the aggregands and the element the lexeme which is the result of the aggregation the aggregator
aggregated texts sometimes need cue words e.g. each together separately both to clarify the aggregation see example NUM next section
john hit tom on monday tom kicked john on tuesday john punched tom on wednesday tom hit john on thursday john hit tom on friday tom kicked john on saturday
only by means of intonation can the user interpret the system s expectation correctly and react accordingly
on the lexicogrammatical stratum the mood systems are the central resource for expressing these speech functions
thus the disorder may be identified in the description of the patient e.g.
it will not be quite that automatic however further distinctions are needed
order to bring about recovery table NUM part of frame semantic tagset for the
either the input or the output symbols can be null a null input symbol is used for an insertion of a phone a null output symbol for a deletion
it is possible for a transduction to fail by finding no next transition to make but this occurs only on bad input for which no output string is possible
it is important to note that the learning algorithm did not have any knowledge of the concepts of vowel and consonant other than through the features used to calculate alignment
finally we should make a few remarks on the scope of our intended effort
the only difference between this transducer and the hand drawn transducer of figure NUM is that the arcs leaving state NUM go to state NUM rather than looping back to state NUM
the entire output string of each transduction is initially stored as the output on the last arc of the transduction that is the arc corresponding to the end of string symbol
the only difference between underlying and surface forms in both the training and test sets in this experiment is the substitution of dx for a t in words where flapping applies
would have to recognize susan as playing slightly different roles in the two associated frames
giving such knowledge to ostia would allow it to hypothesize that if every vowel it has seen has acted a certain way that the rest of them might act similarly
the algorithm also successfully induced transducers with the minimum number of states for the t insertion and t deletion rules in NUM and NUM given only NUM NUM samples
indicating the selectional and syntactic properties of the constituents that can instantiate them
table NUM a matrix representation of a hypothetical example
table NUM kappa coefficients for judgements on sentence importance
the original f structure and its component parts inherit the qlf semantics via r
d ek does not need a stamp
this latter information is determined through the algorithm explained in section NUM NUM
these are extensional entities individuals or collections of individuals plurals
the core grammar for this phase is domainindependent
we can not use word based language models
the second step is the most important
iso NUM is one such coding systems
result strings are shown in figure NUM
we made heuristic rules to map a coding system to possible languages
their method however can not directly applied to our problem
figure NUM extraction of eastern asian characters
a character is normally encoded with two or more bytes
it is reasonable to make use of these dictionaries in bilingual text alignment
bilingual dictionaries are now widely available on line due to advances in cd rom technologies
as a result it can align only some of the two texts
however their main targets are rigid translations that are almost literal translations
categories of matches by manual alignment and indicate the difficulty of the task
this step constructs an am when given an asm and a bilingual dictionary
intuitively true correspondences are close to the diagonal linking the two anchors
a word combination exceeding a predefined threshold is judged as a word correspondence
these pre processing operation can be easily implemented with regular expressions
their methods bypassed aligning sentences and directly acquired word correspondences
in this case the legal topic medical malpractice i.e. medmal3 is selected see figure NUM
hence the probability for unseen types to appear is lowered
sentence of probability q for the word graph wg lcb t f rcb 3m
NUM k rn and both literals of each boolean variable of the formula of ins
the np hardness of computing the mps from a word graph also holds for stochastic context free grammars scfgs
each of these elementary trees corresponds to the conjunct where one of the three possible literals is assigned the value t
the two elementary trees for u of our example are shown in the top left corner of figure NUM
where is the missing mass for ql
combining features to create more complex features
field induction begins with the null field
if these were the only two choices
there have been on the other hand some researches that aim to equally treat lexical information of nouns and that of verbs see e.g.
the scpf code in the causative auxiliary verb saseru has two ambiguities of the set of permutation commands as is shown in fig NUM
we proposed a knowledge representation framework for verb subcategorizations with combinatorial codes for the verb s surface case frame and deep case thematic role frame
the number of different surface deep case mapping types is NUM after we completed the new subcategorization frame code development for NUM NUM verbs and adjectives
computational japanese lexicon for mt we have developed a computational japanese lexicon with more than NUM thousand words NUM thousand of which are verbs and their derivations
the numbers of the variations of subeategorization frames in the lexicon was about NUM for ordinary verbs and adjectives and we have NUM more for idiomatic ones
the maximum number for n is actually set to three in our mt system reflecting the numbers of auxiliary verbs in real utterances and written sentences
a key part of the development was to establish word senses by means of comparing synonymous vocabulary sets of japanese and english nomura89
the analyzer generates the subcategorization frame for the entire predicate by applying the permutation commands developed from the scpf code for one auxiliary verb at a time
the second permutation is performed for the second auxiliary verb next to the first auxiliary verb and the focus moves on to the second attxiliary verb
NUM below show which variables from the two parts are to be identified
we proposed eliminating this by means of a bit vector and the same technique applies here
NUM because all our rules are binary we make no use of active edges
the distinguished index identifies this as a sentence that makes a claim about a running event
when the entry for john is moved no interactions are possible because the chart is empty
points NUM and NUM are serious flaws in our scheme for which we shall describe remedies
we therefore say that p is internal to the sentence the tall young polish athlete ran fast
the words newspaper and fast can also be deleted independently giving a grand total of NUM strings
they cause the first and third edges in NUM to be added to the agenda
this strategy does not prevent the generation of an exponential number of variants of phrases containing modifiers
the levels also represent the encapsulating powers of each japanese
ee k n this formula can be infinitely recursive depending on the properties of the grammar
when analyzing long sentences with two or more predicates i.e.
however we can not calculate this quantity since in order to do so we would need to completely parse the sentence
in this paper we consider probabilities primarily based on probabilistic context free grammars though in principle other more complicated schemes could be used
when we are finished processing one constituent a new one is chosen to be removed from the keylist and so on
our tritag probabilities for the trigram and prefix estimates were learned from this data as well using the deleted interpolation method for smoothing
let g k be the set of all completed edges or rule expansions in which the nonterminal nj k appears
some figure of merit is assigned to potential constituents and the constituent maximizing this value is the next to be added to the chart
traditionally the keylist is represented as a stack so that the last item added to the keylist is the next one removed
this preference assigns priorities to the possible structures of tile sentences
the conjunction lewis we introduced above reduce the syntactic ambiguities of long sentences
the aim of this paper is to understand why this deviation between the wundtlaan NUM NUM xd nijmegen the netherlands
the sentence structure influences a wide range of linguistic phenomena
wc have decided NUM o develop a rule based i xggcr l rcb causc such a
proper names differ from ordinary words in that there are relatively few computational linguistics volume NUM number NUM proper names that are highly frequent in comparison with words in general but there are large numbers of types of names that occur rarely
this variance pattern follows from the high variability in the absolute numbers of types realized especially in the middle log frequency classes in combination with the assumption that for any log frequency class the proportion for that class is itself a random variable
given a form that is previously unseen in a sufficiently large training corpus and that is morphologically n ways ambiguous serves n different lexical functions what is the best estimator for the lexical prior probabilities for the various functions of the form
as we shall argue this is because when one computes an overall measure one is including high frequency words and high frequency words tend to have idiosyncratic properties that are not at all representative of the much larger mass of productively formed low frequency words
the translation component consists of a t arsing module and a generation module
average duration conlidence interwtl and standard deviation of the population for both manual and automatic segmentation are presented in table NUM
using a phonetically labeled vocabulary a baum welch training procedure is applied and parameters of mono null phone models ark obtained
the inflection code of the polish part of an entry is a reference to a set of inflection endings stored in one of classification files prepared in phase NUM
s in source language j i r i ou fgenkit enerator
we consider each rule p in z in order and correspondingly elaborate the machine so as to reproduce the rule s effect
because the initial phrase labeling is only approximate the string is broken into two sub phr es separated by of
as noted above this process occurs in two main steps an initial labeling pass followed by the application of a rule sequence
this mixed mode acquisition is unique among natural language learning proccdurcs mid we put it to good use in building our multilingual name tagging sequences
the basic framework we describe has somewhat less power than a finite state machine and yet achieves high accuracy on standard phrase parsing tasks
the phrase finder with these examples as background we may now turn our attention to the technical details of the phrase finding process
hand crafted rules we first approached this task as an engineering problem and wrote a rule sequence by hand to identify these named entities
the approach we have taken towards discovering phrase rule sequences automatically is a maximum error reduction scheme for selecting the next rule in a sequence
most important they support mixed mode acquisition the rules are both easy for an engineer to write and easy to learn automatically
what is important is that the initial phrase identification fred the cores of phrases reliably even if complete phrases arc not identified
substitutions i.e. prob o i s NUM i i prob oi i si
the actual utterance was okay now let s take the last train and go from albany to milwaukee
the last speech act could be a suggestion of a new goal to move from detroit to washington
hence whether the sr s models are tunable or not the post processor is in neither case redundant
it uses templates associated with different speech act forms that are instantiated with descriptions of the particular objects involved
it could be disastrous to combine two speech acts that arise from i really garbled think that s good
it is also based on a set of prioritized rules this time dealing with plan corrections and extensions
in other cases the user might persevere and continue with another correction such as no through cincinnati
robustness arises in the example because the system uses its knowledge of the domain to produce a reasonable response
the correctness of the above program can be shown by mapping all the characters not in 2r to e because they have idenitical state mmsition wdues i.e.
this can be tone in o ipld by assigning null characters to the locations in f corresponding to NUM byte characters in p
determining the semmltic relation helps us translate the japanese double subject construction into the appropriate english construction and expression
some encoding schemes are designed to encode texts that contain characters from two or more character sets
some technical points the linguistic levels proposed in this exercise do not create any problem as for the linguistic surface forms which are very simple
since a document moves from collection to collection each process only depends upon the documents in its collection
information is passed to each process via collections stored within the tipster compliant document manager dm
in this paper we have presented erel a language education and rehabilitation system for autistic children developed from the generic illico system
the document viewer allows one to modify problem tags based on system supplied corrective actions
a collection contains the information necessary for a process to perform i.e. documents and document relevant information
if system suggestions are rejected tag values can be generated from user supplied data
after the problems associated with a document are addressed the document can be resubmitted to the system for reprocessing
these servers receive streams of documents from currently five sources providers nexis dialog datatimes fbis and newswire
adept will be installed in oir s testbed environment in december NUM where it will undergo a three month evaluation period
the sam process provides the capability to create modify and associate mapping templates with a specific data source
in these experiments the length of phrases was limited to maximum NUM words
in cases where users could not achieve satisfactory results by using and helping the system the human expert would take charge of part ot the translation
then comes the ambiguity type structure comm act class meaning target language reference address situation mode and its value s
in such contexts the automatic analyzer can not fully and reliably disambiguate a sentence or an utterance and the best available heuristics do n t select the correct results often enough
for example what is the use of defining a system of NUM semantic features if no system and no lexicographers may assign them to terms in an efficient and reliable way
take the example given in i1 NUM above ok l so go back and is this number three i right there i shall i wait here for the bus
that could be different in a context where state could be construed as a proper noun state for example in a dialogue involving the state department
for example the kernel header ambiguity i10a NUM NUM NUM identifies kernel NUM in dialogue emmi 10a noted here emmi10a
we do n t elaborate as ambiguity patterns are specific to particular representation systems and analyzers so that they should not appear in our labeling
finally an ambiguity pattern is a schem i wfth variables which can be instantiated to a usually unbounded set of ambiguity kernels
when we define ambiguity types the linguistic intuition should be the main factor to consider because it is the basis for any disambiguation method
in an abstracted form the size of each lhature space becomes tractable
in addition the student may be offered control over parameters of the systems behavior including subject matter difficulty and style
one parameter of an exercise can be a plan for a goal in a situation a capacity that makes exercises portable across microworlds
the teacher interacts with the system through graphical gui tools that facilitate the designing of exercises and the construction of appropriate microworlds
it will not be easy but i hope and believe that before too long there will be a variety of exciting effective nlp based call systems
automated aid has been undertaken lot parts of languages all the way from spelling pronunciation and morphology through syntax and semantics to discourse and cultural knowledge
the system makes this possible by tightly coupling the language to graphical acts and system generated animations within a realistic ongoing situation
in this note a straw man is destroyed optimism is expressed an existing system is sketched and some issues are laid out
portability across languages is familiar to nlp researchers and as noted above portability can also refer to moving exercise types into new situations
awe describe here the terms which are relevant to this paper
NUM two level analysis l i i NUM l invalid partition
context is specified the relevant bit is set
r2 and r3 sanction stem consonants and vowels respectively
r1 is the morpheme boundary rule
t and applying the appropriate vocalism lcb ae rcb
the two level grammar listing NUM assumes three lexical tapes
an algorithm for the interpretation of multi tape two level rules is described
thus the length of chinese personal names ranges from NUM to NUM characters
this section introduces all the corpora that are used in the following sections
most chinese surnames are single character and some rare ones are two characters
recall that it is generated by a rough word segmentation system wlthoul manually checking
when another string rcb i ljl rcb
NUM voice which handles departures from the default core syntactic structure triggered by the use of syntactic alternations e.g. passive or dative moves
as prompted by the needs of new applications and by our better understanding of the respective tasks of syntactic realization and lexical choice NUM
and NUM ca es in the evaluation pool
all instances of pps that are attached to vps and nps were extracted
the probabilities were estimated fi om all the pp cases in the training set
a median of NUM of the pp cases were attached to noun2
NUM adjust each cell entry by multiplying it by the scaling factors
a point of terminology i will use the term grammar to refer to an unweighted grammar be it a context free grammar or attribute value grammar
following NUM the thematic roles accepted by surge in input clause specifications first divide into nuclear and satellite roles
as it stands surge provides a comprehensive syntactic realization component easy to integrate within a wide range of architectures tbr complete generation systems
surge represents our own synthesis within a single working system and computational framework of the descriptive work of several non computational linguists
surge is implemented in the specialpurpose programming language pup NUM and it is distributed as a package with a pup interpreter
nominals are an extremely versatile syntactic category and except for limited cases no linguistic semantic classification of nominals has been provided
it provides an interface to the client program is in terms allen s temporal relations e.g. to describe a past event
since many of these sources belong to the systemic linguistic school surge is mostly a functional unification implementation of systemic grammar
developed over the last seven years NUM it embeds one of the most comprehensive computational grammar of english for generation available to date
to illustrate consider the case in our example in which the probability of the coreference configuration a b d c is determined
the cross entropies of the learned maximum entropy models and the training data were notably better than those for the evidential model at about NUM NUM in each case
natural language information extraction ie systems take texts containing natural language as input and produce database templates populated with information that is relevant to a particular application
characteristics of context for template coreference we now need a set of possible characteristics of context on which the algorithm could choose to conditionalize in deriving the probabilistic model
in practice we will not want to incorporate constraints for all of the features that we might define but only those that are most relevant and informative
however if there are more than two templates we must utilize the pairwise probabilities to derive a distribution over the members of the set of coreference configurations
the second approach we consider models the likelihood of correctness of decisions that a template merger such as the one used in fastus would make in processing a text
template names grouped within parentheses are taken to be mutually coreferring we will refer to such a grouping as a cell of the coreference configu null ration
on the other hand definite noun phrases are often not referential to items evoked in the text e.g. the ammunition depot in fairview
while the entity name expressions were relatively difficult to handle the number numex and time timex expressions encompassing the tag subtypes percent money and time date respectively were handled proficiently by the participant systems
location expressions were typified by entities that likely would be contained in a gazetteer or similar on line resource
references to location frequently appeared within phrases that might or might not subsume the location under another tag
furthermore a correctly identified organization such as l diet was directed by the guidelines to be tagged as location when the context in which it was used indicated that the diet was a facility or structure i.e. if a press conference were being held there
they handled comparatively easy types of expressions with a high NUM degree of accuracy and the hard expressions with surprising proficiency thereby promising marked improvement in the near term and the capability to work in conjunction with other language processing technologies such as machine translation mt and text summadzarion NUM
to complicate matters further once a complex np like j jx miti telecommunications subcommittee is determined to be a proper noun the systems next were required to tag as organization each constituent part of the hierarchical relationship expressed within the phrase
in addition the met japanese guidelines NUM stipulated that fractions such as NUM NUM 0d NUM NUM which are easily calculable as percentages should also be identified
date the japanese participant systems processed date expressions successfully despite the denmnds made by the met japanese guidelines NUM concerning what should be tagged and the wealth of patterns used to represent those expressions
it also introduces an expert coder by the back door in assuming that the majority is always right although this stance is somewhat at odds with passonneau and litman s subsequent assessment of a boundary s strength from one to seven based on the number of coders who noticed it
although NUM and kid s use of NUM differ slightly from litman and hirschberg s use of NUM NUM and NUM in clearly designating one coder as an expert all of these studies have n coders place some kind of units into m exclusive categories
they cite pairwise agreement percentages figured over all thirteen categories again looking at each of the three naive coders separately
when there is no agreement other than that which would be expected by chance k is zero
where no sensible choice of unit is available pretheoretically measure NUM may still be preferred
unfortunately as a field we have not yet come to agreement about how to show reliability of judgments
human communication research centre NUM buccleuch place edinburgh eh8 9lw scotland computational linguistics volume NUM number NUM
or in words NUM of the time the first coder chooses the first category with a NUM
the resulting system did quite well
noun group syntax remains explicit as one phase of pattern matching
these benefits are lost when we encode individual semantic structures
in any case problem NUM still loomed
the defclausepattern procedure performs a rudimentary syntactic analysis of the input
using defclausepatvern the resulting pattern is then analyzed and its clause level syntactic variants are generated
the user can then manipulate the pattern generalizing pattern elements and dropping some pattern elements
the approach we have adopted has been to introduce clause level patterns which are expanded by metarules
in this paper we present a method for measuring both corpus similarity and corpus homogeneity
the definition only serves to show how heterogeneous a collection of objects the word denotes
the third point is that it is a pre requisite to a measure of corpus similarity
all the same language varieties are represented in each corpus and in what proportions
like a corpus a text can be large or small heterogeneous or imlform
a corpus can contain complete texts or sampled texts as in the brown corpus
the glr unification based formalism allows the grammars to construct precise and very detailed ilts
this result can be explained by the folh wing statistic
figure NUM word error of the base system and mne
the path set is a set of strings in
however these can be eliminated in turn using lemma NUM
the operations of substitution and adjunction are discussed in detail below
substitution replaces a node marked for substitution with an initial tree
as traversal proceeds the left context grows larger and larger
there are many ways that the tig formalism could be extended
schabes and waters tree insertion grammar figure NUM left to right tree traversal
as written in figure NUM the algorithm is a recognizer
however one must be careful to meet the requirements imposed by tig while doing this
a cfg is lexicalized if every production rule contains a terminal
in this case the content word is not a keyword so that no organization name is found if these characters can not exist independently they form the name part of an organization
verb level noun level and noun definiteness and it included all second order interaction terms
a contingency table is a matrix with one dimension for each variable including the response variable
for some loglinear models it is possible to obtain closed forms for the expected cell counts
in this section we summarize the results obtained from query expansion and other related experiments
the fbllowing sections present a solution in that morphological wellformedness conditions are
stated at a separate eonlponent the mortfl ology projection
english does not have this option but employs an adjunct clause instead
this is the subject of ongoing work
nothing blocks the presence of two haben ill NUM
a limit of NUM minutes per query in a single block of time is observed
let mcpl ei be the mutual conditional plausibility NUM of ei the posterior probability of el pposti is defined as the evaluation of each learning step is carried on by testing the syntactic disambiguation on a selected set of corpus sentences where ambiguities have been manually solved
the described method is a combination of numerical techniques e.g. the probability driven mcp1 disambiguation operator and some logical devices a shallow syntactic analyzer that embodies a surface and portable grammatical competence helpful in triggering the overall induction a naive semantic type system to obviate the problem of data sparseness and to give the learning system some explanatory power the interaction of such components has been exploited in an incremental process
rewrite the collision sets of o removing hell eslls into a new set of observation o NUM NUM NUM replace o with o until pcf pcf NUM stop let cs lcb el e2 en rcb be any collision set in the corpus where e s are esl s let be the prior probability pprior
truepositives otherwise falsepositives otherwise if NUM r then if e is correct pp or hen falsenegatives otherwise true negatives ncases precision truepositives truene atives truepositives truenegatives falsepositives falsenegatives recall truepositives truene tatives ncases coverage truepositives truene atives falsepositives falsenegatives ncases
these clues mostly lexical markers combined with syntactic structures are easy to spot and can provide a first set of detection heuristics
in this paper we propose a textual clue approach to help metaphor detection in order to improve the semantic processing of this figure
our future works will focuss on the study of the relation between the metaphors introduced by a clue and others that are not conventional
this must not be the only disambiguation tool but when no other is avalaible it provides nlu systems with a probabilistic method
if textual clues give information about possible non literal meanings metaphors and analogies one may argue they do not allow for a robust detection
together these closed class forms determine the main structural delineations of the depicted scene and of the speech context in which it is uttered that there is one agent acting upon two or more objects that this action takes place at a time before my telling you about it and that i assume that you already know of the affected objects i am referring to but that the agent is here newly introduced
thus in english bilateral symmetry may be represented solely by each other they kissed each other rotation only by around and over the pole spun around toppled over and dilation only by in and out the gel spread out shrank in
these determine the basic contents of the depicted scene e.g. here a western landscape in which a cowboy whirls a loop of rope and flings it over the heads of male bovines neutered and bred for human consumption in order to stem them from their owner
the preliminary finding is that each cognitive system has some structural properties that may be uniquely its own some further structural properties that it shares with only one or a few other cognitive systems and some fundamental structural properties that it has in common with all cognitive systems
two attributes are added to textual clues related to metaphors corresponding to the elements of the sentence bearing the source and the target
the guideline is that novel metaphors not introduced by a clue at the sentence level may have been introduced previously in the text
in brief the first three of these which work together in a complementary fashion are configurational structure the schematic delineations that partition the spatial and temporal dimensions of a referent scene perspective point the conceptual location from which one regards a referent scene and distribution of attention the pattern in which some elements of a referent scene are placed in the foreground of attention and others in the background
previously infer was a relation between the referent of an np in one utterance and the referent of an np in a previous utterance
a parsing cycle corresponds to a new time point related to the utterance in every cycle a new vertex is created and new word hypotheses ending at that time point are read and inserted into the chart
in highly distributed systems we generally tind the following levels of control system control the minimal set of operating system related actions that each participating module must be able to per brm which will typically include means to start up reset moni tot trace and terminate individual modules or the system as a whole
in addition since our segmentation task is not hierarchical we do not note whether phrases begin end suspend or resume segments
the architecture shall allow assistance to the native speaker of english in formulating detection criteria in a foreign language
these criteria are usually originated by the user however they may be integral to the specific application
it does not need a dictionary and thus is free of any restrictions
it can be implemented in hardware and serve in real time speech recognition systems
these results are the consequence of two contradictory features of the greek language a
another dimension of the analysis of the results is the domain of the experiment
for dutch the model gives relatively good results NUM NUM for four output candidates
instead of using probabilities we used their negative logarithm thus yielding distances
at this point we had to make decisions taking into account implementation specific parameters
the values of the parameters of the model are in the range of NUM
this is the primary cause of the errors introduced in the experiments with german
o ganization of lhe i of level NUM shown in table NUM the word panel rnight loe used as panel board
it can analyze a NUM word sentence on a pc in less than one second while using less than half of a megabyte of memory
for plural first person pronouns we and us we resolve to the nearest organization or set of persons
there are some obvious heuristics that we have implemented such as generalizing NUM to number and garrick to person
in the meantime we are using the theory that we have worked out to develop a restricted learning component for the message handler
we can then determine whether there is any criterion definable in terms of the events extracted that can improve on inquery s ranking
we will run inquery on that topic and then run the muc NUM fastus system on the NUM texts that inquery ranks most highly
the object still has the index NUM so that the same semantics can be used for the passive as for the active
in the molecular approach the system must recognize a description of the entire event not just the participants in the event
it is most appropriate when the syntactic role of an np is the primary determinate of the entity s role in the event
we have to back up one more phase to the basic phrase recognizer to get these noun groups as independent elements
this will lead to the question of how much information extraction domain development is necessary for how much corresponding document retrieval improvement
there is a relationship which we can crudely characterize as that of linguistic manifestation that links the nucleus to a dominating intention and a satellite to a dominated intention
the authors wish to thank robert dale barbara di eugenio donia scott lynn walker and two anonymous reviews for helpful comments on an earlier draft of this squib
in figure NUM note that while a causes b either a or b can be the nucleus of the relation
the volitional cause relation is defined as one in which the nucleus presents a volitional action and the satellite presents a situation that could have caused the agent to perform the action
dsh is embedded in another segment ds just when the purposes of the two segments are in the for g s dominance in intentional structure determines embedding in linguistic structure
then as part of her plan to achieve i1 the speaker generates i2 the intention that the hearer believe that the show is made up of all new choreography
a segment ds originates with the speaker s intention it is exactly those utterances that the speaker produces in order to satisfy a communicative intention in in the intentional structure
the dominance and satisfaction precedence relations impose a structure on the set of the speaker s intentions the intentional structure of the discourse and this in turn determines the linguistic structure
to identify the implicit claims about ils we must first identify the components of an rst analysis that involve a judgement about the relation between intentions underlying text spans
understanding this correspondence between the theories will enable computational models that effectively synthesize the contributions of the theories and thereby are useful both for interpretation and generation of discourse
the th component is the first processing step provided for by the alep system
if larger units are to be processed this has to be explicitely defined by the user
donkey sentence NUM every farmer who owns a donkey beats it
the resuits of the corpus analysis allowed us to determine a priority list of the linguistic phenonema to be described
the next step involves in the description of gran mar rules whict parse tile structure of a paragraph
the role of the word dictionary is to provide part of the information on the morphological syntactic and semantic revels that is requited for natulal language processing
the edr corpus is composed of the record number ntence information constituent information morphological information syntactic information and semantic information
this information is used in syntactic analysis and generation and provides the basis for the formulation of parsing rules and production rules
for example the super concepts of school are organization building and function
these dictionaries consist of information li om published dictionaries that has been stored on a recording medium and which can then be referred to and used by mechanical means
in fiscal NUM furthermore refinement and improvement were done and the revised version v1 NUM is available since april NUM
a spelling errol ill tile semi automatic letter due to the dale writlen by the r perat rr in a blank of a predefincd senlcnce b personalisation the article and its color me mentioned only in the automatic letter c precision of terminology precision of the explanalion clearly tile autonnltic loller is much more precise
choice of terminology semi automatic system i NUM NUM out of NUM automatic hybrid system NUM out of NUM human written letters NUM out of NUM differences ideal human letters vs automatic letters NUM automatic letters vs sa letters NUM NUM ideal hunmn letters vs sa letters NUM NUM here all differences are relatively great
the third section deals with the black b lcb rcb x mcthodol rcb gy and qttality critcria used for tile aissbssilicii lie lk urth section descl ibcs the results lcb rcb f the alsscssntcn
we have attempt t answer part rcb f this qttcsti lcb rcb n with at descripti rcb n of an assessment lcb rcb f three techniques for producing multiscntcntial text sentiatutomatic fill in lhc blank interfacing automalic linguistic and tcmphltes hybrid generation and hunlall writing
norlnalement wins dewiez ddjh awfir req u la liwaison de ce paquet veuillez m adresser de prdfdrence un cheque pour rdgler la l archandise que rlous votls av rlls ellvoy e
we have l rcb r lcb rcb vidcd a partial rcsl onsc it rcb this issue by analyzing the asscsslnent o three different tcclmiqucs for producing multiscntential text in this case business reply letters
examples of these criteria are correct spelling good grammar comprehensiveness rhythm and llow appropriateness of the tone proximity personalisation absence of repetition correct choice and precision of the terminology used
in fact representativity is ensured by the projection of the results of the previous phase system tests which used the same quality criteria involved a reductxl jury NUM to NUM members and was based on NUM test cases NUM letters of each type
our experiments also show that referentially relevant non linguistic information immediately affects how the linguistic input is initially structured
we believe that this paradigm will prove valuable for addressing questions on a full spectrum of topics in spoken language comprehension ranging from the uptake of acoustic information during word recognition to conversational interactions during cooperative problem solving
invited talk eye movements and spoken language comprehension
our results demonstrate that in natural contexts people interpret spoken language continuously seeking to establish reference with respect to their behavioral goals during the earliest moments of linguistic processing
supported by nih resource grant NUM p41 rr09283 nih hd27206 to mkt nih f32dc00210 to pda nsf graduate research fellowships to mjs k and jsm and a canadian social science research fellowship to jcs
however our purpose in choosing them was purely for convenience in designing an experiment useful for determining the potential of noun based disambiguation of adjectives
because adjectives co occur with their antonyms fairly frequently it was practical to extract disambiguated subcorpora large enough to provide a base for statistical inference
the following nouns were projected to show a preference for one or the other sense of the target adjectives that was statistically significant at the NUM
computational linguistics volume NUM number NUM table NUM coverage and disambiguation error rates for target adjectives in lo0 sentence samples using different indicator sets
in one the sentence itself makes it clear that a generic type of doctor sense was intended see section NUM NUM
the commitment sense of side strongly favors the correctness sense of right whereas the locational senses of side favor the directional senses of right
also consider a sentence with a production that occurs only in one other sentence in the corpus there is some probability that both sentences will end up fin the test data causing both to be ungeneratable
his experiments differed from ours and bod s in many ways including his use of a ditferent version of the atis corpus the use of word strings rather than part of speech strings and the fact that he did not parse sentences containing unknown words effectively throwing out the most difficult sentences
for a grammar with g nonterminals and training data of size t the run time of the algorithm is o tn NUM gn NUM n a since there are two layers of outer loops each with run time at most n and inner loops over addresses training data non terminals and n
since there are eight rules for every node in the training data this is o tn3
they are noticeably worse than those of bod and again very comparable to those of pereira and schabes
he tested these algorithms on a cleaned up version of the atis corpus and achieved some very exciting results reportedly getting NUM of his test set exactly correct a huge improvement over previous results
once the matrix g s t x is computed a dynamic programming algorithm can be used to determine the best parse in the sense of maximizing the number of constituents expected correct
however for his algorithm to have some reasonable chance of finding the most probable parse the number of times he must sample his data is at least inversely proportional to the conditional probability of that parse
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
our main motivation is to build general and adaptable linguistic tools and we have faced the problem of their portability
we first make a quick description of the linguistic tools we have at hand and we explain why linguistic tools unlike other software tools present pmticular portability problems
concerning the code we have now portable versions of the tools mentioned above plus a lexical desambiguer and a lexical correcter using similarity keys
ll esolve external ambiguitie s unl nowns
thread and process control primitives directly into the code
e lnside x NUM ips outside x a outside x for x grammar 1ps lnside x grammar score of ii lpsoutside x a outside x NUM rans x a l
control issues are often very tightly knit with the domain the module is aimed at i.e. it is very difficult to understand the control strategies used without sound knowledge of the underlying domain
score is a record with entries for inside and outside probabilities given to an edge by acoustic bigram prosody and grammar model inside x model scores for the spanned portion of an edge
a chart vertex vt corresponds to frame number t vertices have four lists with pointers to edges ending in and starting in thai vertex inactive out inactive in active out and active out
a prosodic transition penalty used in the combine operation was taken to be the score of the best combination of bottom up boundary hypothesis bx and a trigram score lword bx rword
key then add lcb rcb to e to add i e inside acoustic i l score score to e inside acoustic and add i e outside
figure NUM shows the modularization of intarc NUM NUM
research on architectures for integrated speech language systems in verbmobil
spanish c rt us availabh NUM us and since creating a large hand l tgp xl corltus is both cosl ly aud i r ne l o inconsislamcy gc decision was also a l ra ci ical one
a more widely used algorithnl for unsupervised learning of a pos tagger is hidden markov model i1mm
the test set NUM NUM words was t ngged matmally for comparison agaittst the system tagged texts
our tirst set of experiments tests the etdct of the i s tag eomt lexity
the overestimation bias disappears when the order of the sentences is randomized
a model in which words are sampled without replacement is more precise
figure NUM diagnostic functions for moby dick
f which overestimates the population values
NUM NUM the model proposed by hubert and labbe
i will consider these possibilities in turn
f NUM NUM NUM NUM p
NUM NUM problems with the hubert and labbe model
a key advantage of the rosenkrantz procedure is that unlike the greibach and ltig procedures the output it produces can not be exponentially larger than the input
as a result node sharing can typically be used to represent the ltig compactly it is often smaller than the original cfg see section NUM NUM
similarly auxiliary trees in which every nonempty frontier node is to the right of the foot are called right auxiliary trees
the fact that tig forbids wrapping auxiliary trees guarantees that a pair of indices is always sufficient for representing a left context
if left adjunction is possible at add a new leftmost child of labeled yi and mark it for substitution
other auxiliary trees are called wrapping auxiliary trees NUM the root of each elementary tree must have at least one child
any derivation in g can be converted into exactly one derivation in g by doing the reverse of the conversion above
subtrees are shared by using the same node for example aa on the right hand side of more than one layer production
by means of two simple changes in the prediction rules the tig parser can benefit greatly from this kind of lexicalization
with a little luck our results can provide some support for these assumptions
an example of an initial tree transducer constructed by this process is shown in figure NUM
this transducer emits no output upon seeing a t when the machine is at state NUM
the algorithm produced the arcs of figure NUM by generalizing the arcs from figure NUM above
this method of resolving conflicts repeats until no conflicts remain or until resolution is impossible
at this point the transducer covers all and only the strings of the training set
random ordering of our training set but a corpus based ordering would not be significantly different
this is also the method used in the results previously described for the various english rules
NUM our solution is to add a simple kind of memory to the model of transduction
cm plans and procedures are approved by the architecture committee chair
it also describes the cm roles and responsibilities of other program entities
section NUM NUM describes the relevant se cm configuration organizations for tipster ii
failure to do so will be documented and justified in the tacad
this document is a tipster application conformance assessment document tacad
this document will also justify or explain the discrepancy or deviations found
each panel member is allowed to state his her position on the change
encourage changes to the architecture from anyone who has done an implementation
the cmm is a regular participant in all erb and ccb meetings
NUM NUM syntactic based pruning and implicit spelling correction
the accuracy of the method is evaluated using the compound nouns of length NUM NUM NUM and NUM
be sa id of w rbs which take the same subjects or i he same objects
ed NUM a iylbiguoils as y olii l he l est cort lls used in idxljel iltielil NUM
to develop a text to speech conversion system with ldg it is necessary to prepare the ldg conjunction level information of a large nmnber of conjunct equivalents such as conjunctive particles
another such word is the particle wps t which is usually used as a topic marker for a sentence
when the word toki is used as the if reading this word modifies a clause in which the modality is expressed
in this case the relation between the conjunctive particle and its preceding clause is so weak that a pause tends to be inserted before the conjunctive particle not after it
this means that it is syntactically possible for all phrases or clauses that can modit predicates to modify all other phrases or clauses that appear in the latter part of long sentences
in i dg conjunction levels of clauses are divided into six classiiic tions according to the elements the clause clm contain a s listed in table NUM
we think that tile h vels of clauses produce prosodic infbrmation especially tlw location and h ngth of pauses which are influenced by tile sentence 51obal struchn e
this paper presents a practical method for a global structure analyzing mgorithm of japanese long sentences with lexical information a method which we call lexical discourse grammar ldg
however for words with a comma marked with black bars in fig NUM pause length of lev NUM is shorter than that of lev NUM
to sum up so far we now have a means of representing statistical phenomena inherent in a sample of data namely f and also a means of requiring that our model of the process exhibit these phenomena namely p f NUM f
in this expression p nre is the probability that the english word e generates n french words p yle is the probability that the english word e generates the french word y and d aie f is the probability of the particular order of french words
the generative process yields not only the french sentence f but also an association of the words of f with the words of e we call this association an alignment and denote it by a an alignment a is parametrized by a sequence of ifi numbers aj with NUM ai ie
we have employed this segmenting model as a component in a french english machine translation system in the following manner the model assigns to each position in the french sentence a score p r ft i x which is a measure of how appropriate a split would be at that location
an example of a template NUM constraint is p y pendant e l several y pendant e several a maximum entropy model that incorporates this constraint will predict the translations of in in a manner consistent with whether or not the following word is several
similarly including the template NUM feature NUM if y no interchange and nounl mois f x y NUM otherwise gives the model sensitivity to the fact that french noun de noun phrases beginning with mois such as mois de mai month of may are more likely to be translated word for word
p f e p f 41e NUM given some alignment a viterbi or otherwise between e and f the probability p f aie is given by jei ffi p f ale NUM i p n ei lei
this result provides an added justification for the maximum entropy principle if the notion of selecting a model p on the basis of maximum entropy is n t compelling enough it so happens that this same p is also the model that can best account for the training sample from among all models of the same parametric form NUM
mori hological analysis is an important but dilfic lt t art of the analysis since korean is an agglutinative language with sophisticated morpheme segmentation rules and morphotactic rules
the platform will support researchers and engineers with welldeveloped and standardized resources and al plication tools thereby avoiding duplicate activities fi om scratch a nd aniplifyiilg overall effort on the domain
expansion material can be found in both relevant and non relevant documents benefitting the final query all the same
additionally relevance feedback expansion depends on the inherently partial relevance information which is normally unavailable or unreliable
they were constructed to observe realistic limits of the manual process and to prepare ground for eventual automation
subject to some further fitness criteria these expansion passages are then imported verbatim into the query
it is important that all names recognized in text including those made up of multiple words e.g. south africa or social security are represented as tokens and not broken into single words e.g. south and africa which may turn out to be different names altogether by themselves
the following types of pairs are considered NUM a head noun and its left adjective or noun adjunct NUM a head noun and the head of its fight adjunct NUM the main verb of a clause and the head of its object phrase and NUM the head of the subject phrase and the main verb
the purpose was to devise a method for full text query expansion that would allow for creating exhaustive search queries such that NUM the performance of any system using these queries would be significantly better than when the system is run using the original topics and NUM the method could be eventually automated or semi automated so as to be useful to a nonexpert user
korean language engineering is one for korean language
by doing so the sentences are brought closer to each other in the number of words
alternatively we can acquire rules from the bilingual definition text for senses in a bilingual dictionary
the definition sentence are disambiguated using a sense division based on thesauri for the two language involved
zebra corporation and l ongnmn group arc appreciated tbr the machine readable dictionary
however work on class based systems have indicated that the advantages oulweigh the disadvantages
class based models obviously offer advantages of smaller storage requirement and hi vher system efficiency
approximately NUM NUM bilingual example sentences from lecdoce are used here as the training data
the table NUM and NUM illustrate tile bilingual knowledge acquired from the aligned results
a phrase is any arbitrary sequence of ad iaeent words in a sentence
ip is the number of phrases in a phrase sequence pc
199l has shown that one can estimate p f al
the complication of unit mismatch often implies the need of non flmctional aligntnent such as manyto many mapping
elk NUM pk p es pk p
non flmctiomd mapping tnay also occur in the l htglish french case but with much less frequency
avitlidatcs have very low prol al ility l ha t
the proposed alignment nmthod assumes it l re l roc ssing
a significant pay off may accrue through common usage particularly in the development of operational applications
module is equivalent to a computer system unit csu in the conventional life cycle definition
we define a flmction d that maps a character sequence c q to a list of word hypotheses lcb wi rcb
since recall and precision greatly depend on the frequency threshold we used the f measure to indicate the overall performance
we also compared three word models all words low frequency words and the combination of the two
by using the above mentioned word segmentation algorithm we can get all word segmentation hypotheses of the input sentence
however we give a more intuitive account of the method to introduce an approximation of the generalized forward backward algorithm
the generalized viterbi algorithm can be ob null tained by replacing summation with maximization in equation NUM
as table NUM shows the higher the threshold is the higher the precision and the lower the recall become
coinmunication is regarded as one word in corpus segmentation and counted as an unknown word in the test sentence
they are sensitive to the relative proportion of the different data types e.g. boundaries versus nonboundaries but insensitive to the statistical likelihood that agreements will occur
recently discourse studies have used reliability metrics designed for evaluating classification tasks to determine whether coders can classify various phenomena in discourse corpora as discussed in section NUM NUM
a conservative means for estimating a lower bound for the reliability of our method using krippendorff s c as a metric suggests that the method is reliable
richer linguistic input and more sophisticated methods of combining linguistic data led to significant improvements in performance when the new algorithms were evaluated on a test set of NUM new narratives
however the more interesting result is that for t NUM and t NUM the learning approach has an important limitation with respect to the boundary classification task
now a pronoun e.g. it that this in ci referring to an action event or fact inferable from ci NUM provides an inferential link
it is well known that the accuracy of word segmentation greatly depends on the coverage of the dictionary in other words the out of vocabulary 00v rate of the target texts
in fact it is fairly difficult to get plausible analyses like the ones shown in figure NUM because failure to identify an unknown word affects the segmentation of the neighboring words
this may be because the probability of one long unknown word can be higher than the product of the probabilities of two short unknown or infrequent words and one known word
figure NUM overlapping word hypotheses and pos sible word segmentations for japanese word segmentation we define a generalized forward algorithm and a general
to estimate these counts we replace all words appearing only once in the training corpus with unknown word tags unk before computing relative frequencies
since there are more than NUM NUM characters in japanese the amount of training data would be too small if we divided them by word length
again we discarded word bigrams that appeared only once in the training texts for saving main memory and used the remaining NUM word bigrams
therefore in the generalized forward algorithm and the generalized viterbi algorithm we hypothesize all substrings in the input sentence as words and examine all possible combinations of these word hypotheses
it consists of systems for text interchange and compression hypertext multimedia word processing and others
basically the tools that we present here are for text corpus and dictionaries except for voice and character recognizers
r monolingual terminology data bank users need definitions and explanations of technical terms during their work on specific domains
our goal is to establish korean inforn iation platform of linguistic resources and tools for korean language and information colnumnities
this paper reports tile components and the current status of the project and the importance of the effort
in version NUM platform we yielded NUM NUM million automatically tagged word phrases and NUM NUM million post edited word phrases
it includes phonetically balanced words phonemic sequences pronounced by four different speakers and narration of sample stories
this expanded research community has significantly enlarged the scope of vlc research
all the trees in figure NUM are lexicalized however only the ones containing seems pretty and smoothly are left anchored
ten years ago large corpora were mainly available to industrial research groups academic access to very large bodies of text was limited
then the treebank is divided as a training set with NUM sentences and a test set with NUM sentences based on balanced sampling principle
here for a matched constituent to be correct it must have the same boundary location with a constituent in the treebank parse
the theorem above does not convert tags into cfgs because the construction involving yi and zi does not work for wrapping auxiliary trees
we haw departed from these traditional analyses and have implelnented a fiat structure for the compomld tenses while retaining the cascading one for the passive
athis article shows on the other hand how tbr the past participle the cap can account for agreement in predicative structures for example the passive
the weight associated with each term in the distingui qhing
this is done with the following equafiou for each class
if the last character is an apostrophe remove it
if the last two characters are s remove them
the following table shows some information about the training documents
many of the high frequency words were political or economic
both grammm s strive to account for the same t roperties of natural language although they differ on points of detail the differences being essentially structural
in the gde grammars are pre compiled into ordered phrase structure rules and the number of these rules necessary to account for the vp turned out to be extremely large
simultaneous adjunction merely specifies multiple independent insertions
tig is related to tree adjoining grammar
figure NUM simultaneous left and right adjunction
like cfg tig generates context free languages
schabes and waters tree insertion grammar figure NUM
rule NUM recognizes a completed substitution
one could forbid multiple adjunction altogether
in this run for example an affinity relation between the characters ps r n and shgng is being considered at cycle NUM
the other structures that support the word k r nsh ng life namely the affinity relation between the characters k
the order by which structures are built is not explicitly programmed but is an emergent outcome of chains of codelets working in an asynchronous parallel mode
thirty ambiguous fragments that have alternating word boundaries in different sentential contexts were presented to the system and the system was able to resolve all the ambiguities
a computer program that tests this model on the task of capturing the effect of context on the perception of ambiguous word boundaries in chinese sentences is presented
the idea is to let the system explore diverse paths when the temperature is high while always stick to one search path when the temperature is low
this snapshot also illustrates an important feature of the system syntactic analysis can be performed without waiting for the system to complete the task of word identification
these approaches make use of co occurrence frequencies of characters in a large corpus of written texts to achieve word segmentation without getting into deep syntactic and semantic analysis
almost all codelets make one or more stochastic decisions and the high level behavior of the program arises from the combination of thousands of these very small choices
an application is a complete package including any processing necessary to setup documents for tipster processing and the user interface component
query is the detection component specific form of a query generated by a detection component in the component s unique structure and language
tig does not allow there to be any elementary wrapping auxiliary trees or elementary empty auxiliary trees
in general the accuracy of an automated categorization system is evaluated by contrast with the expert judgements
introduce c st bjectivecase rcb ctivecase l iobjectivecas
for example both man and woman belong to the same semantic category human
sentence analysis or analyzing a sentence sentence analysis is the process that reveals the valency structure of the input sentence
valency element in the valency stn cture each relation between a predicate and its modifier is called a valency element
this paper describes a melhod lbr mmlyzing a japanese double subject construction having an ad i eclivet rcdicate bascd on ihe valency structure
remarks ni label for a valency element sr semantic restriction on a noun jr restriction on case marking particles figure NUM
in the study we calculated the ratio cue words sentences to be NUM NUM and the ratio cue words syntactic aggregation to be NUM i.e. every seventh syntactic aggregation contains a cue word
the association strengths for verb level and noun level were measured using the mutual information between the noun or verb and the preposition
this resulted in NUM NUM pp cases from the brown corpus and NUM NUM pp cases from the ws articles
as the boxplot shows it performs significantly better than the methods that only use estimates of lexical a soclarion
if each aggregation saves approximately six words this will make the text NUM NUM aggregations x NUM words NUM shorter in some cases up to NUM shorter than it would have been without aggregation
such models are able to perform prediction on the basis of estimated probability distributions that are properly conditioned on the combinations of the individual values of the explanatory variables
the performance of the loglinear model can be improved by adding more features but this is not possible with the simpler nmdel that assumes independence between the features
some types of aggregation such as bounded lexical aggregation refer to bounded sets and are sometimes signalled by certain cue words e.g. except all except exception s is are besides excluding exclusion most but all not all but
as before t he semant ics is detined in terms of a supervaluat on construction on sets of disambiguated representations
all three of the passes described in section NUM are integrated in the search i.e. when parsing a test sentence the input to the second pass consists of k of the best distinct pos tag assignments for the input sentence
on sent cependant entre les lights une moindre wsrit lear endroit qu r gard du eolllllluni lne
mt effet f6tait r joui trop vile d entendre le pape parler d un communimte e intrim quemott pervec NUM
3orpus temm tizntion and in lexation based on ll NUM t i re iolle offline
ia mma tization recovers cil a io foruts r m iufl tted forms a n l is a primary ask of hological a nalysis
morphological analysis t ollowing a brief introduction to the project the paper describes the architecture of glossi i lcb rug
to choose the right base form one consults the disambiguator but it selects the verb tag instead of the wanted noun tag
as we proceed to the right we observe that there is a general downward curvature representing a lowering of the proportion of infinitives for the computational linguistics volume NUM number NUM higher frequency words
the horizontal solid line represents the overall mle the relative frequency of the infinitive as computed over all tokens the horizontal dashed line represents the relative frequency of the infinitive among the hapax legomena
in figure NUM the strong downward trend in the regression curve at the right of the figure is due in large measure to the inclusion of high frequency auxiliary verbs examples of which have already been given
n1 in j9 and n1 pl are the number of tokens of the infinitives and finite plurals respectively among the hapaxes in the training set
the hapax based mle estimate for derived nouns in er is somewhat higher than the overall mle for underived nouns the hapax based mle is significantly lower half of the overall mle
the heuristic employs a breadth first search bfs which does not explore the entire frontier but rather explores only at most the top g scoring incomplete parses in the frontier and terminates when it has found m complete parses or when all the hypotheses have been exhausted
the lexicon specification by the proposed verb subcategorization codes and scpf codes tins improved uniformity in quality and the speed of lexicon development as well
fig NUM shows a fixed frame with seven case slots and this is exactly what the record format of our japanese lexicon is
we assume that p n klto j tk p n k that is that the probability of a nonterminal is independent of the tags before and after it in the sentence
where to n is the sequence of the n tags or parts of speech in the sentence numbered to tn NUM and nj k is a nonterminal of type i covering terms tj tk l
sentence length was limited to a maximum of NUM because of the huge number of edges that are generated in doing a full parse of long sentences using this grammar sentences in this length range have produced up to NUM NUM edges
when a constituent is removed from the keylist it only affects the j3 values of its ancestors in the parse trees however l values are propagated to all of the constituent s siblings to the right and all of its descendants
once again applying the usual independence assumption that given a nonterminal the tag sequence it generates depends only on that nonterminal we can rewrite the figure of merit as follows p tj k ito j tk
we can therefore rewrite our ideal figure of merit as i i p to in this equation we can see that a nj k and p to represent the influence of the surrounding words
from the cpu time statistics it can be seen that the running time begins to show a real improvement over the normalized j3 model on sentences of length NUM or greater and the trend suggests that the improvement would be greater for longer sentences
standard speech applications do not use NUM NUM words for training as we do in this experiment but NUM NUM NUM NUM or more
and again we find very little change in perplexity during about NUM NUM initial merges and large changes during the last merges
a corpus is manually tagged with the categories and transition probabilities between two or three categories are estimated from their relative frequencies
we present methods to reduce the time complexity of the algorithm and report on experiments on deriving language models for a speech recognition task
for the rest of the paper we are interested in the probabilities which are assigned to sequences of outputs by the markov models
the probability either stays constant as in figure NUM b and c or decreases as in NUM d and e
the probability of the training corpus has to be calculated for each hypothetical merge which is o l with dynamic programming
there are ntrain nt ai NUM NUM NUM s hypothetical first merges in the unconstraint case
small concept grammars were developed for individual rubric categories
each rubric category had between NUM and NUM responses
responses are automatically scored by being assigned appropriate classifications
it was only meaningful that a constituent relationship occurred
in our lexicon concepts are preceded by
one hundred and seventy two responses were used for training
forty percent of the errors were lexical gap errors
concept structure problems made up NUM percent of the errors
their performance was NUM NUM precision and NUM NUM recall on an NUM NUM chinese character corpus
any position in the macro that we want to instantiate differently for each use is indicated by a parameter NUM instantiations of parameters can be single words in lexical or surface form variables operators or other macros
although finite state techniques are known to be unable to represent all the dependencies found in natural language they have the advantage of allowing a very efficient treatment of a great number of phenomena and the implementation of robust large scale nlp systems
the surface form is preceded by a colon and restricts occurrences of the word to exactly this form e.g.
such expressions which we call multi word lexemes mwl range from idioms to rack one s brains over sth over phrasal verbs to come up with lexical and grammatical collocations to make love with regard to resp to compounds on line dictionary
in addition we define auxiliary macros fix i because we want to instantiate the parameter NUM which stands for the lexically fixed components of the mwl with expressions of variable length fix5 NUM NUM NUM NUM s fix2 NUM NUM etc
encoding the local grammars as res instead of encoding them directly as networks does of course not change the expressive power of the formalism but it conveniently abstracts the handling of mwls from the graph manipulation level allowing to develop and employ devices that operate on string representations and map them to the underlying finite state networks
for instance whereas in german standard word order variation applies to all verbal mwls topicalisation of lexically fixed components is only rarely po ible as in g den vogel dabei hat dana jan abgeschossen finally jan surpassed everyone lit the bird with it has then jan shot
the morphological variable can be very general such a u a for ally adjectival use or more specific such as abs0 for adjectives that may not be used in comparative form and isg to restrict nouns to the singular as in grim abse welle nsg this way the restricted morph syntactic flexibility of mwls can bc expressed very elegantly
rubbing offsuccess noun and adjective can vary in case and in numbcr and comparative and superlative form are possible for the adjective whereas g griine welle phased traffic lights lit
thus the method s poor performance on column type texts despite the fact that texts are becoming increasingly reliable suggests a need to devise a set of attributes different from those for editorials and news reports
derivation tree figure NUM an example of the conjoin operation
where p a is the propo ion of the times that raters agree and p e is the proportion of the times that we would expect them to agree by chance
this suggests informally that the discrepancy between e v n and v n is significant over a wide range of sample sizes
although this single occurrence contributes to the inter textual cohesion of the novel as a whole it can hardly be said to be a key word within text slice NUM
if a simple random variable such as the vocabulary size reveals consistent and significant deviation from its expectation the accuracy of the good turing estimates is also called into question
one highly questionable simplification underlying the derivation of NUM spelled out in the appendix is that specialized words are assumed to occur in a single text slice only
unfortunately this model is based on a series of unrealistic simplifications and can not serve as an explanation for the divergence between the observed and expected vocabulary size
instead of estimating the probability of a word with frequency f by its sample relative frequency f NUM pi good suggests the use of the adjusted estimate
no significant trend remains in the residuals d k du k f NUM NUM NUM NUM p NUM
notation mctoc m good turing estimate ms f m sample estimate mh f m heuristic estimate mp f population mass
let the total number of words in the set s of types with specialized use be pv and also assume that the text slices in which these specialized words appear are randomly distributed over the text
furthermore because different samplings of evaluation data from a source data set could produce wide variations in performance we performed NUM runs of the evaluation procedure on each of the NUM data sets
it did this by ranking the descriptors based on their syntactic role
finally techniques for associating an organization name with location information are examined
coreference has been found to be an important component of many applications
examination of the results has uncovered two new rules for making variations
official muc6 scores were generated using a later version of the scoring program
the company s north american solidwaste operations will retain the name waste management inc
this is often true for the human reader as well
the scores therefore do not represent an absolute measure of performance
this can then guide the variation generator when that pattern has been matched
null NUM of our system s name linked descriptors were associated by context
for novel texts no lexicon that consists simply of a list of word entries will ever be entirely satisfactory since the list will inevitably omit many constructions that should be considered words
ren2 person is a fairly uncontroversial case of a monographemic word and zhongl guo2 middle country china a fairly uncontroversial case of a digraphemic word
NUM in this re estimation procedure only the entries in the base dictionary were used in other words derived words not in the base dictionary and personal and foreign names were not used
for instance the common suffixes nia e.g. virginia and sia are normally transliterated as j i ni2 ya3 and xil ya3 respectively
for a given word in the automatic segmentation if at least k of the human judges agree that this is a word then that word is considered to be correct
the particular classifier used depends upon the noun
consider first the examples in NUM
the most recently proposed constituent shown in figure NUM is the rightmost sequence of trees tin tn m n such that tm is annotated with start x and tm l tn are annotated with join x
for chinese string searching it is not uncommon to search for reduplicating words e.g.
the program computes for state NUM the last states and the other states separately
here we illustrate the use of a larger alphabet zr and ps e er
thus as a first step we focus on modifying the kmp for chinese string searching
two modified versions of the kmp algorithms are presented the classical one and the finite automaton implemenation
n etscape can not interpret the annotations represented by their equivalent NUM byte characters
i urther processing is needed to convert the automaton for NUM byte processing
figure NUM shows the program that computes the state transitions using the faihtre links
this shell provides the abstract model of problem solving upon which the dialogue manager is built
it measures the impact of proposed techniques directly rather than indirectly with an abstract accuracy figure
for instance since the garbled part may include do n t
note these examples both illustrate the strong commitment model
in each unsuccessful attempt the subject was using speech input
there are few systems that attempt to handle unconstrained natural dialogue
none of the subjects had ever used the system before
the error bars in the figure indicate NUM confidence intervals
NUM of the available training data in the speechpp models
rules a bc a b then it is more effective to make the algorithm check the longer rule a bc before checking the shorter rule a b
the format of the inflection code of the english part is selfconstructive i.e. it enables the generation of appropriate inflected forms from a canonical form without the necessity of a time consuming look up of any classification file
a few heuristic methods have been developed in order to limit the search space and thus achieve better efficiency
the algorithm deals with a characteristic feature of polish syntax an almost arbitrary order of verb modifiers
the method replace by alternative consists in replacing two rules with the same left hand symbol and the same beginning of their right sides by one rule e.g. two rules a bc a bd are replaced by one rule a b c or d
the translation software uses the arity prolog interpreter in order to obtain a phrasal structure of an output expression
t constructions are used in analyzing object questions and relative object clauses in which a modifier of the type t does not explicitly occur although the type t modifier is required for the verb according to the dictionary information e.g. in the sentence he is a man i was talking to the object of the clause i was talking to appears outside the clause
the method from longest to shortest says that if a symbol a occurs as a non last right hand symbol of a rule e.g. in the rule d cae and the grammar includes more rules than one to replace the symbol a e.g.
they aim to compensate the loss of communication for people without the use of speech
put the white triangle under the pawn which is in the square b4
in fact the international state of the art in this domain is rather poor
concerning space locating several levels are also possible to point out a square or a position
the illico system is a generic system for natural language processing nlp
the system can be used with or without an assistant depending on the user s autonomy
the illico project has been partially funded by the french ministate de la recherche and conseil rdgional provence alpes cste d azur
the conceptual model describes the world of the application in terms of domains of objects and possible relations between them
the icd shall unambiguously define the architecture component interfaces
the architecture shall recognize a standard set of objects
NUM verification method inspection and demonstration
more specific background may be found in reference NUM
a candidate is given an extra bonus when it is found from these two places
are performed to verify whether the identity of the entity can be recovered from the context or whether there exist semantic relations with other cited entities that are worth being signaled e.g.
compared with other approaches our approach has better performance and our classification is automatic
we randomly select six different sections from a newspaper corpus liberty times news
when the variation of a character is considered equation NUM is formulated
in the sports section there are many chinese personal names and transliterated personal names
to model the rhetorical structure of discourse we consider the rhetorical structure theory as developed in mann and thompson NUM NUM
in our domain however often technical terms can not be avoided since the precise type of document or legal requirement have to be specified
an automatic generation system that is to produce good quality texts has to include effective algorithms for choosing the linguistic expressions referring to the domain entities
in this paper we analyze the problem of generating referring expressions in a multilingnal generation system that produces instructions on how to fill out pension forms
a second perplexity experiment that we conducted tried to test whether we can hypothesize segmentations given that we have no boundaries in the test set
in cases where there does not appear to be a suitable replacement for the restart annotators were instructed to place the as close to the ip as possible
in example NUM it can be seen that a restart has been marked across a turn with the rm and im in one turn and the rr in the next turn
is there any information in segment boundaries if no boundary information is available during testing can we hypothesize this information using a language model trained with segmented training data
since the linguistic segmentations are available for only two thirds of the switchboard data we decided to use the corresponding two thirds of the acoustically segmented training data for our comparative experiments
a weighted combination of scores from different knowledge sources htk acoustic model scores number of words different language model scores etc was then used to re rank the hypotheses
next treating the before and after portions of the sentences as separate corpora we look at the distribution of the vocabulary and the distribution of other phenomena such as restarts
you know at the end of a sentence example NUM is considered as a part of the current sentence as described in more detail in ss2 NUM NUM
l he i reciscly corrcc t
third the tagging process described in section NUM NUM is carried out
the system uses the cosine measure to compute the similarity
a most county jail inmates did not commit violent crimes
they found that there is a preference for choosing the most recent noun phrase in sentences of the form np v np of pp with ambiguous relative pronoun antecedents e.g. the journalist interviewed the daughter of the colonel who had had the accident
the goal of the learning algorithm for relative pronoun disambiguation is NUM to determine whether the wh word is being used as a relative pronoun and if it is NUM to determine which constituents comprise the antecedent
the feature sets proposed by c4 NUM reduce the number of attributes used in the case retrieval algorithm from NUM to an average of NUM NUM and NUM features for part of speech general semantic class and specific semantic class tagging respectively
moreover automatic approaches to feature set selection can outperform feature sets chosen manually by taking advantage of statistical relationships in the data that are difficult for humans to predict and that may be idiosyncrasies of the task and data set at hand
among the features deemed most important for part of speech tagging for example included the general semantic class of the two preceding words the general semantic class of the following word and the semantic class of the subject of the current clause
to apply the restricted memory bias to the baseline case representation we let n represent the memory limit and in each of five runs set n to one of five six seven eight or nine
the baseline represents the constituent that precedes the relative pronoun up to three times in the baseline representation as a constituent feature e.g. direct object and via the last constituent global context features
the automatic etho01 run best exempli fies the direction needed here first getting good performance for three very different but complementary techniques and then discovering the best ways of combining results
this run is a manual modification of the inqio1 run with strict rules for the modifications to only allow removal of words and phrases modification of weights and addition of proximity restrictions
null con concept s NUM natural language processing NUM translation language dictionary font NUM software applications fac factor s nat nationality u s
the modification was to replicate words this increases the weight and to add a few associated words an average of NUM NUM words per query or at most NUM content words
num number NUM title topic financing amtrak desc description a document will address the ngle of the federal government in financing the operation of the national railroad transportation corporation amtrak
many groups tried their automatic query expansion methods on the shorter topics with good success other groups also did manual query construction experiments to contrast these methods for the very short topics
null the use of the top NUM documents as a source of terms as opposed to using the entire corpus should be sensitive to the quality of the documents in this initial set
in both cases a second set of NUM documents was examined from each system using only a sample of topics and systems in trec NUM and using all topics and systems in trec NUM
similar results were found for the more complete trec NUM testing with a median of NUM new relevant documents per topic for the adhoc task and NUM new ones for the routing task
the second issue was the ability to increase the amount of information available about each topic in particular to include with each topic a clear statement of what criteria make a document relevant
control annotations are used in fur for two distinct purposes NUM to control recursion on linguistic constituents the tree of the input fd is fleshed out in top down fashion by re unifying each of its sub constituent with the fg and NUM to reduce backtracking when processing disjunctions
propagate the default person third feature added to the subject filler to the verb group filler without such a propagation the morphology module would not be able to inflect the verb to hand as hands in NUM
we took inspiration principally from NUM for the overall organization of the grammar and the core of the clause rod nominal sub grammars NUM for the semantic aspects of the clause NUM for the treatment of long distance dependencies and NUM for the many linguistic phenomena not mentioned in other works yet encountered in many generation application domains
what tags can only be compositionaily derived during the corpus tagging process
NUM a search heuristic which attempts to find the highest scoring parse tree for a given input sentence
we will refer to the original constrained optimization problem
most of the library contents correspond directly to particular values of parameter settings
examples of two such errors are shown in figure NUM
where xaj denotes the context of the english word eaj
it is at this point that the algorithm should terminate
smaller p lcb mterchange rcb larger figure NUM
this order is often very different from the french word order
we introduce the concept of maximum entropy through a simple example
the representation is anchored on these terms and thus their careful selection is critical
thus while the rewards maybe greater the risks are increasing as well
query expansion via automatically generated domain map was not usd in offical ad hoc runs
in the trec experiments reported here we extracted head modifier word and fixed phrase pairs only
table NUM ad hoc runs with queries NUM NUM NUM abase automatic
this result has been achieved to a certain extent in our work thus far
the evaluation results discussed here were obtained in connection with the 3rd and 4th text
what happens however if we do not find them in a document
location in text the location attribute records information on how far a given sentence appears from the beginning of the text
the possible values are c for column e for editorial and n for news report
community phonologically similar segments behave similarly
either way the transduction will fail
flapping transducer induced from NUM NUM samples
a unique end of string symbol is introduced
example push back operation and state merger
states of the two conflicting arcs
the initial state of the flapping transducer
this machine is shown in figure NUM
this work was partially funded by icsi
the same decision tree after pruning
for example let m2 be the model consisting of g2 plus weights ill NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
in feature selection we need to use random sampling to find the initial weight for a candidate feature and in weight adjustment we need to use random sampling to solve the weight equation
with each rule i in a stochastic context free grammar is associated a weight fli and a functionj x that returns the number of times rule i is used in the derivation of tree x
we use the notation p yq to represent the expectation off under probability distribution p that is p yq x p x f x
intuitively the problem is this the distribution ql assigns too little weight to trees xl and x2 and too much weight to the missing trees call them x5 and x6
to take a very simple example a fair coin can be seen as a method for sampling from the distribution q in which q h NUM NUM q t NUM NUM
for example consider the tree in figure NUM repeated here in figure NUM for convenience rule NUM is used once and rule NUM is used twice accordingly fl x NUM
for example suppose we have a training corpus containing twelve trees of the four types from l g1 shown in figure NUM where c x is the count of how often the
at higher levels a square can be identified by its position on the checker board or by its content the square which contains the black round
we can mention speaking dynamically boardmaker mayer johnson usa as well as talk about don johnston usa
study the logical representation of the sentences semantics for some of the proposed activities the system provides a graphic representation of the semantics of the resulting sentences i.e.
in the opinion of the doctors we have consulted about this project this access to a semantic representation of the sentences is extremely interesting with regard to the treatment of cognitive disorders
the working modes can be discover linguistic components the lexicon the conceptual model etc to enable users to familiarise themselves with words and concepts
the system provides a set of educational play activities illustrated through multimedia technology and designed to stimulate and help users to employ common language to express themselves within a specific context
we must also mention the facilitated communication method which alms to help the user to use a keyboard of a computer to express himself by words and sentences
we must also note that ordinary computer assisted language learning systems can seldom be used by autistic persons for they require intuitive cognitive knowledge which is often lacking in autistic persons
we therefore focussed our study on how to help detecting metaphors in order to chose the most adequate semantic processing
each clue that was found is currently evaluated on a large corpus about NUM NUM words
in this approach multiple semantic analysis can be processed resulting in possibly multiple meaning representations
indeed a textual clue is not sufficient to prove the presence of such figures of speech
in the previous example of textual clue the relevance values are issued from this corpus based analysis
we present an object oriented model for representing the textual clues that were found
it requires a specific knowledge representation base and also results in multiple representation meanings
they are then analyzed by hand in order to determine their relevance attribute
it is currently used for the evaluation of the textual clues that were found
this scatterplot shows the relative frequency of the infinitive versus the finite plural as a function of the log frequency of the en form
the practical utility of the specialized grammar is largely determined by the loss of coverage incurred by the specialization process
the rules in the original general grammar are divided into two sets called phrasal and non phrasal respectively
the index is set up in two parts
our thanks go to dr y yamazaki president of atr itl mr t morimoto head of department NUM and dr k h
this leads us to consider that in syntactic trees the representation of a fragment is not necessarily a horizontally complete subtree
to develop good strategies for interactive disambiguation it is useful to study vatious properties of the ambiguities unsolvable by state of the art analyzers
for defining the proper representations of a represem tation system it is necessary to specify which disjunctions are exclusive and which are inclusive
the set of proper representations associated to a representation r is obtained by expanding all exclusive disjunctions of r and eliminating duplicates
an interruption such as NUM may also create a discontinuous turn NUM NUM here
the labeling begins by listing the text or the transcription of the dialogue thereby indicating segmentation problems with the mark i
can a common evaluation framework satisfy the needs and limitations of both supervised and unsupervised sense disambiguation methods
in order to maximize the over all benefits we have decided to develop our japanese lexicon tool as a www application
note t at the dictionary has currently more than NUM NUM entries with english glosses and kanji trans criptions
there have been also several groups of japanese teachers across the world who have contributed to the ddvelopment
one way to help the user is by providing him with infor mation databases he is looking for
al though it is still at a preliminary stage people from all over the world can access this information
for example rather than killing the user by an information overflow like these long list of translations that most electronic dictionaries provide lists in which the user has to dig deeply in order to find the relevant word one could parametrize the level of detail scope and grain size of trans lations for a given text or text fragment
such schemes where learners directly participate in resource development allow for authentic communi cation hence there is a benefit tbr the learner they also show the engineer the kind of information the learner is interested in inlbrmation which is usually hidden
rather than trying to play the role people are very good at answering on the fly questions on any topic common sense reasoning etc call system should assist people by providing the learner with information humans are generally fairly poor at
due to this tight coupling i.e. connection between the developers and the users the dictionary grew i actually macjdic was developed by a graduate student from harvard whereas the body of the dictionary was first developed and maintained by the center administrator at monash university
what special problems exist when evaluating wsd performance on verbs
would a muc style competitive evaluation program be beneficial or detrimental to progress in the wsd field
how should regular polysemy and metaphor be treated in wsd evaluation
le d6put6 n ignore pas que le gouvernement compte pr6senter avant la fin de NUM ann6e un projet de r6vision de la loi sur les langues officielles
the absolute frequency threshold tf currently set at NUM also helps limit the size of s by rejecting words that appear too few times opposite the source collocation
the error is due to the fact that the french word important did not pass the first step of the algorithm as its dice coefficient with important factor was too low
since we are looking for a way to identify positively correlated events we must be able to easily test the second case while testing the first case is not relevant
once a word fails one of the above tests we are guaranteed that all subsequent words in the list with lower local frequencies will also fail the same test
let the counts of these sentences be nabx naby nagx n NUM where a bar indicates that the corresponding term is absent
after implementing champollion we attempted to generalize these results and confirm our theoretical argumentation by performing an experiment to compare si and the dice coefficient in the context of champollion
seeing the two word groups in aligned NUM in the remainder of this discussion we assume that p x NUM y NUM is not zero
instead a speaker must be aware of the meaning of the phrase as a whole in the source language and know the common phrase typically used in the target language
the resulting perplexity value for this system is NUM NUM
its test set perplexity was NUM
performance is measured by the hughes atwell cluster evaluation system
consequently indirect evaluation can be linguistic or engineering based
the numerator acts as a normalizer
the results are shown in figure NUM
the boys eat the sandwiches quickly
the cheese in the sandwiches is delicious
group averaging and ward s method as their main method
variables are denoted by a number indicating the position of the input segment being referred to and a set of phonological features to change
since this modification only alters the initial tree transducer the behavior of the main state merging loop of the ostia algorithm is essentially unchanged
the process of pruning the trees however is very expensive as the entire training set is verified after each pruning operation
thus even using the expensive method of verifying the entire training set after each pruning operation the entire algorithm is still polynomial
furthermore our additions have not worsened the complexity of the algorithm with respect to n the total number of input string symbols
the set of all positive and negative contexts will not generally determine a unique rule but will determine a set of possible rules
we showed that a domain independent empiricist induction algorithm ostia failed to induce minimal transducers even for very simple rules like flapping
thus it is necessary to make several passes through the states attempting additional pruning at each pass until no more improvement is possible
whether phonological features may be innately guided or derived from earlier induction then the community bias suggests adding knowledge of them to ostia
any ordering relationships are preserved in this composed transducer the order of the rules corresponds to the order in which the transducers were composed
accordingly the pair cr is also considered a single graphemic state since it is pronounced as ks
in order to reduce computation one of the two library supplied logarithm functions had to be used i.e. log10 or log e
although statistical approaches have already been widely applied in several fields of natural language processing they have not been considered for ptgc
the transition probability matrix a should be biased to contain at least one occurrence for every transition and no zero elements
as is clear from the presentation of the algorithm this part contains a rather time consuming sorting procedure plus a floating point multiplication
a hidden markov process is described by a model that consists of three matrices a b and
this fact allows the elimination of code that would check for overflow during the algorithm resulting in a much faster code
one can initially observe the number of times the algorithm produced a word in each position NUM to NUM figure NUM
figure NUM shows the degradation of the success rate of the algorithm as a function of the corruption of the input stream
second a substantial increase in processing speed is achieved since the fixed point addition is faster than floating point multiplication
the gbmt engine is parametrized by a bilingual glossary
tipster annotations are used as a lingua franca for representing linguistic information shared among various nip components such as morphological analyzers taggers bilingual dictionaries the gbmt engine and the morphological generator
using the glossary editor the translator can also access bilingual dictionaries and use a variety of corpus aytalysis tools including a key word in context kwic utility and a concordance tool
the temple translator s workstation project michelle vanni
figure NUM the glossary editor with the japanese glo ary
we present such linguistic investigations on french motion verbs and spatial prepositions and the basic concepts we have found
let us consider for example the following vp sortir dans le jardin to go out into the garden
this information is the result of the interaction of the verb sortir to go out with the preposition dans in qnto
when we travel or run we go from some part to another part of a same global location
we distinguish NUM categories on the basis of which kind of location they intrinsically refer to
it is often the case that from this interaction appear new properties that belong neither to the verb or the preposition
change of posture coptu verbs s asseoir to sit down se baisser to bend down
change of location col verbs entrerto enter sortir to go out denote a change of location
for reason of space we can not detail our formalism here but we intend to present it in the talk
if the value of before is sentence final contour
furthermore even when machine learning does not use global pro
passonneau and litman discourse segmentation table NUM performance on training set
figure NUM illustrates sample output of this algorithm ml
figure NUM shows boundaries cue assigned by the algorithm
level those boundaries proposed by at least four subjects
the one exception is lcb raise rise rcb where tribayes and word score about the same in both conditions
at run time bayes uses the features learned during training to correct the spelling of target words
together these two parameters give a complete picture of system performance the score on correct usages measures the system s rate of false negative errors changing a right word to a wrong one while the score on incorrect usages measures false positives failing to change a wrong word to a right one
acronym but the title of a grammatical treatise written by the syriac polymath inter alia mathematician and grammarian bar ebrsy5 NUM NUM viz
it was found that reliability enhances the strength of good attributes for a sentence leading to an improved performance of abstracting models
it could be the case that data with high agreement may still be too noisy to use for a task for which they were collected
consider a set of k raters and a group of n objects each of which is to be assigned to one of m categories
hitachi ltd NUM NUM takayama ikoma nara NUM NUM japan NUM hatoyama saitama NUM NUM japan matsu c is aist nara ac jp
the same problem arises for other constructions involving nonlocal dependencies s NUM a da i hatte karl i mit gerechnet
a fairy tail he will tell his daughter a fairy tale b erz hlen mfissen wird er tell must will he seiner tochter ein mpsrchen
the challenge is to find an intermediate solution which specializes the grammar non trivimly without losing too much coverage
section NUM concludes and sketches further directions for research which we are presently in the process of investigating
the specialized grammar used the new scheme and had been trained on the full training set
the input to the ebl based grammar specialization process was limited to readings of corpus utterances that had been judged correct
this section describes a number of experiments carried out to test the utility of the theoretical ideas presented above
the resulting specialized grammars are forced to be non recursive with derivations being a maximum of six levels deep
table NUM breakdown of average time spent on each processing phase for each type of processing seconds
an initial implementation exists and is currently being tested preliminary results here are also very positive
secondly there is the question of how to select the rulechunks that will be turned into macro rules
as described in section NUM above the non phrasal grammar rules are subjected to two phases of processing
each time a word in the confusion set occurs in the corpus bayes proposes every feature that matches the context one context word feature for every distinct word within k words of the target word and one collocation for every way of 4a tag is taken to match a word in the sentence iff the tag is a member of the word s set of possible part of speech tags
the method presented here differs from the naive approach in two respects first it does not assume independence among features but rather has heuristics for detecting strong dependencies and resolving them by deleting features until it is left with a reduced set t of relatively independent features which are then used in place of in the formula above
this follows from equations NUM and NUM when comparing p w and p wi the dominant term corresponds to the most likely tagging and in this term if the target word wk and its substitute w have the same tag t then the comparison amounts to comparing p wk and p w lt
confusion set their there they re than then its it s your you re begin being passed past quiet quite weather whether accept except lead led cite sight site scores are given as percentages of correct predictions
this paper addresses the problem of correcting spelling errors that result in valid though unintended words such as peace and piece or quiet and quite and also the problem of correcting particular word usage errors such as amount and number or among and between
as well by putting a threshohl on the graph matching process we were able to limit the expansion of our clustering as we can decide and justify the incorporation of a new concept into a particular cluster
this section describes how given a trigger word we perform a series of forward and backward searches in the dictionary to build a cckg containing useful information pertaining to the trigger word and to closely related words
to help us build this cckg and perform our integration process we assume two main knowledge structures are available a concept hierarchy and a relation hierarchy and we assume the existance of some graph operations
the information given by the description and general knowledge will be used to perform the knowledge integration proposed in section NUM the specific examples are excluded as they tend to involve specific concepts not always deeply related to the word defined
machine readable dictionaries mih s are a good sour e of lexical information and have been shown to be al plical le to the task of i kii collstruction l ola n
many aspects of the concept clustering and knowledge integration processes have already been implemented and it will soon be possible to test the techniques on different trigger words using different thresholds to see how they effect the quality of the clusters
ilowever in practice m ter two or three steps forward or backward the maximal common subgraphs between the new graphs and cckg do not exceed the graph matching threshold and thus are not added to the cluster terminating the expansion
a database may also contain default world knowledge information e.g. with no other over riding information it may be safe to assume that the string mcdonald s refers to an organization
because of these difficulties we believe that for the forseeable future practical applications to discover new names in text will continue to require the sort of human effort invested in nominator
we identify three indicators of potential structural ambiguity prepositions conj unctions and possessive pronouns which we refer to as ambiguous operators
as the verbal complex is analyzed as a constituent the fi onting of erz ihlen miissen in lb can be explained as well
for example if white occurs at sentence initiai position and also as a substring of another name e.g. mr white it is kept
because of the principle that noisy data is preferable to loss of information nominator does not split names if relative strength can not be determined
the scope of ambiguous operators also interacts with the scope of np heads if we define the scope of np heads as the constituents they dominate
thct c arc many m lcb rcb re industrial proiccts in analysis than in natural l anguagc cneration
a relatively high number of prcdefined sentences and paragraphs have to be provided to cover the writers needs but
restans NUM votre enti re disposition je vous prie de croire ch6rc madame h l expression de rues sentiments ddvouds
these items were not available when yottr order wax ivcor led as NUM oil were injbrmed at the time
the fiftll section givcs examl rcb les of letters prt luccd by both the semi autonutlic system and the lilu btistic and tempiate hybrid system
j ai bien fe u votre courrier du 30tohre sic et je eomprends tout h fait votre mdcontentement
nous faisons le maximum pour contenter nos clients mais nous sommes ddpcndants des ddlais de liw aison que nous imposent certains fournisseurs
dear madam in reply to your letter of 3rd owber sicl NUM can completely undetwtand your d sati sfaction
therclorc the bencl ils lcb rcb f using applied n1 would a rcb pcal a crucial issue
what al c the benefits of ttsing natural anguagc cneratio t in an industrial apl lication
we will generate the following eight pcfg rules where the number in parentheses following a rule is its probability
we also ran experiments using bod s data NUM sentence test sets and no limit on sentence length
since every subtree is included even trivial ones corresponding to rules in a pcfg novel sentences with unseen contexts
in method correct the n ary branching productions are converted in such a way that no overgeneration is introduced
of course some of this difference may be due to differences in implementation so this estimate is fairly rough
table NUM length combination of word sequences and their accuracy business letter
table NUM summarizes the combination of the length of english and japanese word sequences
this is why we tried to pursue a way to obtain better translation dictionary from parallel corpora
the fraction in each entry shows the number of correct pairs over the number of extracted pairs
an iterative method with gradual threshold lowering is proposed for getting a high quality translation dictionary
if every possible correspondence of word sequences is to be calculated the combination is large
the figures show the numbers of words that are included at least one extracted translation pairs
this is because those corpora frequently contain a number of lengthy fixed expression or particular collocations
cm personnel are trained in the objectives procedures and methods for performing their assigned cm activities
thus the ccb chairman shall render the final decision as to the course of action to the taken
the erb is an internal tipster ii architecture review and action panel comprised of personnel from all project groups
the architecture committee will determine whether a tipster application has successfully passed a pdr and foc control gate erbs
the specifics of how to run the ccb and the control gates are given in section NUM NUM below
the reviewer may recommend minor modifications to the rfc to maintain a level of consistency in names and methods
the program manager ensures that adequate cm resources including cm tools are made available
finally configuration management is responsible for formal delivery of all tipster ii architecture defining documents to the government
the cm activities described in this cm plan are initiated and completed in accordance with the program master schedule
some of the collocations retrieved are shown in table NUM collocations labeled fixed such as international human rights covenants are rigid compounds
in the table the numbers of collocations correctly and incorrectly translated when the dice coefficient is used are shown in the second and third rows respectively
NUM none of the authors is affiliated with boitet s research center on machine translation in grenoble france which is also named champollion
in the final stage xtract filters any pairs that do not consistently occur in the same syntactic relation using a parsed version of the corpus
then the corpus of paired sentences comprising our database represents a collection of samples for the various random variables x for the various groups of words
we would consider such evidence to strongly indicate very high similarity between the two groups and indeed the dice coefficient of the transformed variables 2x92 NUM NUM
specific mutual information falls somewhere in between the dice coefficient and average mutual information it is not completely symmetric but neither does it ignore NUM NUM matches
unfortunately table NUM shows that generalization improves but not eliminates the problem of repetitive patterns
the cardinality of a generic collision set is directly proportional to the degree of ambiguity of its members
second one can easily imagine variants of tig where simultaneous adjunction is more limited
an ltig is said to be left anchored if every elementary tree is left anchored
however both the auxiliary trees and the adjunction allowed are different than in tag
in contrast every operation allowed by a tig inserts a string into another string
therefore the degree of ambiguity of each string is preserved by the constructed ltig
in general this requires the use of adjoining constraints to prohibit the forbidden adjunctions
the ltig procedure above creates a left anchored ltig that uses only right auxiliary trees
with the inclusion of these adjoining constraints the procedure above works just as before
the left and right adjunction rules recognize the adjunction of left and right auxiliary trees
we will address a number of fundamental questions including what are the different types of information that we want to tag how can information in a structured lexicon facilitate tagging tasks
not all of them give rise to so many rules but the compilation time and size of the output grammar made this solution impractical
any comments recommendations by the cawg will be an addendum to the formal rfc
potential lexical applications come readily to mind for example the orthographic spelling rules for english suffixation such as sky skies
the syntax of datr does not itself prevent one from writing down inconsistent descriptions verb syn cat verb syn cat noun
4deg a more sophisticated approach uses datr itself to construct a dag description in the notation of your choice as a value NUM
NUM the advantage of this is that to check the functionality of a datr description and hence guarantee its consistency is completely trivial
in the light of these considerations we assume here and elsewhere that functionality is a reasonable restriction to place on datr descriptions
if the current model has n states and we divide them into k NUM nonempty equivalence classes c1 c then instead of n n NUM NUM we have to test
the problems with the compositional representation b are concerning the binding of the variables the pronouns in the natural language
but in order to deal with such texts it is also necessary to process linguistic units which are larger than sentences
dpl considers only the information change which concerns heir potential to pass on possible antecedents for subsequent anaphors NUM
in b NUM the third occurence of the variable x is free and thus does n t allow the anaphoric reading
the specifications of the project foreseen the coverage of real life texts which have also been processed by a corpus analysis
in the last section i will show how a very preliminary and tentative implementation of this framework can be modelled within the alep forrealism
but those assumptions are here important if one wants to provide an uniform translation of indefinite nps into existential quantifier see below
the particular expressions of the natural language dpl is dealing with are the following NUM a man walks in the park
the same remarks are valid for x and y in b NUM and for y in b NUM
in our example the first sentence has an output which is as the same time the input of the second one
the algorithm has wider empirical coverage than other current approaches which have been suggested within the computational and linguistic literature
c consider in sequence each argument slot slot in the subcat list of a
phon john sings aml beautifully too i
therefore the algorithm produces the correct reconstructed forms for the elided clauses in ll
c john sang but not in new york at the concert for three hours
examples of the algorithm s output for theses cases are given in NUM and NUM
the set of feasible follow categories nextcats of a particular category cat is returned by the predicate follow cat nextcats
one of the major difficulties in learning to use penman has been acquiring the expertise to construct spl plans
the sentence bank contains a number of sample sentences which illustrate various types of spl plans and constructions
each template also provides a facility to add keywords and values that are not present on the form
this extensible example set the sentence bank provides positive examples of how to construct spl plan templates
sp lat provides a graphical environment where users can enter the spl plan specifications for the penman generation system
the penman upper model a classification of various semantic concepts has traditionally been divided into three disjoint concept hierarchies a high level split between the major semantic abstractions of english processes objects and qualities
in contrast the experienced spl developer would often draw on knowledge of previously constructed spl plans in order to recycle bits of partial spl plans but had no convenient way to store and access this information
this reduces the merit of the very small dictiomtry achie w d in morphological analysis section
prohibited p o kakari uko bunsetsu latrix figure NUM flow of syntactic kakari uke analysis
figure NUM c and figure NUM re kakari uke matrices showing the possible pairs and selected pairs
this means of dealing with structural ambiguities avoids combinatorial explosions and requires flu less machine resources
in this paper we describe the qjp s analysis methods and report on its current performances
such method usually leads to combinatorial explosions which causes a lot of memory and processing time
we changed our viewpoint in order to design and develop aal applicable and usable japanese parser
so we either simplified dealing of structural ambiguities or ignored semantics to lighten the syntactic processing
figure NUM is the output of kakari ukc pairs tagged with parts of speech and bunsctsu features
the following morphemes in most cases are functional words or inflective suffixes
the reader is invited to try to calculate the solution for the same example when the third constraint is imposed
another economy arises because translation between representations is avoided
wag can thus act as a stand alone sentence realiser
adding NUM NUM matches lowers the relative frequencies of l s and therefore always increases the estimate of si x y
in section NUM NUM we describe how we have applied maximum entropy modeling to predict the french translation of an english word in context
our hansard corpus contains NUM NUM million english french sentence pairs for a total of a little under NUM million words in each language
we denote the number of words in the sentence e by iei and the number of words in the sentence f by ifi
in creating p riftlx we are at least in principle modeling the decisions of an expert french segmenter
a few actual examples of such events for in are depicted in table NUM next we define the set of candidate features
but the feature selection problem is critical since the universe of possible constraints is typically in the thousands or even millions
berger della pietra and della pietra a maximum entropy approach table NUM noun de noun phrases and their english equivalents
for more details on the specific system approaches see the complete overview of the trec NUM conference including papers from the participating groups NUM
phase two of the tipster project included two workshops for evaluating document detection information retrieval projects the third and fourth text retrieval conferences trecs
a dominant feature of the adhoc task in trec NUM was the much shorter topics see more on this in the discussion of the topics section NUM NUM
in this case the goal was to investigate techniques for merging results from the various trec subcouections as opposed to treating the collections as a single entity
it is not clear whether this difference comes from the increased precision of the soft boolean approach or from the relatively poor performance of the pircs term expansion results
uwgcll university of waterloo shortest substring ranking multitext experiments for trec NUM by charles l a clarke gordon v cormack and forbes j
this task is similar to how a researcher might use a library where the collection is known but where the questions likely to be asked are unknown
the topics were expanded by NUM phrases that were automaticauy selected from a phrase thesaurus that had been previously built automatically from the entire corpus of documents
there are thesaurus tools available for expansion and this run was the result of many experiments into such issues as term groupings and assignment of term strengths
robertson s walker m m beaulieu m gatford and a payne used a probabilistic term weighting scheme similar to that used in trec NUM
table NUM describes the top k bfs and the semantics of the supporting functions
as such they would be an appropriate starting point for both a broad coverage semantic lexicon and for the semantic tagging of corpora
ment group lcb h b t rcb healer the doctor treated bodypart my knee with heat
the major challenge is to make the resulting large statistical model more understandable by humans so that intuitions can be used to improve it
given that there was not a large dictionary of chinese words with parts of speech a high percentage of words in the text were unknown
we focus below on other major issues we are confronting in interpreting the structure of frames as expressed by fegs
phase iii plans on the research front as a tipster phase iii contractor sra intends to make significant advances in the customizability of extraction technology primarily by bringing together our expertise in machine learning and natural language processing
language of health and sickness and showing how the elements and structure of this frame would be identiffed and described
however the adjunction permitted in tig is sufficiently restricted that tigs only derive context free languages and tigs have the same cubic time worst case complexity bounds for recognition and parsing as context free grammars
simultaneous adjunction is specified by two sequences one of left auxiliary trees and the other of right auxiliary trees that specify the order of the strings corresponding to the trees combined
as a result even though every tree has a unique left anchor a given chart state can correspond to a set of such trees and therefore a set of such anchors
the fact that tigs can only generate context free languages follows from the fact that any tig can be converted into a cfg generating the same language as shown in the following theorem
however this does not alter the strings that are generated because by the definition of tig the structure to the right of the spine of t must be entirely empty
any complete derivation in g can be converted into exactly one derivation in g as follows every instance of a tree in t has to occur in a substitution chain
the chain consists of some number of instances h t2 tm of trees in t with each tree substituted for the leftmost nonempty frontier node of the next
since there are no x rooted trees in a there can not be any adjunction on the root of u or on the roots of any of the trees in the chain
moreover this computational linguistics volume NUM number NUM grammar can be left anchored one where the first element of the right hand side of each rule is a terminal symbol
the authors wish to thank four anonymous reviewers for computational linguistics for useful comments on this paper
consider next the ambiguity in dutch between en verb forms and en plural nouns
these verbs while possible in the infinitival form occur predominantly in the finite form
results of tenfold cross validation for dutch en verb forms from the uit den boogaart corpus
but for infrequent or unseen forms it is less clear how to compute the estimate
consider another dutch example like aanlokken entice appeal
thus we expect an imbalance of the kind we observe
this in turn biases the overall mle and makes it a poor predictor of novel cases
nonetheless the hapax based mle remains a significantly better predictor overall
in general we found that language style has great influence on the realization choices
separate sets of training and evaluation data for the tagger were obtained from from the penn treebank wall street corpus
the accuracy of the different models in a ssigning the most likely poss to words is summarized in figure NUM
this method only achieved a median accuracy of NUM which is worse than always choosing the rightmost attachment site
while these results do not prove that modeling feature interactions is necessary we believe that they provide a strong indication
to understand the difficulties that collocations pose for translation consider sentences le and lf in figure NUM
the french equivalent monsieur le prdsident is not the literal translation but instead uses the translation of the term president
champollion now gives all the candidate final translations that is the best translations at each stage of the iteration process
on the first algorithm set we evaluate and compare the correlation of discourse segmentation with three types of linguistic cues referential noun phrases cue words and pauses
we analyze the errors of our initial algorithms in order to identify a set of enriched input features and to determine how to combine information from the three linguistic knowledge sources
we then analyze the distributions in more detail to determine what aspects of the distribution are significant and thereby to abstract significant data for use in defining segmentations for each narrative
finally we quantify our results using a significance passonneau and litman discourse segmentation test a reliability measure and for purposes of comparison with other work percent agreement
41roupe erniinologie el lnl elligence ari iticielle i i lcb c i i NUM
in the second part of our study data abstracted from the subjects segmentations serve as a target for evaluating two sets of algorithms that use utterance features to perform segmentation
automatic exploration of a sublanguage corpus constitutes a first step towards identifying the semantic classes and relationships which are relevant for this sublanguage
the value of do proportion of observed disagreements is then simply where m is the total number of mismatches j being the potential number of matches
thus an appropriate test of whether our method is statistically reliable would be to compare two repetitions of the method on the same narratives to see if the results are reproducible
characters in p are stored in two arrays pi and p2
those knowledge structm es arc built within the lexical knowledge base lkb integrating lnultiple parts of the i kt around a particular concept to form a clus ter and express the multiple relations among the words in that cluster
it introduction when constructing a l exieal knowledge ilase NUM kb useful for natural l anguage processing the source of information from which knowledge is acquired and the structuring of this information within the lkb are two key issues
by using the american lh ritage first l ictionary a s our source of lexical information we were able to restrict our vocabulary to result ill a project of reasonable size dealing with general knowledge about lay to day concepts and actions
in the example of figure NUM when using tile concept hierarchy to establish the similarity between pen and crayon we find that one is a subclass of lool and the other of wax both then are substoned by the general concept something
figure NUM shows the starting point of an integration process with the trigger word tw lelter its definition its temporary graph tg the concept cluster cc containing only the trigger word and the cckg being the same as the temporary graph
general knowledge or usage this gives information useflfl in daily life like how to use an object what it is made of what it looks llke etc specific example this presents a typical situation using the word defined and it involves specific persons and actions
NUM the set of closed class words ex of to in and NUM relations extracted via defining formulas ex partof made of instrument defining formulas correspond to phrasal patterns that occur often through the dictionary suggesting particular semantic relations ix
a classification is said to be useful if it can contribute to a more accurate linguistic parse of given sentences
the two types of use are related to the two types of approach to the subject linguistic and engineering
next we need to find out how much of an improvement we can achieve using this new bigram model
o the nature of the lexieo sema ntic
from real texts is des ril ed
a system for the resolutio of synla
tests of s msoll s perforln mce are illustra ted
are kilo vii xa nil les
the t ypoh gy of linguistic inl ol ma tiou
under normal circumstances however the se cm will be aware that changes are likely to be forthcoming from an application under development
these two cases are discussed as follows
the highest scored candidate is selected and added to the list of solution
NUM to put or kcep money in a bank
the extracted word pairs are used to match words in st and tt
in the next section we describe sensealign and discuss its main components
the pair of words s t is called a connection
under this proposal a rough chm acter by character alignment is first performed
subsequently the rest of the candidates are re evaluated again
using an em algorithm for model NUM brown et al
this algorithm was evaluated on a noisy english french technical document
the generic extraction system to accomplish the goal of providing a core extraction capability that can be easily customized to a particular domain lockheed martin developed a genetic text processing system
in addition a range of experiments were performed to extend previous classification results NUM to routing and to compare the resulting system with those used in trec NUM
NUM they contributed to defining a tipster architecture testing it and implementing a multiplatform version of the document manager defined by the architecture
the document manager specifies a set of functions that should be used to get text for text processing and to store the output of text processing
this work was evaluated during muc NUM and performed extremely well
oir has been chartered to implement enterprise wide systems to collect archive distribute and manage information obtained from unclassified data sources typically called open sources NUM
this work was performed jointly with ge cr d NUM and shows promising results for large scale integration the work will be integrated for the trec NUM evaluation
lockheed martin has implemented a document manager that accesses documents stored in a variety of databases
aimed at alleviating problems currently being faced by the office of information resources oir
given a sequence of words lcb wl wn rcb and tags lcb tl t rcb as training data define hi as the history available when predicting ti
the features which ask about previous tags and surrounding words now additionally ask about the identity of the current word e.g. a specialized feature for the word about in table NUM could be
in practice the model for the experiment shown in table NUM requires approximately NUM hours to train and NUM hour to test NUM on an ibm rs NUM model NUM with 256mb of ram
if the tag dictionary is in effect the search procedure for known words generates only tags given by the dictionary entry while for unknown words generates all tags in the tag set
the joint probability of a history h and tag t is determined by those parameters whose corresponding features are active i.e. those o j such that fj h t NUM
table NUM features generated from h3 for
it aims to achieve NUM percent accuracy in two nrore years
figure NUM the conceptual diagram of the infor mation platform
language engineering implenicnts functions of a language and inforillation via computers
about thirty five thousand sentences are tangible in version NUM platform
there are four sorts of corpora from contemporary korean texts
the project will continue till the years of twenty first century
an alignment system gathers correspondences between surface representations of both languages
its current accuracy is about NUM for the trained data
we assorted NUM NUM entries each for culture art and korean classical literature
for the extensibility and adaptability we have devised standard dictionary markup language based on sgml
if they are diflhrent singleton sets the training instances were inconsistent
taking as input the first elelneut of tim sibling list the
severa NUM additional points regarding the learning process need to be lnadc
it has been proposed in the computational linguistics literature e.g.
otherwise a new training instance is accepted
violating those rules by a teacher
indeed the japanese development and test articles represented percentages in various other ways such as an the kanji for percent e.g. ts lj
other semantic clues such as the locative designators prefecture or rcb h state assisted in recognizing more obscure place names
identifying and categorizing complex noun phrases in strings where there is no capitalization and whitespace make this type of expression the most difficult to process group f measure average NUM
as in english the typical japanese contextual pattern for generating a valid percent tag was an arabic numeral the sign e.g. NUM
for this reason and based upon the authors own manual tagging experience we felt that identification of enamex types would be the most challenging to the participant systems
one motivation for conducting the named entity task in a foreign language such as japanese was to promote techniques for tackling language specific difficulties in recognizing the names of people and organizations
although the above mentioned patterns are more varied than what one typically encounters in english texts they nevertheless constitute a standard finite list which the participant systems processed well
for instance u s japan trade negotiations would be an event not captured by a singular tag but by two location tags for u s and japan
typically money was specified by an arabic numeral a monetary unit in katakana e.g. NUM iz p NUM million
easy to use date oriented database selection screen
these interfaces shall describe all the external information needed by the component
this shall include any specific standards and protocols allowed in the architecture
security requirements are applicable to this requirement
normalized fills e pointers to other entities
interfaces should be specified to facilitate future development of an application language
the architecture shall provide for extension and adoption of new implementation approaches
this shall provide a basis for any future enhancements to the architecture
these may be implemented at the api level of the software components
e rates for lexical accommodation for all three experiments were significantly different from the level established for coincidental lexical overlap figure NUM table NUM data were subjected to two way analyses of variance
the accommodation rate for each conversation in the three experiments was calculated by dividing the number of different lexical items the two speakers had in common by the total number of different lexical items in the conversation
this is orthographically represented as so that door would be and in this case the hanzi t does not represent a syllable
the cost estimate cost l lcb is computed in the obvious way by summing the negative log probabilities of and
orthographic words are thus only a starting point for further analysis and can only be regarded as a useful hint at the desired division of the sentence into words
each word is terminated by an arc that represents the transduction between and the part of speech of that word weighted with an estimated cost for that word
thus in an english sentence such as i m going to show up at the acl one would reasonably conjecture that there are eight words separated by seven spaces
for a sequence of hanzi that is a possible name we wish to assign a probability to that sequence qua name
figure NUM attached at the end of the paper represents the overall gui picture
automatically identifying meaningful terms from naturally running texts has been an important task for information technologists
the tree is a two dimensional visualization of the term hierarchy
the user begins term navigation by selecting from a list of available topics
operations for growing shrinking and manipulating the tree visualization have been implemented
we view our prototype system as a means to achieve information visualization
upon location the selected term is always highlighted within the document browser
figures NUM and NUM capture the area where the interaction with the key term structures occurs
an ficu contains a single tensed clause that is neither a verb argument nor a restrictive relative clause potentially with sentence fragments or repairs
the large proportion of white space to black space in the left bar chart of figure NUM illustrates graphically that subjects assign boundaries relatively infrequently
as shown in figure NUM we used a simplification of these results to develop an algorithm for identifying boundaries in our corpus using pauses
in contrast the algorithms presented in section NUM NUM are developed using the NUM narratives previously used for testing as a training set of narratives
if ficui contains no third person definite pronoun coreferring with an np in any prior ficu up to the last boundary assigned by the algorithm
the summed deviatio n metric which takes all the metrics into account shows that on the whole performance is considerably worse than humans
these results suggest that another way to improve performance is to consider more sophisticated methods for combining features across the three types of linguistic devices
first we must take into account the dimensions along which the three algorithms differ apart from the different types of linguistic information used
misclassification of boundaries c type errors see figure NUM often occurred where prosodic and cue features conflicted with np features
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
at this stage the unregistered word jql j is already captured by using a pattern marcher in NUM NUM
in ambiguous cases the module is designed to make conservative decisions such as including non names or non name parts in otherwise valid name sequences
since the maximization is carried out with fixed character sequence c the word segmenter only has to maximize the joint probability of word sequence and tag sequence p w t
the second modification is that we prune the expected n gram counts in the unsegmented corpus if they are lower than a predefined threshold before computing equation NUM and NUM
is NUM NUM and the precision is NUM NUM figure NUM shows excerpts of correctly extracted new words matched incorrectly extracted word hypotheses sys matched and new words that
the only effect of reestimation in our experiment is to increase the expected word frequencies of the unknown word hypotheses whose expected word frequencies are greater than the pruning threshold of reestimation
as for the word model it seems the combination of the spelling model for all words and the length model for low frequency words is the best but the difference is small
we made two test sets from the rest of the corpus one for a small size experiment NUM sentences and the other for a medium size experiment NUM sentences
most japanese nlp applications require word segmentation as a first stage because there are phonological units and semantic units whose pronunciation and or meaning is not trivially derivable from the pronunciation and or meaning of the individual characters
null it is often the case that we have overlapping word hypotheses if the input sentence contains unknown words such as p NUM
this working session will address these problems and seek solutions to them
d the international section it contains many items of foreign news and has NUM NUM words and NUM NUM characters
the model also has a separate algorithm that handles harmonic effects by looking for multiple segmental changes in the same word and has separate processes to deal with epenthesis and deletion rules
without an ability to use knowledge about phonological features to generalize across phones ostia s transducers have missing transitions for certain phones from certain states
trees on the arcs of the transducer improved the generalization behavior of our transducers we found that some transducers needed to be generalized even further
second we assume that a cognitive model of automaton induction would be more stochastic and hence more robust than the ostia algorithm underlying our work
in each of these cases a general domain independent learning rule bp ibl mdl is used to learn directly from the data
in addition the incorrect distribution of output symbols prevents the optimal merging of states during the learning process resulting in large and inaccurate transducers
2b shows a positive application of the rule 2c shows a case where the conditions for the rule are not met
this still leaves a large space of permissible orderings and as can be seen from our results the ordering chosen can have a significant effect on the algorithm s outcome
when performing the state mergers of the ostia algorithm two variables are considered to be the same symbol if they agree in both components the index and list of phonological features
well as sentences with verb predicates are processed normally as described ill section NUM
construction many japmmse adjective predicates domhmtc two subjective cases and so form the double subject construction
this constn ction includes both adverbial particle wa and subjective case marking p trticle ga
this paper describes a mefllod for malyzing japanese double subject constniction having an adjective predicate based on the valency structure
as a result the subtrees in the deep part are highly generalized
the one used in the next section was set by the following method
class translation figure NUM case prame tree learned by dtla
we would like to report those features at another opportunity after further experiments
but how can we introduce a thesaurus into the conventional dtla framework
most important one is for parameter a in formula NUM
the 6without this clause the algorithm is just a conventional dtla
the nouns are the most problematic
this package has many parameter setting options
entropy is not a quantity that can be directly measured
a key word in the last paragraph is indistinguishable
the hypothesis would of course be a very useful if true
it is not obvious and i am currently investigating the question further
el secondly our concern is with language corpora not with texts
the measure is compared with a rank based measure and shown to outperform it
words are far easier to count accurately than syntactic categories or word senses
vprovided all expected values are over a threshold of NUM
much of the time the null hypothesis is defeated
the novelty of this approach lies in the automatic comparison of two sample datasets a topic focused dataset based on a predefined topic and a larger and more general base dataset
however to make the feature ranking computation tractable we make the approximation that the addition of a feature f affects only o leaving the values associated with other features unchanged
the system estimates p fie the probability that a french sentence f is a translation of e using a parametric model of the process of english to french translation known as a translation model
with no knowledge of context an expert translator is also quite likely to select superior as the english word generating typical errors encountered in using em based model of brown et al in a french to english translation system
however a fundamental principle in the theory of lagrange multipliers called generically the kuhn tucker theorem asserts that under suitable assumptions the primal and dual problems are in fact closely related
wall street speculators who rank among the best paid statistical modelers build models based on past stock price movements to predict tomorrow s fluctuations and alter their portfolios to capitalize on the predicted future
to test the model we constructed a noun de noun word reordering module which interchanges the order of the nouns if p interchange x NUM NUM and keeps the order the same otherwise
for now we can imagine that these training samples have been generated by a human expert who was presented with a number of random phrases containing in and asked to choose a good translation for each
a feature is a binary valued function of x y a constraint is an equation between the expected value of the feature function in the model and its expected value in the training data
and the optimal model by p suf argmax g s f c NUM p f despite the rather unwieldy notation the idea is simple
for example for the partial sentence zuo4 were2 le where le functions as labeling the verb zuo4 wa u2 as perfect tense some subjects tend to segment it as two words zuo4 an2 le while the other treat it as one single word
this can be explained by the fact that the change of segmentation is very little which is reflected in table reftab accuracy iter and the addition of unseen words NUM NUM k to the vocabulary is also too little to affect the overall perplexity
however since the lms are trained using the presegmented data the trigram model tends to keep the original segmentation because it takes the preceding two words into account while the unigram model is less restricted to deviate from the original segmentation
second the agreement of each of the subjects with either the original trigram or unigram segmentation is quite high see columns NUM NUM and NUM in table NUM and appears to be specific to the subject
we can make a few remarks about the result in table NUM first of all it is interesting to note that the agreement of segmentations among human subjects is roughly at the same level of that between human subjects and machine
the result is summed in table NUM where org stands for the original segmentation p1 p2 and p3 for three human subjects and tri and uni stand for the segmentations generated by trigram lm and unigram lm respectively
for example he segments thesentence NUM dao4 jial li2 chil dun4 fan4 see table NUM as two words dao4 j al l NUM go home and chil dun4 fem4 have a meal because the two words are clearly two semantic units
table NUM shows the figure of merit of the resulting segmentation of the NUM sentence test set described earlier
the other two subjects and machine segment it as dao4 jial li2 chil dtm4 fern4
in the last two years the nlp group has been joined by the world languages research group whose current mission is to extend the nlp group s technology to both european and far eastern languages and by the natural language development group which is responsible for transferring the technology into microsoft products
finally there will be a brief demonstration of the grammar checking functionality in the context of the more general nl understanding system
there will also be a description of how the three nl groups at microsoft have collaborated successfully to move the technology into real world products
the natural language processing nlp group at microsoft research has been working for over five years on the development of a broad coverage nl understanding system
some usability and integration issues such as background processing will also be discussed
microsoft natural language understanding system and grammar checker
the grammar checker will be demonstrated in the context of word NUM showing a number of the errors it detects and some of the rewrites it suggests
publications describing the work of the nlp group at microsoft research may be accessed on the world wide web at http www research microsoft com
it is envisioned that this very general and flexible component together with the other components under development will provide the basis for many nl enabled functions as well as specific applications other than grammar checking
a scheme in which the indices were handles referring to subexpressions in any variety of fiat semantics could have been treated in the same way
the final interaction is between the first and second edges in NUM which give rise to the edge in NUM
in other words the semantics of a phrase must contain all predicates from the input specification that refer to any indices internal to it
they can not fill this role in generation because they are not natural properties of the semantic expressions that are the input to the process
the variables in the cat and semantics columns of NUM provide the essential link between syntax and semantics
we take up the complexity issue first and then turn to bow the efficiency of the generation chart might be enhanced through indexing
unfortunately we often do not know how to compute terms weights
therefore we started experimenting with manual and automatic query building techniques
on the other hand full text query expansion works remarkably well
table NUM precision improvements over stems
we used also a plain text stream
building effective queries in natural language information retrieval
whenever an unseen character is found it is assigned a character value that is the negative of the amount of different NUM byte characters seen so far
it has to scan left or right until a one byte character the beginning of the text string or the end of the text string is encountered
for instance if iqcl NUM and i NUM i0 NUM then about NUM NUM milts of storage integers are needed
a direct approach is to compute the conceptual automaton me which regards the NUM byte characters as one byte and then convert the automaton for multi byte processing
in the case that there is ambiguity in the final morphological analysis of a given compound noun the morphological analyzer picks up the solution with the least number of segmentations
note that no part of a phrase such as a c b c c is picked up so that erroneous evidence can be to avoided
it employs boot strapping search to cope with unregistered words if an unregistered word is lound in the process of searching the examples it is recorded and invokes additional searches to gather the examples containing it
in japanese if the two children are both content words the value of the head attribute of the parent node is usually identical to the value of the hend attribute of the right daughter
however the accuracy of their results on NUM kanji compound nouns is NUM unless they combine their conceptual dependency model with a heuristic using the distance of modifier and modifee
but also registered words such as as and unregistered words such as j h
this results in two different disambiguation algorithms the learning phase is used only to remove hell esl s from the collision sets without forcing any paradise choice e.g. a maximum likelihood candidate
because of these inherent difficulties we believe that syntactic learning should be a gradual process in which the most difficult decisions are made as late as possible using increasingly refined levels of knowledge
only if we can rely on some apriori model of the world even a naive model NUM to guide difficult choices then we can hope in a better coverage of repetitive phenomena
null the average mutual information was evaluated by first computing in the standard way the mutual information of all the pairs of esl s that co occurred in at least one collision set
furthermore similar sentences like for example imposta sul reddito delle societa tax on the income of companies always have a human entity as head modifier
where desired the lexicon fst can be composed with only the upper set of rules to make a lexical transducer that gencratcs and recognizes only fully roweled surface forms l or general recognition both sets of rules a s shown in figure NUM are composed
such a pedagogical decision has of course important consequences on the system s architecture a bilingual approach requiring several components of a mt system
yet there are several generic programs that support the contextual interpretation of the student s input linguistic or graphical tracking his her goals and providing cooperative responses
in order to deal with the student s errors in a principled way the grammar should anticipate typical errors and annotate them for automatic recovery and explanation generation
intelligent planners linear or nonlinear could be used in plan based tutorials with the microworlds defining the possible limits of departure from the expectation based tutorial phms
lexical thesauri since word acquisition is a crucial part of language learning a thesaurus such as wordnet is practically a must in a broader call system
mouse dragging the waveform followed by the synthesized result the pedagogical value and user acceptability of a call system would certainly be greatly enhanced
with a graphical representation of the two pronunciations waveform pitch duration etc and a means to operate on them e.g.
while introspection and observation are a first step in determining typical errors data gathered in a corpus are a nmch more reliable approach
such a tool could provide lists of synonyms antonyms hyper hyponyms meronyms and contexts in which these words are used
nevertheless the hypermedia technology did solve one very important aspect of computer assisted learning by putting the student in a visual environment
the probabilities are kept the same
figure NUM the elementary trees for the example 3sat instance
a parse is possibly generatable by many derivations
the decision problem mps is np complete also under scfg
these sentences do not have second type derivations
stsgs and scfgs are closely related
NUM NUM stochastic tree substltution grammar stsg
for our present purposes the adequacy of lists of frame elements such as what we present in table NUM for the vocabulary domain of health care can be established only if precisely these elements are the ones that are needed for distinguishing the semantic and combinatorial properties of the major lexical items that belong to that domain
in the use where it refers to the growth of new tissue over a wound it can be found in both transitive and intransitive clauses the cut healed lcb w rcb and the ointment healed the cut the ointment facilitated the natural process of healing lcb m w rcb
we can move to another frame to illustrate how frame based annotation would be accomplished by considering a few words from the 5we leave out of this account the inheritance of a higher level exchange frame in the commercial transaction fralne and the means for showing that a completed instance of the realestatetransaction scene is a prerequisite to the enactment of the associated
in the case of inheritance for example the information that it gets used for buying something will make clear that this is an instance of estate inheritance rather than genetic inheritance or frame inheritance and the phrasing her portion fits frame understandings about the distribution of an inheritance among multiple heirs
but there is also a purely transitive use with a meaning very close to that of cure with lcb h d rcb or lcb m d rcb as in the shaman healed my influenza or the waters healed my arthritis and this use of heal usually implies something extra medical or supernatural
so for example if we are examining the commercial transaction frame we will need to identify such frame elements as buyer seller payment goods etc and we can speak of such words as buy sell pay charge customer merchant clerk etc as capable of evoking this frame
for example while we can agree that the treatment element in the previous examples was merely unmentioned the omission of the disease element in a sentence like the doctor cured me has a somewhat different status there is clearly some disease that the speaker has in mind and its omission is licensed by the assumption that its nature is given in the context
this could be done in a fairly natural and transparent way as long as the tags were clearly seen as the names of frame elements specifically related to the head verb bought in that sentence
this model achieved a median accuracy of NUM
there were NUM NUM such cases in the training data
mood is in the first instance tim grammatical realization of semantic speech function
we develop concrete mappings between the extra linguistic and semantic strata
the speech function is question since the system wants to initiate a response
for a more detailed account see NUM
figure NUM key systems in declarative clauses simpli
moves begin with upper case and acts with lower case
section NUM presents our dialogue model and the intonational resources
speech functions comprise command offer statement and question
would you close the window please
and the models were evaluated on the evaluation data
model theoretic intert retations for lfg fstructures
o translates into where the f structure reentrancy surfaces in terms of identical qlf term indices and metaw riables NUM r i as required
while the initial definition of r is designed to maxilnally exploit NUM he contextual resolution of qlf later ve rskms nfininfise resolution efl ecl s
for an arbitrary qlf c however the reverse does not hold w assigns a meaning to an f structure that depends on the f structure and qlf contextual resolution
ps the core of a mapping taking us from fstructures to qlfs places the values of subcategorizable grammmatieal fnnctions into their argument positions in the governing semantic form and recurses on those arguments
modifier ordering can then be transferred to resolution or encoded in the categories of the rest r lion and modifiers to filrther constrain the order of application selected by resolution
as an example the reader may verify that r retranslates the qlf associated with most representatives persuaded a candidate to support every subsidiary back into the f structure associated with the sentence as required
the qlf formalisln delitmratcly leaves entirely open the amounl of syntactic information that should be encoded within a qlf the decision rests on how much syntactic intbrmation is required for successful contextual resolution
sif the results of linear logic deductions are interpreted in terms of the supervaluation construction we have preservation of truth directly with respect to underspecified representations qlfs and sets of linear logic premisses
for this reason only tuples which contained the preposition were used in backed off estimates this reduces the problem to a choice between NUM triples and NUM pairs at each respective stage
the attachment decisions for these triples were unknown so an unsupervised training method was used section NUM NUM describes the algorithm in more detail
even if f v nl p n2 NUM it may still be very low and this may make the above mle estimate inaccurate
both of these methods consider the second noun n2 as well as v nl and p with the hope that this additional information will improve results
in an effort to reduce sparse data problems the following processing was run over both test and training data all NUM digit numbers were replaced with the string year
itindle and rooth used a partial paxser to extract head nouns from a corpus together with a preceding verb and a followillg preposition giving a table of v nl p triples
the probability of the attachment variable a being NUM or NUM signifying noun or verb attachment respectively is a probability p which is conditional on the values of the words in the quadruple
meanwhile we have to include nodes that govern lm human NUM i.e.
since it is not easy to find the best value before a trial we used a heuristic method
best performance for a particular natural language learning task
associated with object of focus e.g. the subject
we will refer to these as solution features
the weights were chosen more or less arbitrarily
the final row of results will be described below
automating feature set selection for case based learning of linguistic knowledge
table NUM results for the recency bias representations
none of the changes is statistically significant
table NUM results for the subject accessibility
we want high level generalization but low level translation errors but how do we achieve this in an optimum way
this may or may not be worth the cost of analysis if system improvement is driven solely by pipingmore and more massive amounts of development data into a statistical learning engine
if sufficiently massive and varied development data is available presumably the system eventually will train upon something approaching all of the relevant data subtypes without any need to know and describe what those subtypes are
and one might desire at some point in the system development loop to capture these observations systematically so as to direct efforts at system improvement
one would expect observant end users of information extraction systems to notice rather quickly that certain high frequency hard to get or thematically significant categories of names are missing or incorrect in the output
as suggested above variations of the above procedure can be used to generate profiles of the data in order to direct efforts at system improvement
the inqi01 run had NUM topics with superior performance to the westpl run mostly because of new relevant documents being retrieved to the top NUM document set
the further directions for this research will concern with unknown word processing and increase the accuracy of the gradual refinement method
in thai implicit spelling errors can occur more easily than in english because there are NUM distinctive characters on each keypad
chance of the second coder also doing so
kid designate one particular coder as the expert
for instance one can imagine determining whether coders using a simplified coding scheme match what can be obtained by some better but more expensive method which might itself be either objective or subjective
silverman et al treat all coders indistinguishably although they do build an interesting argument about how agreement levels shift when a number of less experienced transcribers are added to a pool of highly experienced ones
dividing newspaper articles based on subject matter
in assessing the amount of agreement among coders of category distinctions the kappa statistic normalizes for the amount of expected chance agreement and allows a single measure to be calculated over multiple coders
measure NUM still falls foul of the same problem with expected chance agreement as measure NUM because it does not take into account the number of categories occurring in the coding scheme
this is a reasonable requirement because if researchers can not even show that people can agree about the judgments on which their research is based then there is no chance of replicating the research results
it is more important to ask how different the results are from random and whether or not the data produced by coding is too noisy to use for the purpose for which it was collected
krippendorff gives well established techniques that generalize on this sort of odd man out result which involve isolating particular coders categories and kinds of units to establish the source of any disagreement
the priority once calculated is available as an attribute for sorting documents for subsequent processing whether viewing by the user information extraction or subsequent routing
this sub set or its extracted information is then presented to the user passed to other components or stored for later use
corrections to a document shall be applied so as to leave the original document as defined in paragraph NUM NUM intact
the architecture shall be based on multi lingual layers so that additional languages can easily be added without recreating the basic application
the architecture shall support a detection component that allows foreign language documents to be searched or distributed using english language selection statements
NUM NUM NUM NUM NUM the application should provide any necessary assistance for selection statement generation
in the future the architecture shall allow construction combining of templates objects and patterns by automated processes and the libraries updated automatically
the architecture shall maintain the logical association between the english version of the query and the foreign language version of the query
in the case of the extraction component estimate it is the component s confidence in its process for filling each slot
the architecture shall specify how to store and pass filled templates between components as well as the detail representation of these types
in seeing this sentence merely as an expression evoking the commercial transaction frame we could begin by tagging the subject of the sentence george s cousin as the buyer and the object a new metcedes as the goods and the oblique object her portion of the inheritance marked by the preposition with as the payment
the first iteration of step NUM on our example collocation would select the word officielles among the NUM words in s as the first candidate translation with a score of NUM NUM
for both cases the second column indicates the number of collocations that were correctly translated with si and the third column indicates the number of these collocations that were incorrectly translated with si
what is clearly needed is a way to determine the generality of each produced translation as many translations found by champouion are of general use and could be directly applied to other domains
for example using our scoring technique the correlation of the collocation official languages with the french word officielles is equal to NUM NUM and the correlation with the french collocation langues officielles is NUM NUM
although organizations such as the united nations the european community and governments of countries with several official languages are big producers such corpora are still difficult to obtain for research purposes
in practice about NUM NUM of the words in the list fail to meet the two tests above so we dramatically reduce our search space without having to perform any relatively expensive operations
for a given source language collocation champollion identifies individual words in the target language that are highly correlated with the source collocation thus producing a set of words in the target language
the dependence of si x y on the marginal probabilities of l s shows that using it would make rare word groups look more similar than they really are
the third set of collocations c3 consists of NUM collocations selected computational linguistics volume NUM number NUM NUM applications a bilingual lexicon of collocations has a variety of potential uses
in the current implementation of champollion we were restricted to using tools for only one of the two languages since at the time of implementation tools for french were not readily available
the trees containing boy and saw are initial trees
the adjunction of a left auxiliary tree is referred to as left adjunction
given only initial trees the final conversion to a cfg is trivial
in addition there can be several different derivations for the same tree
rule NUM recognizes the presence of a terminal symbol in the input string
the algorithm takes o igi2n NUM time in the worst case
the adjunction of a wrapping auxiliary tree is referred to as wrapping adjunction
it must also be the case that left adjunction is not possible on aa
similarly a tig is lexicalized if every tree contains a terminal symbol
preferences for picking one inference chain over another were determined by the focusing heuristics which provide ordered expectations of discourse actions given the existing plan tree
a system which processes spoken language must address all of the ambiguities arising when processing written language plus other ambiguities specitie to the speech processing task
the discourse l roeessor also updal es a calendar which keeps track of what the speakers h we said m out their schedules
in determining how the inference chain attaches to the plan tree the speech act is recognized since each inference chain is associated with a single speech act
the paper is organized as follows fh st we briefly introduce the enthusiast speech translation system and discuss the ambiguity problem in enthusiast
secondly we needed to quantify the disambiguating predictions made by the plan based discourse processor in order to combine these predictions with the non context based ones
atl ach meat preferences by the focusing heuristics are translated into numerical preference scores based on attachment positions and the length of the in ference chains
discourse processor was extended to accept multiple ilts as input it became clear that ibr most ambignous parses the original focusing heuristics did not provide enough information to distinguish among the alternatives
out of conte x it is iml ossil le to tell which is the i cst intert retation
for training wc partitioned the de velopment set into sev ral dilt erent sized sets in order to st the elfeels of training corpus sizes
our spanish part of speech tagger is a successful implementation and extension of brill s unsuper vised learning algorithm that reduces the ambiguity of part of speech tags on words in spanish texts
the NUM reakdown can t each word in the test set the accuracy is NUM NUM NUM NUM with the simple verh tag set
since this phenomenon never occurred in any of the other learning rims one can see that the learning pro eess can be heavily influenced by the choice of it put texts
as can be seen from the tables performance increased as the size of the learning set incre ased up to the medium set where the score levelled otf
ills initial state tagging accuracy was NUM NUM whictl is considerably higher than our sl a ish case NUM NUM
no training of NUM NUM while the best accuracy of our tagger is currently NUM NUM for the simple tag set NUM tags with the base accuracy of NUM NUM
one surprising data t oint in the simple verb tag set experiments was the full score which dropped mmost NUM fi om the medium score
the library is being expanded with routines for semantic construction driven by semantic types
the poi up menu allows a user to pertbrm single derivation steps
to akl comparison of theories there are translation routines between some semantic tbrmalisms
thus there was theoretical motivation to ensure components of the system were shared wherever possible
one of the aims in building the tool was to show where semantic formalisms converge
it is intended to be used as both a research tool and a tutorim tool
the next section outlines the library contents and the system architecture which was designed to reflect convergence between theories
this allows differences to be located in as small pieces of code as possible e.g.
with restleet to auxiliaries the standard raising at preach that is usually adopted yields undesirable structural complexity and results in idiosyncratic language particular analyses of the role of auxiliaries
rthe annotation p m in NUM refers to the m structure associated with the parent c structure node and t t refers to the m structure associated with the daughter node
in paragraph this attribute records the location of a given sentence within the paragraph
in particular the easil ility of levelot ing paralm grammars for differing languages is greatly increased through the formula don of uniformly applicable transparent analyses
c4 NUM works with coded descriptions of data or cases
furthermore a representation of arguinent structure is implenmnted that is related to NUM ut not idcnl ical to the ret resentation of grainmatical flmel ions
further we use information retrieval metrics recall and precision to quantify the performance of the decision tree approach
both give sentences whose semantics subsumes the entire input
several things are noteworthy about the process just outlined
charts constitute a natural uniform architecture for parsing and generation provided string position is replaced by a notion more appropriate to logical forms and that measures are taken to curtail generation paths containing semantically incomplete phrases
while the lexicon may have words to express these predicates the grammar has no way of associating their referents with the above noun phrases because the variables corresponding to those referents are internal
the grammar has access to indices only through the variables that annotate grammatical categories in its rules so that rules that incorporate this sentence into larger phrases can have no further access to the index p
this method uses a small number of parameters compared to previous methods based on word trigrams
would be assigned to each word in the confusion set when substituted into the target sentence
in the different tags condition tribayes invokes trigrams and thus scores identically
bayes learns these features from a training corpus of correct text
bayes uses two types of features context words and collocations
table NUM shows the performance of tribayes compared to its components
in the same tags condition tribayes invokes bayes
we then invoked the grammar checker accepting every suggestion offered
the methods handle multiple confusion sets by applying the same technique to each confusion set independently
first we introduce a method called trigrams that uses part of speech trigrams to encode the context
null for example consider the following sentence fragment null imposta sul reddito delle persone tax on the income of people that occurs in the ld corpus almost NUM times
learning and testing disambiguation cues according to the disambiguate as late as possible strategy the learning and testing phases have different objectives null during the learning phase the objective is to take only highly reliable decisions by eliminating those esl s with a very low plausibility while delaying unreliable choices
table NUM performance values of the mcp1 without learning
i introduction in natural language processing the importance of large volume corpus has been pointed out together with the need for technology of analyzing these linguistic data
prepare pt NUM pointer table o of n records of sp source pointer with the values of NUM NUM NUM i n NUM
here the value i represents the string word i which is the substring from position i to the last character n NUM address in the source text
first from the view point of collocational expression extraction the problems of nagao and moffs algorithm for calculating arbitrary length of n gram has been pointed out
in this experiments xerox NUM in the case of a there would be a combination of substrings which is regarded as a interrupted collocation
procedure NUM numbering of the sentences sn sentence number field is added for entering the sentence number of original sentence to which one s record belongs
procedure NUM table condensation the table obtained is condensed by procedures shown in the following to obtain a spt NUM
these number are registered in the nes number of extracted substrings field of the respective record in spt NUM
when considering the collocation of substrings within a sentence combinations of expressions spread over borders of sentences need to be excluded
in this example the types of substrings extracted by the conventional algorithm amounted to NUM with the total frequency of NUM
no idiomatic or non compositional interpretation is implied
plant and animal celia plant closures
other unsupervised methods have shown great promise
features that appear in the pruned decision tree are assumed to be relevant to the task features that are missing from the tree are assumed to be unnecessary for the task
we first present empirical evidence that the success of case based learning methods for natural language processing tasks depends to a large degree on the feature set used to describe the training instances
this paper addresses the role of the underlying instance representation for one class of symbolic machine learning algorithm as applied to natural language understanding tasks that of case based learning cbl
more importantly the approach offers a mechanism for explicitly combining the frequency information available from corpus based techniques with linguistic bias information employed in traditional linguistic and knowledge based approaches to natural language processing
in case based approaches to natural language understanding the goal of the training phase is to collect a set of cases that describe ambiguity resolution episodes for a particular problem in text analysis
sentences that exhibit a direct object for example will have a direct object feature sentences that have no direct object will contain no direct object feature
we show how each bias can be used to automatically modify the baseline case representation and measure the effects of those modifications on the learning algorithm s ability to predict relative pronoun antecedents
in the second approach to incorporating the recency bias we increment the weight associated with each constituent as a function of its proximity to the relative pronoun see table NUM
table NUM shows the effects of allowing matches on the subject attribute to contribute two five seven and ten times as much as they did in the baseline representation
the ccb is the government review and action board comprised of key personnel from the architecture committee se cm and the appropriate contractors
in short the tipster application must undergo a preliminary design review pdr and a final operating capability foc review
the most important affect of the cm process will be to promote modular substitution software re use and reduced risk of project planning
the role of the cmm is to schedule these meetings provide an agenda record and disseminate minutes and track action items
instead it uses a lexicon stating the possible parts of speech for each word a raw text corpus and an initial bias for the transition and output probabilities
it is not possible to start with a model of NUM NUM states and to successively merge them at least it is not possible on today s machines
the states differ from those of the previously found model but there is no difference in the number of states and corpus perplexity in the optimal point
the ccb will assign and use an engineering review board erb to compile the documentation necessary for the ccb s review
in sentence NUM if we take the position that the string mary a book is not a constituent i.e.
fig NUM contains the derivation and derived structures for NUM and fig NUM for NUM
slater we will discuss an alternative which replaces this operation by the traditional operations of substitution and adjunction
fig NUM gives the possible scenarios tbr the position of nodes that have been linked by a contraction
fig NUM gives some examples each node in the contraction set is circled in the figure
cooked thebcans keatsliked andchapmanhated figurc NUM derivation tbr mary cooked the beans which keats liked and chapman hated
however we note that both hughes and finch use contiguous and noncontiguous bigram information whereas we use contiguous bigram information only the simplest estimate of word context possible to explore just how much information could be extracted from this minimal context
a poor language model component will receive virtually no weight in an interpolated system if we find that weight is distributed mostly with one or two components we can conclude that interpolated language models do not find much use for multiple class information
also since entropy provides a lower bound on the average code length the project of statistical language modeling makes some connections with text compression good compression algorithms correspond to good models of the source that generated the text in the first place
a third criticism of the scheme is its arbitrariness in weighting and selecting canonical classes the criticism is only slight however because the main advantage of any benchmark is that it provides a standard regardless of the pragmatically influenced details of its construction
the inaccuracies introduced by the first of these characteristics may be controlled to a limited extent only by using a hybrid top down and bottom up approach instead of clustering vocabulary items from the top down we could first merge some words into small word classes
a second limitation lies in the evaluation scheme estimating the canonical part of speech based on the rank of the parts of speech of each word in it a better system would make the weight be some function of the probability of the parts of speech
this algorithm which is o v NUM for vocabulary size v works well with the most therefore a word in class m may only move to class n to maximize the mutual information any other move would violate a previous level s classification
the idarex formalism and the corresponding fsc finite state compiler have been developed at rank xerox research centre by l karttunen p tapanainen and g valetto NUM
in the alpnet arabic system roots and pat terns were stored in separate trees in the lexical forest and an algorithm called detouring performed the interdigitation of semitic roots and patterns into stems at runtime
lexicons are stored and manipulated at runtime as a forest of letter trees with each trec typically containing a single class of morphemes with the leaves connected to subsequent morpheme trees via a system of continuation classes
represents any letter and c represents any radical consonant the root drs ty can be interpreted as d r s
mccarthy NUM the voweling of the pattern is also abstracted out leaving pattern templatcs like cvcvc and a vocalic element that cat bc formalized as a a
null NUM because there was no algorithm to intersect the rule transduccrs over NUM of them in the alpnet system thcy are stored separately and must each be consulted separately at each step of the analysis
figure NUM composition of lexicon and rule fsts into a single lexical transducer lexical level drs formi perfect active NUM p f em sg surface level drst figure NUM typical transduction from lexical
with building phantom stems and the unavoidable backtracking caused by the overall deficiency and ambiguity of written arabic words the resulting system was rather slow analyzing about NUM words per second on a small ibm mainframe
whih the resulting system was successfidly sold and is also currently being used as the morphological engine of an arabic project at the university of maryland it suffers from many well known limitations of traditional two level morphology
supplied with full diacritical markings a style of writing found only in religious texts poetry and writings intended for children and other learners partially diacriticized or undiacriticized which is the normal case
we are greatly indebted to the many people who contributed to this special issue by serving as reviewers for the NUM papers that were submitted
computational linguistics volume NUM number NUM the statistical test we use identifies x NUM as the threshold separating insignificant boundaries from significant ones
recently however the field has turned to issues of robustness and the coverage of theories of particular phenomena with respect to specific types of data
similarly there are no publicly available corpora of text based explanations in particular domains that could be a resource for the generation community
section NUM discusses how each article exemplifies the empirical research strategy and how empirical methods have been employed in each research project
in the past research in this area focused on specifying the mechanisms underlying particular discourse phenomena the models proposed were often motivated by a few constructed examples
however even if more corpora become available most discourse studies require data to be tagged and there are currently no publicly available tagged corpora
we need to develop shared coding schemes and make coded walker and moore empirical studies in discourse data publicly available to support comparisons of different models
upon review of a change request the erb first determines the classification as either a class i or class ii type of change
r0asonal le bee rose the colll eltl s of the NUM NUM corpus are news articles tt t l hese triples seem to show some highly abstract briefing of the cont ent
in our diseltssiou we will consider the simple netwohc o fig NUM we will use 1he expression sim ci cj to denote the similarity of eoncel ts h arid e2
given these probabilities he eoltlptttes tile similarit y of concepts i ased on the inl on nation that wou l be necessary to distinguish them tneasured ttsing imbrmalion theoretie calculations
in fact unless the distinctive features of c3 significantly overlap the distinctive feature of c1 it will be the case that si c1 c2 si c2 c3
considering the possible abstraction levels available by the fiatdepth method i.e. depth NUM NUM synsets depth NUM NUM synsets depth NUM NUM synsets this is a great advantage over the flat probability grouping
the chief problems with relation b sed similarity methods lie in their sensitivity to artifacts in the coding of the ontology l or instance msca algorithms are sensitive to the relative deplh and detail of different parts of the concept taxonomy
but it could not use the oc currence of these concepts as conceptual cues for lewfloping coneel ts like lit igadon or l eading in connection with the lawyer eoncel t
varsity htiled nations t ealn subsidiary state state staff so vier school l olitburo police patrol party palml organization oi ci operation lle vvsl rcb vl lilissioll ministry lll ii21 t f lll tg zine lin law firm law hind 3u tice l epartmcnt jury industry hol s
sllt l orl s ore tl taxilhtllll qlll i ce sul l orl score tim normalized e luet y fre luency nmxi unm fi equency
on a recent test set we achieved a NUM detection rate of out of domain parses with no false alarms
combining the two approaches using the parse quality judgement of the ill lcb parser results in improved performance
this working session seeks to shed light on the relationship between structured lexicons and semantic tagging
the compression is measured as the ratio between hell s els s and the number of the observed esl s
furthermore a better coverage NUM is shown in fig NUM d step NUM
the output of the last iteration represents a less noisy environment on which additional learning process can be triggered e.g.
the approach that we support is to reduce syntactic ambiguity through an incremental process
other methods supervised or not do not consider more complex ambiguous structures
unsupervised methods of lexical learning have just as well many inherent limitations
however collecting bigrams rather than trigrams reduces the well known problem of data sparseness
bold characters identify the rood p w shared by colliding tuples
the system quickly removes from the corpus syntactically wrong esl s with low mcp1
for each ei in cs do if NUM athen pprior remove ei from cs i.e.
the incremental disambiguation activity stops when no more evidence can be derived to solve new ambiguous cases
a feedback algorithm for noise reduction the process of incremental noise reduction works as follows NUM
therefore a reliable decision is not allowed by the set of syntactic observations found in the corpus
this type of problem was observed frequently when words are ambiguous between proper nouns and some other parts of speech such as flo es adj propn lozano adj propn van v pp opn a serra v l i lcb opn etc because not all the proper nouns are in the lexicoil
to support the preparation of configuration status reports the cmm maintains a database containing the change status of all architecture elements and changes
since there was n i s t agged
ic sojiic dix c ss a lso ccollill NUM for lllof iri egular ca ses such a s the o selection of aouns and semant i ajly light verbs also known ill the litera l ure
ba sed on world knowledge contril utes llere we oncentral e on how morl hosynta etiea lly ambiguous soas can i e solved on the basis of lexico semantic knowh dge in pa rtieular the focus is on the texieo sema ntic
in the left cohmm the inherent lea lures of each element of the pa ttern are specilied
NUM llesolution of ambiguous soas dcus l is a specia liscd
oscillat e tempi h a i uita s e scendie re ti mperatuita s
on the other hmtd word of der informa tioll can not be relied on con lusively due to the freedom allowed in the ordering o sentem e constituents in italian where virtually all permutations of verb subjeel utd object a re possi hie
results for these experiments indicate that some combinations of the linguistic bias parameters work very well together and others do not
the remaining five columns of table NUM show the effects of incorporating all three linguistic biases into the baseline case representation
results using this NUM nearest neighbor cbl algorithm for relative pronoun disambiguation using the baseline case representation are shown in table NUM
it recognizes coarse character shape classes character shape codes rather than character codes
they are easily generated by scanning hard copy documents which the real world is massively using
for obvious reasons we have left off the labels on the arcs in figure NUM
there are two reasons this data may not be present in any reasonable training set
next it generates a document profile through the following stages stage NUM
we show this using a lexicon of NUM NUM distinct word surface form entries
other hand word shape token generation was a little faster for lower quality images
we feel the word shape token representation is sufficient for locating some suffixes with accuracy
first we identify the positions of the text lines as shown in figure NUM
the last one is further classified by presence or absence of a deep eastward southward concavity
this socalled dornair architecture is incomplete in the sense that it does not specify any interactio strategics
to assure the integrity of all items placed within cm control the cmm establishes and maintains both electronic and hard copy libraries with access limited to cm personnel
in comparison to the comeu expansion results crnlea the main problems appear to be missed relevant documents for all NUM of the topics where the cornell results were superior
however unlike pronominal anaphora which is resolved by matching a pronoun with an antecedent noun phrase the interpretation of an ellipsis fragment or sequence of fragments generally involves mapping it them into a sentential structure by association with an antecedent clause
for example the specification for resume would be transformations middle basic i subj org NUM head resume word NUM obj talk word
early in the processing in name recognition entities that are referred to by the same name or by a name and a plausible acronym or alias are marked as coreferential
each of these variables is a list of lexical or other attributes and when they are plugged into the metarule they define a pattern that is constrained to those attributes
to do this we identify the highest level phase call it phase n in which the constituent boundaries produced by the phase correspond to the way the user has broken up the text
thus the detroit automaker will resolve to general motors or to a company since automaker is of type company and general motors is a company
event event adj ng symconj vg active head NUM ng obj NUM event adj semantics
one need only specify the subject verb object preposition and prepositional object and the classes of metarules that need to be instantiated and the specific rules are automatically generated
cars which are manufactured by gm
compl is matched by various possible noun complements
another method employs global structure presumption to divide a sentence into clauses by utilizing general lexical information
a conventional bottom up parsing method can reduce ambiguity in modification by local information in tim surface structure
sumption we assume that the modality structure an mmnly be detected by lexical intbrmation
the following figure illustrates the modality consistency between particles and when if for example
a subordinate clause with modality modifies a consistent modality predicate type
this tendency basically does not depend on whether or not a comma exists after the words
this conjunction level the highest rank can contain every element even an independent sentence
the phrase toki wps tends to be used to modify phrases with auxiliary verbs of modality
even so it can tune tile pause length more finely than methods without sentence structure presumption
in addition we assume that all conjunctive particles have a modification preference according to their modality
we could define a feature with values in lcb sing plur rcb lcb mass count rcb and provide rules and entries with the appropriate boolean combinations of these
we will assume a set of primitive types like atom or category and allow for complex types also feature person atom lcb NUM NUM NUM rcb
as with many of the techniques described here implementation by way of a compiled out notation can be complex if the features involved interact with other aspects of linguistic description
for example the sentence john sent out will be parsed successfully as indeed will john sent because no conflict with any of the subcategorization possibilities has been encountered
however that category need not actually be present its marker can instead be introduced by the rule that combines a completed vp with a subject to make a sentence
this is not a claim that the efficiency problem is solved even for pure unification grammars but it is at least less of a problem than for these richer formalisms
the original category appears as the value of the kcat feature and the categories that followed this one in the original rule appear as the value of the finish feature
to give an illustration the following grammar generates indefinitely long flat np conjunctions of the john mary bill and fred type
we need two extra arguments this time and then expressions like ga444 c NUM and ca NUM NUM will be coded as
rather than simply constraining the expected probability of a french word y to equal its empirical probability these constraints require that the expected joint probability of the english word immediately following in and the french rendering of in be equal to its empirical probability
the best french translation of in is a function of the surrounding english words if a month s time are the subsequent words pendant might be more likely but if thefiscal year NUM are what follows then dans is more likely
c uf lcb pe pip f f forallfesuf rcb NUM the optimal model in this space of models is psu argmaxh p NUM p c suf
one final note about features and constraints bears repeating although the words feature and constraint are often used interchangeably in discussions of maximum entropy we will be vigilant in distinguishing the two and urge the reader to do likewise
if we had a training sample of infinite size we could determine the true expected value for a candidate feature f e simply by computing the fraction of events in the sample for which f x y NUM
feature f e which maximizes the gain al f that is we select the candidate feature which when adjoined to the set of active features s produces the greatest increase in likelihood of the training sample
furthermore the linear constraints in our applications will not even come close to determining p c v uniquely as they do in c instead the set c q c2 m n c of allowable models will be infinite
with this interpretation the result of the previous section can be rephrased as the model p e c with maximum entropy is the model in the parametric family p ylx that maximizes the likelihood of the training sample
h p x v ylx log p ylx NUM x y NUM a more common notation for the conditional entropy is h y x where y and x are random variables
if we start from a one constraint model whose optimal parameter value is a a0 and consider the increase in l p from adjoining a second constraint with the parameter a the exact answer requires a search over a a
the representation is anchored on these terms and thus their careful selection is critical
the final ranking is derived by calculating the combined relevance scores for all retrieved documents
this paper is based upon work supported in part by the advanced research projects agency under
it should be noted that the parser s output is a predicateargument structure centered around main elements of various phrases
overall we still can see a NUM improvement on all NUM queries vs
strzalkowski scheyen NUM extraction of head modifier pairs corpus based decomposition disambiguation of long noun phrases
the merging process weights contributions from each stream using a combination that was found the most effective in training runs
the system is thus free to pursue unusual theories while remaining aware of the fact that they are unlikely
second atis provides an existing evaluation methodology complete with independent training and test corpora and scoring programs
the current problem then is to compute the prior probability of meaning ms and parse t occurring together
the probability p ms t is then simply the prior probability of producing the augmented tree structure
this phase searches the space of slot filling operations using a simple beam search procedure
the probability of each of these operations is determined by a statistical decision tree model
the discourse model contains NUM such statistical decision trees one for each slot position
the probability p yix is then the product of all NUM decision probabilities
the new state transition probabilities are p state n i staten t stateup ft
unlike probabilities in the parsing model there obviously is not sufficient training data to estimate slot fill probabilities directly
preliminary experiments on feature extraction using NUM corpus in this section our preliminary experiments of the feature extraction process are described
an abstracted triple represents a set of exmnples in the text corpus and each sentence in the corpus usually describes some specific event
polarities of abstracted triple sets for NUM NUM NUM level abstraction are NUM NUM m NUM NUM m and NUM NUM m respectively
surface triples are expanded to corresponding deep triples triples of synset ids by expanding each surface word to its corresponding synsets
the frequency of the surface triples is divided by the number of generated deep triples and it is assigned to each deep triple
ifsm consists of a hierarchical conceptual thesaurus a set of distinctive features assigned to each object and weightings of the features
in our model features have a weight l ased otl the importance o1 the feature to the eolleel t
each abstract triple holds ft equeu y oc llj lx llo lltl ll
if more context is allowed we can define a feature of human as agent of utilizing fire
file result of the alignment may lead to richer bilingual data than can be derived from only wordlevel aligments
words attd functional morphemes and the korean and i mglish words may be assigned with apl ropriate tags
first we introduce the method in word to word alignment att l then extend it to inchme phrase to phrase alignment
the lister lion probabilities are defined on the positional relations such as absolute or relative positions of matching words
phrasal alignment avoids the problem of alignment units and appease the problem of ordering mismatch
l he bilingual dictionari s are not always awfilabh and take
that k matches with e in the corresl onding sentence k and e the count of e giwm k
in figure NUM the right hand side of pair wise aligmnent is the corresponding korean words
alignment as a study of parallel corpus refers to the process of establishing the correspondences between matching elements in parallel corpus
first the korean sentences consisting of words with more than NUM occurrences in the corpus are considered in the experiment
by combining this with a count of the times a rule did not apply the algorithm can compute a probability for each rule
figure NUM breaks down our automatically generated rule probabilities for the wall street journal corpus ities for phonological rules into male and female speakers
each path is ranked by its probability which is computed by multiplying each of the transition probabilities and the phone probabilities for each frame
null in other current work we have also been using this algorithm to model the phonological component of the accent of non native speakers
each pronunciation of the words of and the is represented by a path through the probabilistic automaton for the word
null in order to collect our NUM NUM pronunciations we combined seven different on line pronunciation dictionaries including the five shown in table NUM
the lexicon contains underlying forms which are very shallow thus they are post lexical in the sense that there is no represented relationship between e.g.
our base lexicon is quite large it is used to generate the lexicons for all of our speech recognition work at icsi
chunkandpostag m chunkandpostag n the head word constituent or pos label and start join annotation of the nth tree
the training samples are respectively used to create the models pt g pchunk pbuild and pcmeck all of which have the form
the contextual predicates derived from the templates of table NUM are used to create the features necessary for the maximum entropy models
the actual contextual predicates are generated automatically by scanning the derivations of the trees in the manually parsed corpus with the templates
this allows the maximum entropy parser to easily integrate varying kinds of features such as those for punctuation whereas the bigram parser uses hand crafted punctuation rules
NUM the decision tree predicts the class of a potential boundary site based on the features before after duration cue1 wordt corer infer and global pro
the value is sentence final contour
figure NUM features and their range of values
and that sound was really prominent
features encode an action a as well as some contextual predicate cp that a tree building procedure would find useful for predicting the action a
since the total probability of the trees produced by the stsg is NUM and the pcfg produces these trees with the same probability no probability is left over for any other trees
notice also the minimum and maximum columns of the dop p s lines constructed by finding for each of the paired runs the difference between the dop and the pereira and schabes algorithms
still the monte carlo algorithm has never been tested on sentences longer than those in the atis corpus there is good reason to believe the algorithm will not work as well on longer sentences
however this overgeneration is constrained so that elements that tend to occur only at the beginning middle or end of the right hand side of a production can not occur somewhere else
consider a modified form of the dop model in which when subtrees occurred multiple times in the training corpus their counts were not merged both identical trees are added to the grammar
in this paper we introduce a reduction of the dop model to an exactly equivalent probabilistic context free grammar pcfg that is linear in the number of nodes in the training data
our technique for finding statistical significance is more strenuous than most we assume that since all test sentences were parsed with the same training data all results of a single run are correlated
bod did examine versions of dop that smoothed allowing productions which did not occur in the training set however his reference to coverage is with respect to a version which does no smoothing
thus we can get an upper bound on performance by ex null amining the test corpus and finding which parse trees could not be generated using only productions in the training corpus
figure NUM shows an example of a transducer that implements the flapping rule in NUM
we conclude with some observations about computational complexity and the inherent bias of the context sensitive rewrite rule formalism
aaexe xxe xxng aiaaexena axngxxgex in exxxxn xxe xxxxna aae xxxaa xna xxag aiaaexena xexigax in xalea aaexe axngxxgex xxe aggexea
the size and accuracy of the transducers produced by the alignment algorithm are summarized in table NUM
thus they did n t act as a negative factor but virtually made the size of the test document smaller
we need to find a graphical feature to distinguish some capital letters from others considering the complexity of image analysis
the implementation of a computational grammar of french using the grammar development environment
indeed cliticization in cn is possit te 14d
NUM NUM the structure of the french vp
NUM NUM the structure of the french np
feature specification defaults NUM NUM NUM gde implementation of a flat vp
in gpsg agreement is handled by the control agreement principle cap
the design and implementation of a large coverage computational grammar of french is described
let us now turn to our account of the structure of the np
sul l ose liow l lial seus l lcb
wher the relevant pa ra digms al i ea r
xtra et d from on liue re sources
a s slll l l l verbs
they include no few at most three less than three quarters o cardinal quantifiers are of the form exactly n
they are simply the size of the focus set or equivalently the number of mappings in the focus of the dependency function
section NUM traces the implications of the results obtained for distributions of units derived from words such as syllables and digrams and examines the accuracy of the good turing frequency estimates
for instance in normal written english following the determiner the the appearance of a second instance of the same determiner as in this sentence is extremely unlikely
the number of underdispersed types in text slice k vu k and the corresponding number of underdispersed tokens nu k can now be defined as
given the urn model the probability that the first token sampled represents a type that will not be represented by any other token equals v n NUM n
the bottom panels show the progressive difference error scores for the total vocabulary d k and for the subset of underdispersed words du k
the numbers of underdispersed types and tokens vu k and nu k reveal some variation but unlike in alice in wonderland there is only a nonsignificant trend
the error scores e v n v n for moby dick and max havelaar shown in figure NUM reveal a different developmental profile
more precisely for each text slice k k NUM NUM we calculate the progressive difference error scores d k k NUM NUM
the dotted line in the left hand panel represents the observed vocabulary size of the complete trouw text the solid line shows the result from interpolation and extrapolation from n NUM NUM
NUM the upper right panel shows that interpolation on the basis of sichel s model dashed line is virtually indistinguishable from interpolation using NUM dotted line
ltig a lexicalized tree insertion grammar ltig NUM g nt l a s is a tig where every elementary tree in i u a is lexicalized
the first rule NUM initializes the chart by adding all states of the form s sock NUM NUM where s is the root of an initial tree
if left adjunction is possible on aa the state aa q vx a i j must be independently retained in order to trigger left adjunction when appropriate
let t e i be an elementary initial tree whose root is labeled with x s further suppose that none of the substitution nodes if any on the fringe of t are labeled x
let w be the set of every auxiliary tree that can be created by substituting t for one or more frontier nodes in an auxiliary tree v e a that are labeled x and marked for substitution
for instance in lemma NUM if is the only substitution node labeled x and x s then when t is discarded every x rooted initial tree can be discarded as well
if t is a left auxiliary tree add a new root labeled yi with two children on the left and on the right a node labeled yi and marked for substitution
the substitution rules are triggered by states of the form a c eub fl i j where ub is a node at which substitution can occur
to see this compare for instance what would happen to the statistic if the same discourse boundary agreement data were calculated variously over a base of clause boundaries transcribed word boundaries and transcribed phoneme boundaries
sentence NUM in comparison with NUM shows that a clause with node can subordinate at clause with na g md NUM NUM but the reverse is impossible
however in reference to the pause length data the adverbial verbs ill the former group might fall into lev NUM or lev NUM while those in the latter group with te might full into lev NUM or lev NUM
here first type modality includes conjecture such as darou f2 NUM which corresponds to may can maybe and possibly in english auxiliary verbs adverbs and adjectives
ldg assumes that japanese function words such as conjunctive partides postpositions located at the end of each clause convey modmity or propositional attitude and suggest global structures of japanese long sentences in cooperation with modality in predicates especially within auxiliary verbs
we atnalyzed the speech utterances of a professional new announcer male and fonnd a correla tion between a particle s encapsulating power and tile pause hmgt h inserted ai ter dm clause with a conjunctiw i ar rich
lcb warai smile nagara while tazune ask t past node becavse rcb wa tad NUM wa topic k ot e answe NUM ta past
when this phrase with modality modifies a plain form of a verb with a period at the end of the sentence the readers recognize that the plain form of the verb contains a kind of modality such as the subject s or speaker s intention
japanese relative notln8 such as lnae ii j before are another type of nouns that play roles simil r to those of conjunctions in english when they are moditied by predicative phrases or clauses
ldg presumes tile inter clausal dependency within japanese sentences prior to syntactic and semantic analyses by utilizing the differences of the encapsulating powers each japanese function word has and by utilizing modification preference between function words and predicates that reflects consistency of modality in them
in morphological analysis misspelled input word forms can be corrected and morphologically analyzed concurrently
such recognition has applications to error tolerant morphological processing spelling correction and approximate string matching in information retrieval
in the last spelling correction experiment for turkish almost all incorrect forms with an edit distance of NUM or more had three or more non ascii turkish characters all of which were rendered with the nearest ascii version e.g. ya g n m zde on our birthday was written as yasgunumuzde
after a description of the concepts and algorithms involved we give examples from two applications in the context of morphological analysis error tolerant recognition allows misspelled input word forms to be corrected and morphologically analyzed concurrently
the discrepancy can be noted and documented
figure NUM NUM illustrates the functional structure of the cm organization
follows submissions of rfcs can come from any source
the architecture committee will then have both rfcs to approve disapprove
provide for consideration of changes proposed by any interested party
architecture files contain all written materials and documentation on the tipster ii architecture
it presents a proof that the following problems are np hard computing the most probable parse from a sentence or from a word graph and computing the most probable sentence mps from a wordgraph
for the sentences parses that correspond to consistent assignments there is at least some NUM k m such that wak l wak NUM and wak NUM are all f
the fact that mps under scfg is also np hard implies that the complexity of the mppwg mps and mpp is due to the definitions of the probabilistic model rather than the complexity of tile syntactic model
opt node set find short NUM return opt node set h lcb xlxenu lcb e rcb lining x NUM l n rcb NUM
wsh attribute a b a h b NUM the dtla then measures the goodness of the attribute by gain h a wsh attribute a NUM with these processes in mind we can naturally extend the dtla to handle the structured attributes while integrating t
this algorithm first converts t in figure NUM into a dag NUM as in figure NUM we call this graph a traversal 9raph and each path from s to e in the traversal graph a traverse
this naturally reflects manual syntactic categorizations where a word can belong to several syntactic classes but each occurrence of a word is unambiguous
to compensate for different lengths and to make their probabilities comparable one uses the perplexity pp of an output sequence instead of its probability
therefore the perplexity of some random sample of dialogue data what the test part is supposed to be should decrease during merging
in the previous experiment we abandoned the same output constraint after NUM NUM merges to keep the influence on the final result as small as possible
figure NUM a shows the trivial model for a corpus with words a b c and utterances ab ac abac
it has one path for each of the three utterances ab ac and abac and each path gets the same probability NUM NUM
4actually this is a slight overestimate for a few reasons including the fact that the NUM sentences are drawn without replacement
NUM the first number in each column is the probability that a sentence in the training data will have a production that occurs nowhere else
taken from figure NUM the following pcfg subderivation is isomorphic s np i vp NUM pn pn vp NUM pn pn v np
in this paper we solve the first problem by a novel reduction of the dop model to a small equivalent probabilistic context free grammar
if the stsg formalism were modified slightly so that trees could occur multiple times then our relationship could be made one to one
furthermore sim a an limited the number of substitution sites for his trees effectively using a subset of the dop model
next we present an algorithm for parsing which returns the parse that is expected to have the largest number of correct constituents
in words it is the probability that given the sentence wl w a symbol x generates ws wt
for each of the ten sets both the dop algorithm outlined here and the grammar induction experiment of pereira and schabes were run
une foule de ces 6tudiants a crowd of those students furthermore some constituents have a specifier at level x1 even if there is a specifier at level x2 NUM n2 NUM spec h2 tous les enfants all the children
in the vp for example the past participle an agree with a direct object when it is anteposed NUM or with its subject when it is conjugated with trc for a non pronominal verb otherwise it remains invariant
the following rules for a subset of french quantitative constructions highlight the prevalence of this n2 NUM NUM a n2 pform nil adv2 qte h2 de beaucoup de filles many girls b
there is no specifier in the verb phrase vp thus the v2 immediately dominates the v complex specifiers in noun phrases np and adjectival and adverbial phrases adjp and advp are given special treatment there are x2 level specifiers of x2 constituents as tbr example in i n2 NUM r2 poss h2 the man s black hat
6for space reasons the xc indicates xcomp the d a dep
figure NUM NUM illustrates the organizational structure of the se cm organization
the planner is unable to generate a path between these points since it is greater than four hops
rather we used the modality switch as a way of manipulating the error rate and the degree of spontaneity
linguistically commands are realized as imperatives and hence tone NUM
there was no particular task that was troublesome and no particular subject that had difficulty
a typical move in this genre is the request move
in order to stress the system in our robustness evaluation we used the atis language model provided from cmu
these rules match against the speech act the problem solving state and the current state of the domain
when we think about the problem from the perspective of intonation the picture becomes clearer
since i i luslrat es the simplest possible l at tern i.e. a word co occurrence pattern noun and verb slots are filled in by actua lly
for a baseline we built a class based back off language model for sphinx ii using only transcriptions of atis spoken utterances
in pra ctice for a n analogy i el wee two linguistic objects to i recognized a s rehwant thus triggering core extra ction
the ccb s decision is recorded as a change directive
the reduction constructs one elementary tree that has root s with children ck NUM k rn in the same order as these appear in the formula of ins see the bottom right corner of figure NUM
however the last requirement on p0 implies that NUM 20v z i l NUM NUM which is a stronger requirement than the other n requirements
this is the case because such sentences have fewer than n derivations of the first type and the derivations of the second type can never compensate for that the requirements on NUM take care of this
the reduction constructs for each of tile two literals of each variable ni two elementary trees where the literal is assigned in one case t and in the other f figure NUM shows these elementary trees for variable ul in the bottom left corner
each of the elementary trees with root s or ck is also represented here but each t and f on the frontier has a child vkj wherever the t or f appears as the child of the jth child a literal of ck
the decision problem related to maximizing a quantity m which is a function of a variable v can be stated as follows is there a value for v that makes the quantity m greater than or equal to a predetermined value m
there exists a NUM that fulfills all these requirements polynomlality of the reduction this reduction is deterministic polynomial time in n because it constructs not more than 2n NUM 3m 4n elementary trees of maximum number of nodes NUM 7m NUM
this is taken care of by demanding that the sum of the probabilities of elementary trees that have the same root non terminal is NUM and by the definition of the derivation s probability the parse s probability and the sentence s probability
by describing certain distinctions between cure and heal as involving selectionai restrictions
we do not in this context depend on any cialm q
the ccb s major responsibilities are review all proposed change requests
lcb m b rcb medicine the ointment cured bodypart my foot
we belicve thcsc problems arc solvable in the ncar term and wc have partial solutions in place already
morphological transformations relabel the initial default tagging of those words that failed to be found in the lexicon
once again though these initial precision errors arc readily addressed by patching rules
further this simplicity enables the automatic acquisition of phraseparsing rules through an error reduction strategy
if these test succeed then the rule s bounds and label actions are executed
phrase rule interpreter the phrase rule interpreter implements the rule language in a straightforward way
this paper explores an approach to syntactic analysis that is unconventional in several respects
error estimation methods a rule that fixes a problem in some cases might well introduce errors in some other cases
the initial phrase labeling for the proper name cases is implemented by accumulating runs of nnp and nnestagged lexemes
the last two steps will be repeated as needed to refine the frame description
treatment h d rcb healer the doctor cured disorder my disease
the kappa coefficient k of agreement measures the ratio of observed agreements to possible agreements among a set of raters on category judgements correcting for chance agreement
the first point comes from using approx
for instance consider our generation strategy
at the top are the i o facilities
our approach here is clearly bottom up
the other half used keyboard first and then alternated
the results suggest further that if attributes used are indeed a good predictor of s mmary extracts their strength as a predictor will be enhanced by the reliability or quality of human judgements
valency pattern valency patterns are stnlcture patterns that formulate possible valency structures for predicates
however the following problems arise when processing type NUM and type NUM cases in the normal way
thc analysis thcn tries to bind modifiers that have ml adverbial particle to the non bolmd valency elements
therefore they include ahnost of all linguislic phenomena flu l the systems have to process
therefore type NUM is separated from type NUM in this classification from viewpoint of enghmering
as a result the method proposed here improves the translation accuracy of alt j e
double sul jeet construction usually a simple sentence has only one subjeclivc case
moreover japm ese time expressions are often translated into english adverbial phi ases
marks subjective case to wflency elements in tile wflcncy pattern lbr the prcdicate
the jap mese double subject conslntclion was nol handled well by the original alt j e
NUM a set of maximum entropy models that compute probabilities of the above actions and effectively score parse trees
the presumingvariable option corresponds to a textual anaphora
in the centering model for each utterance u a list of forward looking centers cf un made up of all the entities realized in the utterance is associated
from this study a general typology of entities referred to in the domain emerged together with an indication of how such entities are typically expressed in the different languages see figure NUM
for example if n NUM then class names of beautiful and the are x iful and the respectively
table NUM shows examples of regular expression patterns in our system NUM it is decoded with the presupposed coding system and registered in ca string list
NUM selecting the language with the highest likelihood null this step compares likelihood scores then returns the language with the highest likelihood score
the language identification routine described in section NUM NUM takes a character string and returns the most likely language and the score of likelihood
this system assigns a unique identifier to every registered character set in the world and specifies escape sequences for switching one character set to another
although creating the annotated corpus requires much linguistic expertise creating the feature set for the parser itself requires very little linguistic effort
when selecting nodes with children the tree will expand resulting in the display of multiple word terms of the root key term
but this would prevent us from being able to express certain syntactic and semantic generalizations such as the fact that while we speak of curing diseases we do not speak of curing wounds and we speak of wounds but not diseases as heming s ethere might be alternative ways of considering such data
the machinese induced human habits will spread to human human situations and if we do not yet have an answer let us hope that the question is premature
if virtually all text is crunched through the same corporate national or global network mill norm adherence and standardisation can be warranted on a many levels
deviants can be automatically identified commented upon amended returned censored and or punished say in excusable cases by some intentional delay
that has an impoverishing effect not necessarily worse though than the self imposed discipline of writers mindful of human translators or non native readers who are less familiar with the language used
or conversely are there particular features of artificial systems which we should refrain from introducing because of such side effects even though they may be cost effective for their immediate purposes
in particular it has been tacitly assumed the languages in use between humans what engineers see as some unique next to metaphysical entity which they call natural language in singular
and or decay of finer shades of meaning emotional overtones and the social subtleties between information and command between tell you and i tell you to
effects of information technology on the development of natural languages on some occasions during coling conferences we should raise our gaze from the hows of our trade to regard the whys and whynots
thus in some branches of legal informatics the efforts made by today s writers to attract tomorrow s readers by paving the way for yesterday s retrieval technology has a stultifying effect
we are only at the beginning of a beginning
feature selection and weight adjustment can be accomplished using the iis algorithm
in any text there are words the use of which is mainly or even exclusively restricted to a given subsection of that text
nonrandom word usage also affects the accuracy of the good turing frequency estimates which for the lowest frequencies reveal a strong underestimation bias
in the newspaper trouw where heid words do not play a central role in an overall discourse no significant divergence emerges
if wi e s itsfi tokens m will all appear in the same part of the text
this probability approximates the probability that after n tokens have been sampled the next token sampled will be a new type
where we assume that the frequencies f i m are independently and identically binomially m pi distributed
these one poillt crossovers use each conllllon part of tile english and japanese sentences
NUM NUM modality in conjunctive particles and modification preferences
it is easy to see why NUM n is an upper bound for coherent text by focusing on its interpretation
steps 2a and 2b are applied iteratively for each i NUM i m until every initial tree satisfies f
in trec NUM disks NUM and NUM were used for the adhoc task and new data also shown in table NUM was used for the routing task
the number of participating systems has grown from NUM in trec NUM to NUM in trec NUM see table NUM and to NUM in trec NUM see table NUM
of the NUM runs shown in figure NUM two runs inq201 and cityal used a similar number and source of expansion terms as for the longer queries
three of the top NUM runs were results of major revisions in the basic ranking algorithms revisions that were the outcome of extensive analysis work on previous trec results
note that term expansion is mostly a recall device adding new terms to a topic increases the chances of matching the wide variation of terms usually found in relevant documents
it may be that the narrative section of the topic is necessary to make the intent of the user clear to both the manual query builder and the automatic systems
the average number of documents actually judged per topic those that were unique was NUM NUM for trec NUM and NUM NUM for trec NUM
their basic term weighting formula and query processing was simplified from that used in trec NUM and they also used passage retrieval and whole document information in their ranking
there have been four text retrieval conferences trecs trec NUM in november NUM trec NUM in august NUM trec NUM in november NUM and trec NUM in november NUM
the trec conferences have proven to be very successful allowing broad participation in the overall darpa tipster effort and causing widespread use of a very large test collection
any cfg can be trivially converted into a tig that derives the same trees by converting each rule r into a single level initial tree
as we have seen dutch en is used as a verb marker it marks the infinitive present plural and for strong verbs also the past plural it is also used as a marker of noun plurals
what is clear from the plot in the top panel of figure NUM is that the downward trend in the regression curve to the right of the plot is due to the lexical properties of a relatively small number of high frequency verbs
any lexical biases that are inherent in these morphological processes for example the fact that a low frequency dutch word ending in en is more likely to be a noun than a verb are well estimated by the hapaxes
with the increasingly large corpora that are becoming available at present enhanced sampling methods should pose no serious problem
for large m and small p binomial probabilities can be approximated by poisson probabilities leading to the simplified expressions
in this table no inf and no pl represent the observed number of tokens of infinitives and plurals in the held out portion of the data representing types that had not been seen in the training data
the bottom right panel of figure NUM plots the corresponding errors for the unadjusted sample probability
was obtained for the parameter values c NUM NUM NUM NUM NUM
first to what extent does the nonrandomness of word occurrences affect distributions of units selected or derived from words
over sampling time we observe a slight increase in both the numbers of tokens and the numbers of types
for estimates of the probability of unseen types the sample and good turing estimates provide approximate upper and lower bounds
detailed analyses of how these key words appear over sampling time in the novels reveal marked differences in their distributions
according to the stack model since utterances a NUM a NUM are part of an embedded segment so a NUM is hierarchically recent when a NUM is interpreted
utterance a 8a realizes the proposition my daughter s husband is working as well but this realization depends on both an anaphoric referent and an anaphoric property
since the number of leaves d in the present thesaurus is k the first term in formula NUM be d showing that t has o d time comes complexity in this case
here we have two functions named make lyee NUM and wsh NUM the function make lh ee0 executes the recursive search and division and the wsh calculates the weighted sum of entropy
the essence of t lies in the conversion of the partial thesaurus from a tree t into a directed acyclic graph dag t this makes the problem into the shortest path problem in a graph to which we can apply several efficient algorithms
if an attribute has m different values whicil divide a into m subsets as a lcb bi j m rcb the dtla evahmtes the purity after division by the weighed sum of entropy wsh attribute a
and gv n p and e p are generally mutually conflicting high generality node p with low gv n p will induce many errors resulting in high e p and vice versa
we apply this algorithm to a bilingual corpus and show that it successfiflly learned a generalized decision tree for classifying the verb take and that the tree was smaller with more prediction power on the open data than the tree learned by the conventional dtla
in utterance a NUM h interrupts c s narrative to ask for his name but in a NUM c continues as though a NUM had just been said
the intentions of a conversant and the recognition of the other s intentions determine what is retrieved from main memory and what is preferentially retained in the cache
let s suppose that we select human in figure NUM for a ucc node set p cc then we can not include mammal in the p c there will be leaf overlap between the two nodes which violates the unique cover
to conclude the analysis presented here suggests many hypotheses that could be empirically tested which the currently available evidence does not enable us to resolve
when conversants return to a prior intention information relevant to that intention must be retrieved from main memory if it has not been retained in the cache
here i propose an alternate model to the stack model which i will call the cache model and discuss the evidence for this model
in the stack model focus spaces for segments that have been closed are popped from the stack and entities in those focus spaces are not accessible
as in trec NUM we used a randomized index splitting mechanism which creates not one but several balanced sub indexes
the user s natural language request is also parsed and all indexing terms occurring in it are identified
in large databases such as tipster the use of phrasal terms is not just desirable it becomes necessary
after the final query is constructed the database search follows and a ranked list of documents is returned
since names are traditionally capitalized in english text spotting them is relatively easy most of the time
this situation is even worse when single word terms are intermixed with phrasal terms and the term independence becomes harder to justify
the bulk of the text data used in trec NUM has been previously processed for trec NUM about NUM NUM gbytes
routing experiments involved some additional new text about NUM mbytes which we processed through our nlp module
in general we can note substantial improvement in performance when phrasal terms are used especially in ad hoc runs
main thrust of this project is to use natural language processing techniques to enhance the effectiveness of full text document retrieval
they are readily patternable subtypes for example many taggable organization names begin with a location name and end with a unit designator as in the name minnesota mining and manufacturing corporation
log n is a normalization factor
the age of subjects varied from NUM to NUM
attributes provide ways in which to code a sentence
the selection of attributes is essentially heuristic and empirical
proposed constituent is a fiat chunk since such constituents must be formed in the second pass
for example consider the following sentences NUM a
compute the head and the modifiers using the algorithm described in section NUM NUM
in this case a pronoun or a definite expression can be used
test focusstate to identify the ntost appropriate determiner for the noun phrase
once the ties have been determined the distinguishing semantic features are identified
in kl one knowledge representation languages they are represented as instances in the a box
initial tree transducer for bat batter and band with flapping applied
subsequential finite state transducers are a subtype of finite state transducers with the following properties
if the end of word symbol follows the corresponding unvoiced stop will be emitted
training patterns the three particular stressed vowels caused the decision tree to
the difference is that the arcs in figure NUM have more general labels
furthermore results with lexicographic orderings vary with the ordering of segments used
the transducer for word final stop devoicing using variables is shown in figure NUM
thus our transducers only needed to be modified to deal with rightward context
the context principle suggests that phonological rules refer to variables in their context
let ni denote the number of occurrences of both literals of variable ui in the formula of ins
the probability of a sentence is the sum of the probabilities of all derivations that generate that sentence
the probability of a parse is defined as the sum of the probabilities of the derivations that generate it
this type of derivation corresponds to assigning to at least one literal in each conjunct the value true
the probability associated with each word is the per word sentence probability in the case of trigrams or the conditional probability p wi in the case of bayes
we adopt the bayesian hybrid method which we will call bayes having experimented with each of the methods and found bayes to be among the best performing for the task at hand
the results are broken down by whether or not all words in the confusion set would have the same tagging when substituted into the target sentence
this is another case in which context words actually hurt bayes as running it without context words again improved its performance to the baseline level
pruning criteria may be applied at this point to eliminate features that are based on insufficient data or that are ineffective at discriminating among the words in the confusion set
the results of bayes are shown in table NUM NUM generally speaking bayes does worse than trigrams when the words in the confusion set have different parts of speech
two points about the application of bayes in the hybrid method first bayes is now being asked to distinguish among words only when they have the same part of speech
it does not necessarily score the same however because as mentioned above it is trained on a subset of the examples that stand alone bayes is trained on
the confusion set lcb raise rise rcb demonstrates albeit modestly the ability of the hybrid to outscore both of its components by putting together the performance of the better component for both conditions
because the ostia algorithm tends to settle in local minima when merging states the problem becomes one of searching the space of permissible orderings of state mergers
finally the actions underlying the sentences have to be computed their treatment introduces the general problem of the semantics of actions
the rose data administrator can perform simple queries and execute quick reports against the collected data
adept was conceived as a vehicle for capabilities to alleviate problems currently being faced by oir
as depicted in figure NUM i there are two categories of collections production and adaptation
for each document selected the document viewer gui is invoked
problem documents are sent to the problem queue to await analysis
dp does not stop processing the document once encountering an error
the document and its relevant information is stored in local storage via the dm function calls
the set of sgml tags with their corresponding value constitute the sgml template for that document
the dp pqm and om processes each have a unique collection associated with it
when completed the document is moved to another collection for the next process to continue
that is NUM of the training data can pass the threshold
the precision and the recall for these three types are NUM NUM
build decides whether a tree will start a new constituent or join the incomplete constituent immediately to its left
long complex phrases are similarly decomposed into collections of pairs using corpus statistics to resolve structural ambiguities
in designing nominator we have tried to achieve a balance between high accuracy and speed by adopting a model which uses minimal context and world knowledge
as a rule their entity types should be identical as well to prevent a merge of boston place and boston org
the payoff of this choice is a very high precision rate NUM for the assignment of semantic type to those names that were disambiguated
while the heuristics for splitting names are linguistically motivated and rule governed the heuristics for handling sentence initial names are based on patterns of word occurrence in the document
new york s moma is further split at s because of a heuristic that checks for place names on the left of a possessive pronoun or a comma
in the rest of the paper we describe the resources and heuristics we have designed and implemented in nominator and the extent to which they resolve these ambiguities
the same combination but with a last name that is not a listed organization word results in a low positive score as for justice johnson or frank sinatra
if the sentence initial candidate name also occurs as a non sentence initial name or as a substring of it the candidate name is assumed to be valid and is retained
special treatment is required for words in sentence initial position which may be capitalized because they are part of a proper name or simply because they are sentence initial
NUM NUM surface svo triples are extracted from the NUM NUM corpus
one measm es the l lausitfility o al stract ed
in our presentation we make these hierarchies into lattices they always have a top and bottom element and every pair of types has a glb and lub
we are also considering using logical forms instead of word and word classes in collocation patterns
another method called raw training is to record only full patterns for ambiguous and unambiguous attachments in the corpus
thus it appears that at least one of these methods of generalization is needed for this highdimensional space
for instance we would expect shorter patterns such as need in to carry less weight than longer ones
while searching for attachment statistics for sentence NUM kankei will check its hash tables for the key need orange in elmira
figure NUM shows how the format of these patterns allows for combinations including a verb np head rightmost np before the postmodifier and either the preposition and head noun in the pp or one or more adverbs
in ambiguous verb phrases of form v np pp or v np adverb s the two corpora have very different pp and adverb attachment patterns in the first the correct attachment is to the vp NUM NUM of the time while in the second the correct attachment is to the np NUM NUM of the time
kankei uses various n gram patterns of the phrase heads around these ambiguities and assigns parse trees with these ambiguities a score based on a linear combination of the frequencies with which these patterns appear with np and vp attachments in the trains corpora
normally kankei will do partial matching i.e. if it can not find a pattern such as need orange in elmira it will look for smaller partial patterns which here would be need in orange in orange in elmira need in elmira and need orangein
table NUM results for lexical tagging using case based learning with and without feature set selection
it is able to improve on our semantic feature tagging results by a few percentage points
after training the system can use the case base to resolve ambiguities in novel sentences
figure NUM shows a portion of three relative pronoun disambiguation cases using the baseline case representation
null NUM it determines the relative importance of relevant features
the value for each feature provides the phrase s semantic class
at first it may seem surprising that this bias does not result in a better representation
this section presents a new technique for feature set selection for case based learning of natural language
the right to left ordering yields a different feature set and hence a different case representation
our baseline case representation does not necessarily make use of this restricted memory bias however
equation NUM screens out the impossible candidates
if the confidence is somewhat lower there are two ways of realizing a yes no question tone 2a interrogative yes no type informationseeking unmarked neutral assessment or tone NUM interrogative yes no request
on this basis we have determined a number of factors that contribute to the selection of appropriate tones such as speech func NUM tion speaker s attitudes and hearer s expectations and types of dialogue moves in context
if the system is confident that it has understood what the user said it would ask only to confirm what it believes to know hence it would choose a declarative with tone NUM answering positive answering toquestion strong
NUM even goes one step further in arguing that it is not sufficient to describe tones by a combination of fall and rise instead much finer distinctions have to be made see NUM
these include NUM accounting for the textual meaning of intonation encoded in information structure and thematic development progression realized in tonicity see section NUM we handle only situations in which there is a one to one corresponds between tone group and clause
in this article we develop a model of how the dialogue module can control the traversal of those regions of the tal generation of utterances from pre linguistic conceptual structures to the formation of syntactic and phonological structures with an interface to a speech synthesis module for german
these tones are all tonel rise tone2 progredient tone3 all rise tone4 rise all tones where the first four can be further differentiated into secondary a and b tones
we suggest that the linguistic realization of this question depends on how confident the system is about what the user informed hence in order to choose appropriate mood and key features we argue that we need access to an additional resource a confidence measure NUM
this estimation is allowed since the training corpora are reasonably large and the bias of one occurrence per transition has no significant effect on the validity of the actually nonzero matrix elements
taking advantage of the relative sparseness of matrix b we first determine if bj t is nonzero and only then does the algorithm proceed to the rest of the processing
leading to state si at time t that are more probable than qm t which is a path among the first e most probable paths a contradictory statement
by multiplying every distance with this factor and truncating it to its integral part it is guaranteed that there will be no overflow in the execution of the viterbi algorithm
matrix a is established according to formula NUM through training in appropriate corpora using as n x max n x NUM
the algorithm that was developed here uses the features of the viterbi algorithm in a slightly different way tailored to the needs of the ptgc problem as described in section NUM NUM
this was also expected since in the training phase the model is trained using the correct graphemic form of the words which is later reproduced in the conversion experiments
derivatit n tree derived tree figure NUM example of an ltag and an ltag derivation
this assumption needs to be revised when gapping is considered in this framework ss5
the notation marks tile foot node of an auxiliary tree
this section discusses parsing issues that arise in the modified tag formalism that we have presented
the derivation encodes exactly how particular elementary trees are put together
note that this representation is not required by the ltag formalism
figure NUM handling the gapping construction us ing contractions
various positions for such traversals is shown in fig NUM
permitting contractions on mul tiple substitution and adjunction sites along with
corpl corp2 equal equal equal equal much higher high low high dis comment equal same language variety ies high low higher high high low high high a bit higher low low a bit higher different language varieties corp2 is homogeneous and falls within the range of general corpl corp2 is homogeneous and falls outside the range of general corpl impossible overlapping share some varieties similar varieties interpreted with respect to homogeneity
the tuit editor ted is a gui that can be used to view and edit multilingual texts
it provides an interface that enables users to read save documents to tipster document collections null create delete documents rename collection documents edit document attributes launch a new tu1t editor with a document create delete collections add unix files to collection based on a path and wildcard edit collection attributes copy and move documents between and among col
tuit is configurable at run time on a number of dimensions through a standard configuration format using tcl style syntax
a document joke NUM i i ii iii figure NUM a typical document browser window showing annotation highlights proper names browser window such as that shown in figure NUM would be created with this single function call
the formal approach adopted proved to be particularly suitable to cope with multilinguality issues since the tests performed at the various choice points can be easily customized according to the output language
in section NUM we first present the results of observations made on the corpus texts with the aim of identifying the typical referring expressions occurring in our domain
further investigations on the corpus texts have been conducted to identify language dependent referring phenomena and general heuristics for the choice of the most appropriate linguistic realization
they have to conform to the expectations of the reader according to his evolving flow of attention and they have to contribute to the cohesion of the text
at every stage of the referring expressions generation process issues raised by multilinguality are considered and dealt with by means of rules customized with respect to the language
these domains differ from the daughter list in that the ele ments in a domain signs correspond in their serialization to the surface order of the words in the string
instead i will explain some of the problems of the hinrichs and nakazawa approach hinrichs and nakazawa changed the value of slasii into a set of signs rather than local objects
lp eonstraints apply to elements of the order domain
it is not possible to front parts of tile verbal comi lex that would be located in the middle of the verbal eom plex in a verb tinal sentence sb
the various two level rules which had to be hand compiled into finite state transducers were run in parallel by code that simulated their intersection
a letter path through the lexieal trees from a legal starting state to a final leaf defines an abstract or lexical string
like all finite state transducers it also generates as easily as it analyzes literally by running the transducer back null wards
table NUM shows some examples for each type
the basic operation for the integration of temporary graphs is the maximal join operation where a union of two graphs is formed around their maximal common subgraph using the most specific concepts of each
ltere we give a meaning to their clustering we tint and show the connections between concepts and by doing so we build more than a cluster of words
in fact an lkb lmilt fl om a children s dictionary could be seen as a starting point from which we could extend our acquisition of knowledge using text corpora or other dictionaries
figure NUM shows the result of the maximal join
figure NUM example of relaxed maximal common
we extend the subsumption notion to the graphs
table NUM examples of relations found in sentences
cereal is a kind of food
continue until no changes are made
each vertex of the chart is labeled with the score of the best path through the chart that visits that vertex
in particular how can a parser that uses a general grammar achieve a level of efficiency that is practically acceptable
this is especially true in the second stage of pruning when many constituents of different lengths have been created
the minimum score derived from any of the criteria applied is deemed initially to be the score of the constituent
note that only the non phrasal rules are used as input to the chunks from which the specialized grammar rules are constructed
table NUM shows the relative scores of the four parsing variants measured according to the preferable translation criterion
section NUM describes the grammar specialization method focusing on how the current work extends and improves on previous results
our word recognition module is a modified viterbi decoder where two changes in the algorithm design were made we use only the forward search pass and whenever a final ttmm state is reached for an active word model a corresponding word hypothesis is sent to the parser
this is because cunse is unreliable especially when c a is low
the NUM collocations were selected from among the collocations of mid range frequency collocations appearing more than NUM times in the corpus
this can be attributed to the fact that the number of l s in the original variables is far smaller
in the following sections we first present a review of related work in statistical natural language processing dealing with bilingual data
for example in american english one says set the table while in british english the phrase lay the table is used
the dice coefficient clearly identifies aujourd hui as the group of words most similar to today which is what we want
the two subsequent columns give the similarity values computed according to the dice coefficient and specific mutual information in bits
although based on only a few cases this experiment confirms that the dice coefficient outperforms si in the context of champoluon
the existence and use of multi lingual layers shall not have significant adverse effect on single language processing
retrieval of annotations should also be possible by type by annotation group and for the entire document
the user shall be able to relate this query to both the criteria and the document it retrieves
the architecture shall allow prioritization to affect the manner in which documents are retrieved and presented for review
reference b sgml standards defines the concept of a dtd document type definition
the common lexicon format should cover a frequently used set of data fields and be applicable across languages
pattern is an expression of a specific form that is used for matching text during the extraction process
this boundary is the interface between the tipster architecture and the user interface implementation for a specific application
for example fill rules may be in a language different than the language of the source document
the selection specification may identify the length of the string that is considered for identical selection
given lhe situation of fig NUM both ms l an t tlesnik s m ism most in formative sul stmtor
if a reasonably large text is available for training then ns x n x
consequently the available training material was inadequate for the creation of a correct model and led to poor performance
a first and second order hmm have been created and the viterbi and n best algorithm have been used for the conversion
in the first case one suggestion no language model is needed to disambiguate potential homophones at sentence level
in this sense the output of the ptgc system never misleads the final human user about what the input was
finally in figure NUM the degradation of performance as a function of the corrupted input words is shown
in tables NUM through NUM the model parameters for all the models created for the experiments mentioned above are presented
the viterbi algorithm produces the overall best state sequence by maximizing the overall probability p o i q
initially the first order hmm and the common viterbi algorithm were used to provide a simple transcription for each input word
another very important issue when searching for words in a dictionary is the number of candidates resulting from each phonemic input
the experiments described below have been done in connection with the ls gram project NUM which is concerned with the development of large scale grammars
the pi i arc labels stand for all lcxemes in the lexicon that may be labeled with the part of speech gj the induction step in the construction procccds from l bl the machine built to reproduce z up l hrough rule l bl in the sequence and adds additional states and arcs so as to reproduce z up through ruh
the way dpl is interpretating the distinct quantitiers and connectives is the following one existential quantification and conjunction are e ctcrnally dynanfic
not everyone agrees on those assumptions as this can be seen in the discourse representation theory or in the work by irene helm NUM
none golkswagen none of none america inc none the example rule designates the partial phrase america inc as an out a precision error because of its partiality and fails to produce an otto label spanning the entire string a recall error
it is also not neccssatt for these candidates to be unfragmented as fragments can be reassembled later just as with volkswagen of america inc further applications that require multiple types of phrase labels need not choose such a label during the initial phrase finding pass
labebaction org change the phrase s label but not its boundaries now consider the following partially labeled string none donald f descenza none analyst with text initial labelled text c finaltext nprocessed lexlconlabelling lookup j transformatlons morphological rules j the sgml markup delimits phrases whose boundaries were identified by the initial phrase finding pass
system the machinc learned rules achieved an overall named cntitics f score of NUM NUM compared to the NUM NUM achieved by the hand crafted rttlcs it should be noted however that the system loaded with these machine crafted rules still outpcrfimned about a third of systems participating in the muc NUM evaluation
at least they do n t allow a anaphoric reading no man walks in the park
they can bind variables within and outside their scope a man walks in the park and hei whistles
to implement this technique a three stage approach is adopted to the gradual refinement module
the strongest strength chain will be selected as the most likely sequence of the right words in the right tags
table i shows three kinds of spelling errors caused by carelessness and lack of knowledge
it consists of preference based pruning syntactic based pruning and semantic based pruning
the work reported in this paper was supported by the national research council of thailand
this causes the problem of word boundary ambiguity see fig NUM
the implicit spelling errors are spelling errors which make the other right meaningful words
in general associating a weight of two with the subject constituent improves the accuracy of the learning algorithm as compared to the corresponding representation that omits the subject accessibility bias
as was the case with the baseline representation incorporation of the subject accessibility bias steadily decreases performance of the learning algorithm as the weight on the subject constituent is increased
ioli ncndc f 17lll o l i l a k ul r sidence where NUM NUM NUM i is sill plied hi hilml wiljl ii s l a x liyili s l ui is no al
we solve the second problem by a novel deterministic parsing strategy that maximizes the expected number of correct constituents rather than the probability of a correct parse tree
NUM NUM NUM NUM incremental improvements in coverage but at the cost of increasing the ambiguity of the grammar
therefore a text sentence containing eight commas and no other punctuation will have NUM analyses
however their experiments are not strictly comparable because they both utilise more homogeneous and probably simpler corpora
to date no robust parser has been shown to be practical and useful for some nlp task
the number of analyses varies from one NUM to the thousands NUM NUM
we have developed a declarative grammar in the anlt metagrammatical formalism based on nunberg s procedural description
however further improvements in coverage will require some automated approach to rule induction driven by parse failure
the major reason that the recall is not sufficiently high is that we decided to use a rather severe condition on selecting a translation pairs in step NUM in the algorithm
human users as well would profit from a careful description of the variability of mwls so it should be worthwhile to also include the canonical forms in dictionaries for human users
nunberg et al NUM it is difficult to establish such a relationship on a large scale and a lot of remaining idiosyncratic characteristics of individual mwls need to be represented
square brackets and the bar are used to describe lexical variants and alternations of more complex sequences such as word order variation in german
the variations allowed by general rules is valid outside that subset the expression loses its special idiomatic meaning either reverting to its literal meaning or losing any significance altogether
we propose to use local grammars for this written as a special type of regular expressions res in the finite state formalism idaitex which makes use of a two level morphological lexicon
the identification of mwls is essential for any natural language processing based on lexical information ranging from intelligent dictionary look up over concordancing or indexing to machine translation
in the latter case the translation for the entire mwl is returned otherwise a selection of translations for the most appropiate part of speech
2f we w f sire we f w f wj kitamura matsumoto used the same formula for calculating word similarity from japanese english parallel corpora
these forms include not only inflections but also particle forms such as prepositions and conjunctions as well as grammatical relations lexical categories and syntactic structures
the context of this study for the past three decades the mainstream of linguistics has focused its research agenda on the formal aspects of language primarily syntax
there is also a resemblance in that the form is shape neutral as seen in in the well and in the trench
alternative sets of closed class forms within a stretch of discourse can impose different partitionings onto what would otherwise be the same scene
yet the substantive content is wholly comparable still a western cowboy landscape with theft of livestock by use of rope
j it is not only the constraining principles just described that bring organization and order to the universal inventory of structuring concepts
the different functions performed by these two classes of elements can be shown in relief by changing one class while keeping the other constant
the objects in each of these pairs are treated as geometrically alike in language whereas they are wholly different objects in mathematical topology
on the other hand such notions can be referred to by open class forms a fact demonstrated by the words just used
in our theoretical framework this function of closed class forms to structure discourse is included under the notion of scene partitioning
i lcb ecause each of the two translation methods appears to perform better on different types of utterances they may hopefldly be combined in a way that takes adwmtage of the strengths of each of them
a word that is already known to the system however can cause a concept pattern not to match if it occurs in a position unspecified in the grammar
this is done using a beam search heuristic that limits tile combinations of skipped words considered by the parser and ensures that it operates within feasible time and space bonnds
he translation of an utterance is manually evaluated by assigning it a grade or a set of grades based on the number of sentences in the utteralice
the grammars we develop for the ian us system are designed to produce eature structures that correspond to a frame based language independent representation of the meaning of the input utterance
the parser can identify sentence boundaries within each hypothesis with the help of a statistical method that determines the probability of a boundary at each point in the utterance
whenever the parse result t om gli lcb is judged as bad the translation is generated from the corresponding output of the phoenix parser
each sentence is cla ssified first as either relevant to the scheduling domain in domain or not rel null evant to the scheduling domain out of domain
whereas glr is general enough to support both semantic and syntactic grammars or some combination of both types the phoenix approach was specifically designed for semantic grammars
there are three little boys nonboundary NUM up on the road a little bit nonboundary and they see this little accident
in b the constraint c1 narrows the set of allowable models to those that lie on the line defined by the linear constraint
in b we show the reduced problem a line search over a optimization problem requiring more sophisticated methods such as conjugate gradient
that is when determining the gain of f over the model ps we pretend that the best model containing features u f has the form
in the next few pages we discuss several applications of maximum entropy modeling within candide a fully automatic french to english machine translation system under development at ibm
imposing one linear constraint q restricts us to those p e p that lie on the region defined by c1 as shown in b
the choice of NUM must capture as much information about the random process as possible yet only include features whose expected values can be reliably estimated
separate the training data x y into a training portion pr and a withheld portion h
we used the feature selection algorithm of section NUM to construct a maximum entropy model from candidate features derived from templates NUM NUM and NUM
a maximum entropy model that uses only template NUM features predicts each french translation y with the probability y determined by the empirical data
two of the schemes used boolean queries one with ranking and one without and the third used the same queries without operatots
the brkly6 run uses a logistic regression model to combine information from NUM measures of document relevancy based on term matches and term distribution
table NUM shows a breakdown of improvements from expansion and passage retrieval that combines information from the non official runs given in the individual papers
the use of passages or subdocuments to reduce the noise effect of large documents has been used for several years in the pircs system
the issues of long documents with their higher frequency terms mean that the algorithms originally built for abstract length documents need rethinking
null two level searching scheme in which the documents are first ranked via coarse grain methods and then the resulting subset is further refined
in both cases the improvements come from finding more relevant documents because of the expansions but different expansion methods help different topics
the assctv1 nan also represents a manual expansion effort but using a pre built thesaurus as opposed to using textual sources for the expansion
the trec NUM adhoc evaluation used new topics topics NUM NUM against two disks of training documents disks NUM and NUM
a short summary of the techniques used in these runs shows the breadth of the approaches and the changes in approach from trec NUM
extracted by the system through the ana logy ba sed
through i a ra lighi extension
we have seen that both e v n and the good turing estimates mct f m especially for f NUM lead to underestimation of population values
in these novels the maximal divergence appears early on in the text after which the divergence decreases until just before the end v n becomes even slightly larger than its expectation
the number of different words expected on the basis of the urn model to appear in for example the first half of a text is known to overestimate the observed number of different words
NUM the growth curve of the vocabulary let n be the size of a text in word tokens and let v denote the total number of different word types observed among the n word tokens
if lexical specialization affects the influx of new types its effects appear not in the central sections of the novel as suggested by figure NUM but rather in the beginning and perhaps at the end
according to the urn model however such a sequence is likely to occur once every NUM words the relative frequency of the in english is approximately NUM NUM say once every two pages
NUM the number of chunks in which an underdispersed word appears and the frequencies with which such a word appears in the various chunks can not be predicted on the basis of the urn model
the number of text chunks exploited in this paper NUM has been chosen to allow patterns in sampling time to become visible without leading to overly small text slices for the smaller texts
however this is only a weak lexicalization because the trees generated by the lexicalized grammar are not the same as those generated by the original cfg
any complete derivation in g can be converted into exactly one derivation in g as follows a derivation consists of elementary trees and operations between them
a constructive procedure is presented for converting a cfg into a left anchored i.e. word initial ltig that preserves ambiguity and generates the same trees
however two different trees can be derived one where the left auxiliary tree is on top and one where the right auxiliary tree is on top
rule NUM predicts the presence of a left auxiliary tree if and only if a node that the auxiliary tree can adjoin on has already been predicted
in the worst case a grammar with m nonterminals can have m sets of mutually left recursive rules and the result ltig will be enormous
however as noted at the end of section NUM counting the number of elementary trees is not an appropriate measure of the size of an ltig
since the trees in t are the only x rooted trees in a all the trees being simultaneously adjoined must be instances of trees in t t
by relying on the fact that the intersection of two regular languages must be regular it is easy to show that l is not a regular language
a special user friendly interface was developed for this purpose allowing editing scaling viewing lltbelling and pitch marking of the speech signal t irst the approxiumte neighborhood of a diphone was determined then a fine labeling of its boundaries was performed and the center of the phoneme transition was marked l inally pitch markers were manually set for voiced parts of tile corresponding speech signal
due to the properties ot the slovenian language some phones are composed of several phone components like the stop consonants k p b d t and the affricates c and such phones are described by multiple submodels
stressed syllables are longer thus less submitted to coarticulation which results in easily chainable units while unstressed ones are more ntnnerous in natural speech so that producing them efficiently would both increase segmental qt ality and reduce lrlcmory requirements
hidden markov models hmms are stochastic finite state automata that consist of a finite number of states modelling the temporal structure of speech and a probabilistic function for each of the states modelling the emission and observation of acoustic feature vectors rabiner89
first words are syllabitied by counting the nmnber of their vowel clusters and duration of syllables is modelled according to the speaker s normal articulation rate depending on the number of syllables within a word and on the word s position within a phrase
a diphonc is generally speaking a unit which starts in tim middle o1 one phone passes through the transi tion to the next phone and ends in the middle of this next phone
in section NUM we this work was partly funded by the commission of the european community under cop NUM contract no NUM sqel explain how we intend to automatically derive additional diphone inventories for building new synthetic voices
while concatenating diphones into words it suddenly turned out that there was a large discrepancy between the duration of allophones as suggested by the prosody module and the actual corresponding diphone duration stored in the diphone inventory
the concept explication is an explanation written in natural language tor the p i o c of assisting humans in differentiating one nccpl imm another
the first version of edr dictionary vi NUM and its revised version v1 NUM are already released and are now utilized at many sites for both academic and commercial purposes
relationships between sections of edr electronic dictionary
headconcept and concept explications are provided as accampanying information
i will illustrate this with a case study of the recognition of embedded case law citations including anaphoric references and case names
commercial implementation of text recognition tools for vlc
most surprisingly each of of these parsers incorporates a very different model yet they perform similarly
this technique allows for flexible reprocessing of vlc that might otherwise not be done when improvements to algorithms are made
implementation of products or features using these tools for vlc can require months of processing or even years
the double subject constmctiou having an miective predicate actually has several wlrimlts so no one approach c m be uscd to mlalyze it
the role relegated to the lexical scanner is usually the simple tokenization while the relationships of the tokens to one another is done by the parser
in this talk i will describe how extending the capabilities of the lexical scanner while optlmi ng its performance can allow it to complete the recognition work traditionally done by parsers
type determination first lype NUM is set if the modifier with adverbial parlicle wa represents a time expression or not
this lends to two possible attachment sites the verb and the object of the verb
the initial set included the following features null preposition
adverbial particle adverbial particles give a case an additional function topicalizalion etc by their allachmenl to ibc cascmarkingparlicle
the graph compares the three different methods of handling unknown words
the following describes how ordintu y sentences are analyzed by alt j e using the valency structure for the predicate of file input sentence
this paper proposes a method tbr analyzing a japanese double subject construction having an adjective predicate in order to overcome lhe problems described
one of the exercises proposed by the software consists in putting and moving objects on a board
argmax p eif NUM e by bayes theorem this is equivalent to finding such that argmax p fie p e NUM e
specifically we can not expect that for every feature f e the estimate of f we derive from this sample will be close to its value in the limit as n grows large
the most important practical consequence of this result is that any algorithm for finding the maximum a of a can be used to find the maximum p of h p for pec
the entropy is bounded from below by zero the entropy of a model with no uncertainty at all and from above by log lyl the entropy of the uniform distribution over all possible lyl values of y
our goal is to extract a set of facts about the decision making process from the sample the first task of modeling that will aid us in constructing a model of this process the second task
baseball managers who rank among the better paid statistical modelers employ batting averages compiled from a history of at bats to gauge the likelihood that a player will succeed in his next appearance at the plate
with a slight abuse of notation we will also use p ylx to denote the entire conditional probability distribution provided by the model with the interpretation that y and x are placeholders rather than specific instantiations
to study the process we observe the behavior of the random process for some time collecting a large number of samples xl yl x2 y2 xn yn
i propose the following heuristic heuristic NUM select those segments that are about the most frequent cb in the text NUM for the summary
this establishes a right to left labeling of constituents rather than the left to right labeling that the baseline representation incorporates
this platform does n t integrate all the project outcomes but some of the 5mdamental resources and basic tools since it reflects the current configuration that is not concrete but open to changes
it will be extended to cover chinese japanese and german as well as more domains including electrical electronic engineering medical science law etc
we took the hand transcribed pronunciations of each word in timit and computed rule probabilities by the same rule tag counting procedure used for our forced viterbi output
all the lexicon sources except limsi use arpabet like phone sets NUM cmu britpron and pronlex phone sets include three levels of vowel stress
approximately NUM of the suggested connections are correct
the procedures for acquiring these rules is also described
examples of its output are provided in section NUM
the rest of this paper is organized as tbllows
figure NUM presents the sensealign algorithm
alignment rules can be acquired from a bilingual corpus
c two thesauri for classifying words
table NUM q he final alignment
consequently p reflects the status of ongoing projects and is an as is framework on which further researches and development works can be performed
since ip plays a key role in the effort we hope that our endeavors would be well geared to the needs of nation wide language engineering
since we aim to provide software versions on unix solaris and pc windows altogether initial hardware requirements for each tool may be different
a o her division handles standardization issues for code schemes and w cabnlaries keyboard layout standard text formats and internationalization
its other characteristic lies in the common gateway interface cgi which makes it possible to interface with various shell scripts and program codes without difficulties
it realizes linguistic activities of everyday life and linguistic competence of human beings with the aids of computer science thereby supporting people s intellectual linguistic productions
by running this algorithm on a large corpus of sentences we produce a list of bottom up pronunciations for each word in the corpus
the algorithm is based on using a speech recognition system to discover the surface pronunciations of words in spe ech corpora using an automatic system obviates expensive phonetic labeling by hand
in the first step phonetic likelihood esti mation we examine each 20ms frame of speech data and probabilistically label each frame with the phones that were likely to produce the data
however phones vary in length as a function of idiolect and rate of speech and of course the very fact of optional phonological rules implies multiple possible pronunciations for each word
we describe the details of our algorithm and show the probabilities the system has learned for ten common phonological rules which model reductions and coarticulation effects
note that all of the rules are indeed quite optional even the most commonly employed rules like flapping and h voicing only apply on average about NUM of the time
in our grammar as in the anlt grammar x bar schemata are respected in general although there are differences of detail
in this paper we highlight the many similarities between the two grammars but especially differences due to phenomena particular to french
consider figure NUM which plots the number of times ahab appears in NUM successive equally sized text slices that jointly constitute the full text of moby dick
NUM NUM NUM NUM fax NUM NUM NUM NUM email hovy isi edu
lexical aggregation can be divided into two major types bounded and unbounded
persistent knowledge repository document management user information requests user information outputs
representative document structures are maintained in the document structure library
without aggregation automated language generation systems would not be able to produce fluent text from real world databases and knowledge bases since information is rarely stored in computers in forms directly supporting fluent expression
with bounded lexical bl aggregation the aggregator lexeme covers a closed set of concepts and the redundancy is obvious the aggregated information is recoverable and the aggregation process must be carried out
hercules dalianis NUM and eduard hovy NUM NUM department of computer and systems sciences the royal institute of technology and stockholm university electrum NUM s NUM NUM kista sweden mob
various types of aggregation syntactic lexical referential have been identified in regation the process by which a set of items is replaced with a single new lexeme that encompasses the same meaning
example of unbounded and bounded lexical aggregation
annotations may be created and or used by the extraction component
in addition the text becomes easier to read
example taken from wall street journal NUM march NUM NUM NUM words which together with asiatisk dagbok NUM NUM NUM words contains NUM NUM words and NUM NUM sentences in both english and swedish
before studying how these words appear in texts and how they affect the growth curve of the vocabulary it is useful to further refine our definition of underdispersion
the other side of the same coin is that good s estimate for the probability mass of unseen types NUM n is an upper bound
they measure agreement over both a pool of highly experienced coders and a larger pool of mixed experience coders and argue informally that since the level of agreement is not much different between the two their coding system is easy to learn
on the other hand passonneau and litman note that their figures are not properly interpretable and attempt to overcome this failing to some extent by showing that the agreement which they have obtained at least significantly differs from random agreement
we discuss what is wrong with reliability measures as they are currently used for discourse and dialogue work in computational linguistics and cognitive science and argue that we would be better off as afield adopting techniques from content analysis
without knowing the density of conversational move boundaries in the corpus this makes it difficult to assess how well the coders agreed on the absence of boundaries or to compare measures NUM and NUM
this process consists of a series of hierarchical structure building activities in which high level linguistic structures are formed from their constituents and get properly hooked up to each other as the process converges
thus our system has an extra degree of flexibility which allows uphill steps in temperature in effect this means that the system is annealing at the metalevel as well
sentence NUM is an example with local overlap and combination ambiguities in the NUM diverse paths refers to different ways of analyzing the structure of a sentence
to derive affinity relations between characters we have the usage frequencies of NUM NUM chinese characters specified in the gb2312 NUM standard and the usage frequencies of NUM NUM words derived from a corpus
at cycle NUM the temperature is still clamped at NUM and hence the temperature regulated strengths of these two competing structures are both NUM rounded up to the nearest integer
the system currently adopts a greedy approach and starts posting large numbers of this type of codelet as soon as it has identified a plausible interpretation of the word boundaries of a sentence
a codelet is a piece of code that carries out some small local task that is part of the process of building a linguistic structure
this paper suggests that the language understanding process can be effectively modeled as the statistical outcome of a large number of independent activities occurring in parallel
this paper proposes that the process of language understanding can be modeled as a collective phenomenon that emerges from a myriad of microscopic and diverse activities
now consider measure NUM which has an advantage over measure NUM when there is a pool of coders none of whom should be distinguished in that it produces one figure that sums reliability over all coder pairs
where p a is the proportion of times that the coders agree and p e is the proportion of times that we would expect them to agree by chance calculated along the lines of the intuitive argument presented above
NUM as a simple example of a clause le4el pattern con null this specifies a clause with a subject of class cperson a verb of class c run which includes run and head and an object of class c company
however we have recently found ourselves at a disadvantage with respect to groups which performed more local pattern matching in three regards NUM our systems were quite slow in processing the language as a whole our system is operating with only relatively weak semantic preferences
using defclansepattern reduced the number of patterns required and at the same time slightly improved coverage because when we had been expanding patterns by hand we had not included all expansions in all cases
in other words ttl2 ij represents the combined effect of the values i and j for the first and second variables on the logarithms of the expected cell counts
since our training data are bracketed it was possible to estimate tile lexical associations with much less noise than hindle r ooth who were working with unparsed text
a loglinear model is a statistical model of the effect of a set of categorical variables and their combinations on the cell counts in a contingency table
therefore we investigated the effect of different types of models for unknown words on the error rate for tagging text with different proportions of unknown words
to create a model that combines various structural and lexical features without independence assumptions we implemented a loglinear model that includes the variables verb level first noun level
as the diagram shows the accuracies for both methods rise with the first few features but then the two methods show a clear divergence
in the second series of experiments we compare the performance of different statistical models on the task of predicting prepositional phrase pp attachment
we reimplemented this model by using four features pos inflection capitalized and hyphenated in figures i NUM the results for this model are labeled NUM independent features
the simpler v np pp pattern with two syntactically different attachment sites yielded a null result the loglinear method did not perform significantly better than the lexical association method
cd rom to support document processing tasks in different applications
the architecture shall allow for sharing of persistent knowledge items
some requirements may be traced to specific documents given below
cscs may be further decomposed into other cscs and csus
this serves to bound the component and there by modularizing it
a document shall always be available in an original form
the specific necessary group of attributes is application dependent
therefore the sender should give or the receiver should infer with what coding system the received coded sequence is encoded
however on the www active discussion still continues on the www as to how to deliver the coding system
in summary a more sophisticated method is required to identify the coding system from the content of the code string
a coding system consists of a character set and an encoding s te ne
for example the lnternet mail protocol can transfer the coding system with which the message is encoded
loop NUM the second loop tries to identify the language of each east asian character string in ca string list
a notable example is the explosive growth of world wide web www involving more than NUM million documents
in contrast english has only one type of characters
the system is extendable by registering statis null words
insignificant mutual information values are filtered out by thresholding t score
many words occur just once which weakens the statistics approach
the rest of the algorithm took NUM seconds in all
we briefly report here the computation time of our method
we have described a text alignment method for structurally different languages
statistics repeats the iteration by using statistical corresponding words only
asm represents possible sentence correspondences and consists of ones and zeros
table NUM shows the word correspondences that have high mutual information
they count an error only when the system segmentation violates morpheme boundaries
our method might be called n best reestimation
the tag unk represents an unknown word
the training texts contained NUM word types
possible sources for static combination include mrd entries wordnet levin verb classes corpus statistics and other lexical resources
all combinations of these words are examined
figure NUM japanese morphological analysis example
the training texts contained NUM character types
words are listed in unix sdiff style
1deg for instance it appears that the relationship between stenose and lesion which was central in figure NUM with NUM shared contexts almost diseappears if one considers the number of shared cooccurrences
for the t NUM boundaries the superior recall of ea compared with conditions NUM and NUM of the automated algorithms is significant
when trying to identify essential concepts and relationships in a medium size corpus it is not always possible to rely on statistical methods as the frequencies are too low
by using enriched linguistic information and by allowing more complex interactions among linguistic devices both methods achieve results that approach human performance
table NUM presents the reliability results from a comparison of boundaries found by two distinct partitions of subjects responses on four narratives
on the other hand medium size corpora between NUM NUM and NUM NUM words typically a reference manual are already too complex and too long to rely on reading only even with concordances
on the other hand s he must relate these entities and relationships to their linguistic realizations so as to isolate the lexical entries to be considered as certified terms for the domain
symbolic word clustering for medium size corpora benoit habert and elie naulleau and adeline nazarenko equipe de linguistique inform tique ecole norinale sup ieure de fontenay st cloud NUM av
null we experimented so t r two parsers aleth h am and i exl er which are being used at der edi for terminology acquisition and updating
NUM determine through a dialog with the system which conjnnct fits best at the start of the sentence under consideration
if they do not it accepts the answer provided by trigrams if they do it applies bayes
NUM there is only one verb komaru be in trouble the active voice of which shows two nominative cases cga s
our aim i martin75 fm t NUM contain some lists but is too partial for the purpose of developing an mt system
why is it not necessary to have more slots though we know there are definitely more than seven case postpositions in japanese
the developed lexicon is adopted in a real world scale intedingua based mt system that translates between english and japanese muraki87
the future tasks should include further explorations of providing the concept dictionary with more syntactic test conditions and extensions to more than two languages other than english and japanese
the reduction of the number of verb subeategorization codes was made possible by carefully identifying superficially different case frames with the idea of alternative ease markers and semantic roles
another slot dat in fig NUM shows that he could replace the major case postposition ni and be assigned the thematic role goal
x does not want to be lbrced to eat y by z since three auxiliary verb forms caus pass and want appear by this ordering in e.g.
NUM two dummies are replace to the left of the first and to the right of the last word of the source sentence
the evaluation is based on composite scores of various factors applicability specificity fan out relative distortion probabilities and evidence from bilingual dictionaries
n th order ergodie multigram hmm for modeling of languages without marked word boundaries
by using a word based approach less frequent words or words with diverse translations generally do not have statistically significant evidence for confident alignment
here the training data were used primarily to acquire rules by a greedy learner and to determine empmcally probability thnctions of various factors
we anticipated that removing them from consideration would highlight the true differences in the figures of merit
normalized alf in the previous section we showed that our ideal figure of merit can be written as
from the edge count statistics it is clear that straight NUM is a poor figure of merit
the best performer in running time was the parser using the trigram estimate as a figure of merit
null NUM non NUM e the percentage of nonzero length edges used by the best first parse to get NUM
zero length edges are required by our parser as a book keeping measure and as such are virtually un elimitable
the denominator p t0 k is once again calculated from a tritag model
the first term in the numerator is just the definition of the outside probability a of the constituent
chitrao and grishman implemented a best first probabilistic parser and noted the parser s tendency to prefer shorter constituents
using word frequency lists to measure corpus homogeneity and similarity between corpora
firstly what is hnportant about texts is their me ing
one may contain hundreds of words another hundreds of millions
also most contain some texts that are much bigger than others
the specifics of how to run the erb are given in section NUM NUM below
NUM NUM NUM verification method inspection
user interaction shall be part of an application through modular interfaces
these groups are necessary for retrieval purposes and to control processing
it shall be possible to make corrections annotations to corrections annotations
the architecture permits a standard code set for all internal operations
NUM NUM NUM verification method inspection
NUM NUM NUM verification method demonstration
NUM NUM NUM verification method demonstration
this requirement is applicable to requirements NUM NUM NUM NUM NUM NUM and NUM NUM NUM
sample or training templates may be used to assist the user
in this section we describe a maximum entropy model that given a french noun de noun phrase estimates the probability that the best english translation involves an interchange of the two nouns
this principle instructs us to choose among all the models berger della pietra and della pietra a maximum entropy approach consistent with the constraints the model with the greatest entropy
for each sentence pair we use the basic translation model to compute the viterbi alignment between e and f using this alignment we then construct an x y training event
we sometimes make the berger della pietra and della pietra a maximum entropy approach simplifying assumption that there exists one extremely probable alignment NUM called the viterbi alignment for which
the reader will notice that a safe segmentation does not necessarily result in semantically coherent segments mes and devoirs are certainly part of one logical unit yet are separated in this safe segmentation
specifically we consider functions f x y that are one if y is some particular french word and the context x contains a given english word and are zero otherwise
this constraint requires that the model s expected probability of dans if one of the three words to the right of in is the word speech is equal to that in the empirical sample
although a detailed account of this relationship is beyond the scope of this paper it is easy to state the final result suppose that a is the solution of the dual problem
we let y be no interchange if the english translation is a word forword translation of the french phrase and y interchange if the order of the nouns in the english and french phrases are interchanged
in particular the growth in the number of rules is at worst o ms where m is the number of nonterminals
the contingency matrix shows a the number of pis in which both wjp and w ng were found b the number of pis in which just w g was found c the number of pis in which just wjp was found d the number of pis in which neither word was found
the japanese morphological analyzer we used does not contain an entry for i i and split it into a sequence of three characters and
NUM if the following three conditions hold add NUM to the i j element of am NUM jsentencei and esentencej contain a stochastic word correspondence wjpn and w na that has mutual information ihig h and whose t score exceeds thigh
NUM if the following three conditions hold add NUM to the i j element of am NUM jsentencei and esentencej contain a stochastic word correspondence wjp and we g that has mutual information itoto and whose t score exceeds ttow
figure NUM a modified version of kmp for chinese string searching
here we assumed that a null character is patched at those positions
to this end we split the whole data set into two parts half for building lms and half reserved for testing
starting with an initial segmented corpus and an lm based upon it we use a viterbi liek algorithm to segment another set of data
starting with an initial segmented corpus and an lm based upon it we use viterbi like algorithm to segment another set of data
thus it is necessary to decode the input data as characters
a conceptual array which can hold both single and NUM byte characters
figure NUM string searching of multi byte characters using the finite automaton
searching is implemented as state transitions of m figure NUM
se e tl e fbllowing examples with conjunctive pardcles ode c c t t nagara NUM NUM is added to meaningless sentences
in most cases auxiliary verbs such as darou z NUM may maybe or ta hou ga yoi t it NUM NUM NUM had better should preferably express the modality of the modifee clause
secondly there is no encoding of the requirement to find the correct number of subcategorized entities because p a p is logically equivalent to p
notice that the components of the tuple are values that can appear in boolean combinations for they must be of the same type as the subcat feature
corrections are made through the use of annotations
document collection is an unordered set of documents
discourse knowledge engineers can express these inclusion conditions as predicates on the knowledge base and on a user model if one is employed
edps have several types of nodes where each type provides a particular set of attributes to the discourse knowledge engineer table NUM
because edps are frame based they can be easily viewed and edited by knowledge engineers using the graphical tools commonly associated with frame based languages
c edp node o edp node instantiated kb accessor view
for each node in the edp the planner determines if it should construct a counterpart node in the explanation plan it is building
explanation planning is a synthetic task in which multiple resources are consulted to assemble data structures that specify the content and organization of explanations
in the course of our research we informally reviewed numerous on the order of one hundred passages in several biology textbooks
lester and porter robust explanation generators hovy NUM maybury NUM moore NUM moore and paris NUM suthers NUM
basically a complete organization name can be divided into two parts name and keyword
besides error candidates and organization names without keywords error left boundary is also a problem
the increase of the precision in cohmm NUM verifies this idea
the entry which has clear right boundary has a high weight
equation NUM is simplified from equation NUM
in total this corpus has NUM NUM words and NUM NUM characters
almost all foreign names are in transliteration not in translation
moreover on the language engineering standpoint the main consequences are a significant data compression and a corresponding improvement of the overall system efficiency
after a first scan of the corpus by the ssa and after the computation of global mcpi values a primary knowledge base is available
application corpora are noisy may not be very large and include repetitive and complex ambiguities that are an obstacle to reliable statistical learning
many ambiguities occur within several identical phrases hence the wrong and the right associations may gain the same statistical evidence
however the phenomena that we analyzed in section NUM have a negative impact on the possibility of a longer incremental learning process
if the corpus is our unique source of knowledge it is not possible to learn things for which there is no evidence
the conclusion we may derive from these two experiments is that most syntactic disambiguation methods presented in literature are tested in an unrealistic environment
notice that the phenomenon of systematic ambiguity is much less striking lower mi and higher variance though it is not eliminated
fig null ure NUM c measures the data compression that is the mere reduction of eis s in the corpus
thus the con cct segmentation ie
this f dback enables tile bootstrapping acquisition of evidence
we can be optimistic about eliminating these three types of errors
NUM NUM selection of proper analysis NUM NUM NUM cost calculation and mutual information
patterns in NUM NUM collect the evidence of a coordination relationship between two words
note that the number of occurrences uxt the observed relationships are recorded
one can add any pattern as long as it supplies reliable evklence
fable NUM shows the main part of the pattern matchers
baselines have rarely been introduced in research on japanese noun compounds
they are simple units with no internal structure and no interruption point
these segmentations were based on pauses silences non speech elements e.g.
table NUM trigram perplexity on lm95 swbd
this led to a degradation in performance
multiple restarts are handled as embedded and are repaired from left to right
henceforth these models are referred to as the ling seg and acoustie seg models
you re asking what my opinion about a NUM yeah
the context from the previous lattice was not provided for the current lattice
however this information can not easily be derived from the acoustic signal
a tig derivation starts with an initial tree rooted at s this tree is repeatedly extended using substitution and adjunction
when we transformed the lexicon into word shape token representation the number of distinct entries was reduced to NUM NUM
if assembled properly these pieces could result in very powerful language learning systems
minimizing cultural differences they ve been able to draw on shared background knowledge microworld immersiveness
there are many ai and cl programs solving various specific call relevant problems
that s why it is not easy to find a ready made plug in module for call systems
corpus linguistics has become el very promising and active area of investigation
such a grammar should have at least the following qualities parsing the student s input it should be error tolerant yet having a broad enough coverage for being useful both for beginners and for advanced students
other important aspects of typical immersion based approaches i.e. natural learning such as mixedinitiative fault tolerance dialogue repair cooperative behavior etc are still in their infancy
language technology has significantly evolved during the last decade
error correction is usually done contextually by drawing either explicitly attention to the deviation by producing a similar but correct sentence or by simply ignoring the mistake leaving its correction for later
accordingly information retrieval has become one of the most important research topics in natural language processing nlp
NUM inflect open class words morphological processillg e.g. the verb to hand as hands in NUM
buys the book involves successively accessing the sub grammars for the clause the verb group the nominal group twice and the determiner sequence
this paper describes a short demo providing an overview of surge systemic unification realization grammar of english a syntactic realization front end for natural language generation systems
NUM modifying the nominal grammar to support nominalizations and some forms of syntactic alternations and NUM improving the treatment of obligatory pronominalization and binding
figure NUM an lcb xample st rge i o ternations NUM e.g. adding tile dative move yes feature to NUM would
NUM control syntactic paraphrasing and al null result in the generation of the paraphrase NUM she hands the editor the draft
each sub grammar is then divided into a set of systems in the systemic sense each one encapsulating an orthogonal set of decisions constraints and features
the main top level syntactic categories used in surge are clause nominal group or np determiner sequence verb group adjectival phrase and pp
yet the argument structure and or semantics of many english verbs do not fit neatly in any element of this hierarchy NUM
both the input and the output of a fuf program are fds while the program itself is a meta fd called a functional grammar fg
build always processes the leftmost tree without any start x or join x annotation
null it may be the case however that the icd does not include specifications for the modules the developer needs
similarly the research community can take advantage of the architecture to facilitate the testing of new ideas in advanced text handling
developers of new tipster compliant text processing applications will follow the existing icd specifications for those modules whose functionalities are included in their application
developers who do not produce entire applications will still be able to produce architecturally compliant components or modules in their areas of expertise
the more widely the architecture is used the better will be the basis on which to estimate cost and staffing requirements
the use of common shared modules in the architecture will require the systcm support officer to become knowledgeable about fewer application parts
in this way proof of concept and hypothesis testing can be performed with more comprehensive realistic applications and thus more effectively
the tipster architecture is not envisioned to encompass the processing of non generic parts such as specific types of tables or outlines
to exploit document parts markups must indicate at a minimum boundaries between the parts and identify the part type
moreover any new annotations that might become available represent a limited subset of the infinite number of ways that ne data might be subcategorized in accordance with particular interests applications and capabilities
the third pass terminates when check is presented a constituent that spans the entire sentence
cons m cons n cons p
score t h q adbi a ederiv t
this oscillatory structure receives some support from the autocorrelation functions shown in the second row of panels
for n NUM NUM the error is large NUM NUM of the actual vocabulary size
here the proportion of new underdispersed tokens on the total number of new tokens is defined as
as the novel draws to its dramatic end the frequency of ahab increases to its maximum
what we find is that he is not mentioned at all in the first five text slices
to see this consider error scores for the influx of new types in alice in wonderland
the most frequent word a occurs NUM times among a total of NUM word occurrences
reproduced by permission h om tile american iteritagi first dic i ionaily
the resuiting maximal comlnon snbgraph as shown in figure NUM contains the concept label NUM
the notion of semantic distance can be seen as the informativeness of the subsuming graph
figure NUM is NUM NUM computed as in figure NUM rule NUM is used once and rule NUM is used twice
if r NUM then we accept y with a probability that diminishes as r increases specifically with probability NUM r
in the present paper i define stochastic attribute value grammars and give an algorithm for computing the maximum likelihood estimate of their parameters
the empirical probability of the former is NUM NUM NUM NUM NUM NUM and the empirical probability of the latter is also NUM NUM
for stochastic context free grammars it can be shown that the erf method yields the best model for a given training corpus
we would like to do both in a way that permits us to find the best model in the sense of the model that minimizes the feature selection
writing q for the distribution that results from assigning weight fl to feature f fl is the solution to the equation
we have seen that random sampling is necessary both to set the initial weight for features under consideration and to adjust all weights after a new feature is adopted
it could be the case that no good attribute exists for columns
any constituents scoring less than the threshold are pruned out
in practice this is still unacceptably high for most applications
such dictionaries are only effectively usable for their own domains much human labor will be mitigated if such a dictionary is obtained in an automatic way from a set of translation examples
the recall rates are shown in parentheses which indicates how much proportion of the words with two or more occurrences in the corpora are finally participated in at least one translation pair
kumano hirakawa stand on a different setting from the other works in that they assume ordinary bilingual dictionary and use non parallel non aligned corpora
any assessment of the component confidence of the slot fills shall also be available for viewing and if tags or links were edited by the user the confidence field may also be edited
the goal is NUM seconds for interactive activities such as document or list displays and a few tens of seconds for activities such as query or search that require significant computer resources
the detection process shall use the detection criteria in conjunction with document management and the persistent knowledge repository to select and route documents from document sets
the routing function is expected to have compared within some specified interval that may be minutes to hours each new document with all profiles
the architecture shall allow a multi lingual extraction component that represents template definition in any combination of multi lingual fill rules template objects slots and documents
it is here that document information is manipulated in various ways to support the identification of a desired sub set of documents or extracted information
in general the architecture shall not restrict an application from using the architecture shall support conversion of document files written in various character encodings to a standard encoding before tipster processing
this includes providing interfaces for a program that shall take an english selection statement and shall return a version of the selection statement in another language or several versions in several different languages
components shall be scaleable to a large number of documents and a high document flow rate up to a maximum of NUM NUM NUM documents per day with access to NUM terabytes of text
NUM NUM successor versions to the original document shall be marked with a revision number and the document id and the cause for revision recorded as a document history attribute
finally we show experiment results and prove its effectiveness
this translation uses words that do not correspond to individual words in the source the english translation of prouver is prove and son adhdsion translates as one s adhesion
the third and fourth columns give the independent frequencies of each word group while the fifth column gives the number of times that both groups appear in matched sentences
we asked several people fluent in both french and english to judge the results and the accuracy of champollion was found to range from NUM to NUM
the rosetta stone is a tablet of black basalt containing parallel inscriptions in three different scripts greek and two forms of ancient egyptian writings demotic and hieroglyphics
here the verb functions as a support verb it derives its meaning in good part from the object in this context and not from its own semantic features
in this case the test is easier whereas the dice coefficient is equal to p x NUM p x NUM to decide using the dice coefficient
computational linguistics volume NUM number NUM sentences a NUM NUM match certainly contributes to their association and increases our belief that one is the translation of the other
to rank the proposed translations so that the best one is selected champollion uses a quantitative measure of correlation between the source collocation and its complete or partial translations
we have thus assumed in champollion that these tools were only available in one of the two languages namely english termed the source language throughout the paper
automatic identification of the coding system is achieved in communities where a limited number of coding systems are used
with this method words which at one time are moved to a new region in the classification hierarchy can move back at a later time if licensed by the mutual information metric
figure NUM summarizes the test set perplexity results
its m t value is calculated
in theory structural tag representations can be dynamically updated for example bank might be close to river in some contexts and closer to money in others
many levels of classification granularity can be made available simultaneously and the weight which each of these levels can be given in for example a statistical language model can alter dynamically
we add these words randomly due to hardware limitations though we notice that the NUM NUM th most frequent word in our vocabulary occurs twice only a very difficult task for any classification system
using the top two sentences the boy seat the sandwiches and the boys eat the sandwiches we can examine the practical benefits of class information for statistical language modeling
can also give rise to the observed phonemic stream
the w a language model ranks the preferred sentence second
the success of a negative rule is equivalent to a failure of the hypothesis
most of the expressions parsed by that system are transferable in the system poleng
NUM the phase of creating the dictionary of canonical forms
software enables the storing of lexical data in a finite automaton
in a polish sentence verbs are characterised both by preand post modifiers
each form inherits the syntactic semantic information from its canonical form
this deterministic part of the algorithm has a notable impact on the effectiveness of the translation process
the problem of determiners is not solved at all all noun groups are assumed definite
the processes of syntactic parsing semantic analysis transfer and morphological generation are not separated
all vectors are approximately perpendicular to one another
this learning law is motivated by the following observation
each component has access to the common data structure through a unique interface provided by the tipster document manager developed at crl
the tree structure manipulated by the gbmt engine contains both the source tree and the target tree which are simply source and target projections of the same data structure
thus when a translator modifies the lexical database figure NUM the modifications are immediately seen and used by the glossary based engine in the machine translation system
a multilingual dictionary and glossary editor and utilities to parse and load flat dictionary machine readable dictionaries and glossary files into the system s lexical database
the temple project has built upon this experience and extended the gbmt approach to other languages japanese arabic and russian
an nlp component interface to the document manager includes a mapping from the component representation to the temple internal unique linguistic representation
the translator uses a phrase extraction utility to build a list of recurring patterns of words in a corpus ngrams
these tools work on large tagged corpora and use statistics on co occurrence of words in a given corpus to extract phrase patterns
a gbmt system produces a phrase by phrase translation of the source texl falling back on a word by word translation when no phrase from the glossary matches the input
c otherwise increase the s me by one and remove decisions against the majority so defined
we use a corpus of coded texts where each sentence is represented with a set of attributes and assigned to either a yes or a no r category according to whether the sentence is a summary extract selected by a group of humans with some level of agreement among them
we represent the assignments data as an n x m matrix table NUM where the value n j at each cellij NUM i n NUM j m denotes the number of raters assigning the ith object to the jth category
NUM data with various levels of agreement can be obtained by removing from agreement tables those decisions which are against the majority opinion for various values of r NUM of them only those data whose agreement rate is over a specific k threshold are used as training test data for automatic abstracting
this attribute is categorical and takes one of the three values type NUM type NUM and type NUM depending on whether the sentence ends with a verbal of non attitudinal type type i or with an attidutinal verbal or a modal type NUM or with a sentence final particle type NUM
in particll r we use a NUM fold cross validation method where the data are divided into NUM blocks of cases of which NUM blocks are used for the training and the remaining one for the edata with the threshold NUM NUM for instance consists of coded representations of texts whose agreement rate is above or is equal to NUM NUM
what it does in essence is to classify sentences as either yes or no w based on a prediction it makes about whether a given sentence is to be included in a sllmmary extract
we demonstrate that there is a positive correlation of data reliability with a performance of automatic abstracting and show results indicating that the reliability of human provided data is crucial for improving the performance of automatic abstracting
we did not attempt to specify a formal set of rules for the taggers to follow
we have observed taggers desires to take into account all aspects of the general problem surrounding the task
if for example the taggers are being asked to categorize objects into one of a set of mutually exclusive exhaustive classes for most nlp problems the taggers will be faced with borderline ambiguous and vague instances
unlike word tribayes as presented above is purely a predictive system and never suppresses its suggestions
tribayes is also compared with the grammar checker in microsoft word and is found to have substantially higher performance
recent results suggest that many of our manually coded features have the promise of being automatically coded
the results from the first part of our study section NUM support these hypotheses
in such cases we accepted the first suggestion and then moved on
while this method does not make sense for humans computers can truly ignore previous iterations
however as with the training data ea has somewhat less variation around the average
then the first branch is taken and the potential boundary site is assigned the class nonboundary
isting feature representation it does not facilitate experimentation with large sets of multiple features simultaneously
the algorithms presented in section NUM NUM indeed use more features as shown in figure NUM
that is a boundary is proposed if some combination of the algorithms proposed a boundary
alphabet declarations take the form tl alphabet tape symbol list and variable sets are described by the predicate tl set lcb id lcb symbol list rcb
word formation rules take the form of unification based cfg rules synrule identifier mother daughter1 rcb daughtern rcb l
each method will be described in terms of its operation on a single confusion set c lcb wl w rcb that is we will say how the method disambiguates occurrences of words wl through wn
since this method is based on word trigrams it requires an enormous training corpus to fit all of these parameters accurately in addition at run time it requires extensive system resources to store and manipulate the resulting huge word trigram table
tribayes however achieves the maximum of their scores by and large the exceptions being due to cases where one method or the other had an unexpectedly low score discussed in sections NUM and NUM
the previous section showed that the part of speech trigram method works well when the words in the confusion set have different parts of speech but essentially can not distinguish among the words if they have the same part of speech
we would then calculate the probability that each word wi in the confusion set is the correct identity of the target word given that we have observed features NUM r using bayes rule with the independence assumption
moreover bayes use of context word features is arguably misguided here as context words pick up differences in topic and tense which are irrelevant here and in fact tend to degrade performance by detecting spurious differences
although this behavior is never observed in its extreme form it is a good approximation of word s behavior in a few cases such as lcb principal principle rcb where it scores NUM and NUM
we will not attempt to combine these two parameters into a single measure of system goodness as the appropriate combination varies for different users depending on the user s typing accuracy and tolerance of false negatives and positives
thus despite the high standard deviations NUM narratives seems to have been a sufficient sample size for evaluating the initial np algorithm
both methods consider much more knowledge than previously considered by ourselves or others and result in algorithms that exhibit marked improvements in performance
but the way s is representating the utterances allows the uniform translation of indefinite nps into an existential quantifier
the examples and the grammar descriptions i am using are taken from the german grammar see rieder al
this has been postponed to the semantic which is treated within the re finemenl component of the grammar
x man x a walk in the park x a whistle x
using the simple unification technique as for the processing of other linguistic phenomena within alep a resolution of the pronoun can then be tried out parts of the content information of the pronoun are going to be compared unified with specific parts of the content information of the possible antecedent
in the next section i will first show how larger linguistic units can be processed within the alep system
i will not discuss this point here but just mention that for the german grammar we should have a look at a detailled analysis of the meaning of such expressions NUM once this has been done we can encode this information in the lexicon as will be seen in the next section
in principle these are the steps which are necessary in order to extend the coverage of the grammar to larger linguistic units there is naturally some more technical work to be done but this will be described in the third chapter where i will go into more details of the architecture of the grammar development
they can bind variables only inside their scope every farmer who owns a donkey i beats it i every man i walks in the park
two strong assumptions which are controversial in the discussion on this topic are underlying the dpl approach indefinite nps are considered to be quantifiea ional expressions and pronouns to act like variables
we first evaluated an initial set of three algorithms each based on a single type of linguistic input and their additive combinations
figure NUM is a modified version of figure NUM showing the classification of the statistically validated boundaries in the same narrative excerpt
for any one narrative we should expect a new set of seven subjects to yield roughly the same set of segment boundaries
for example the second column shows the score of each character as regards to chinese zho when the input code string is decoded with euc gb
our basic idea is to use statistic language models to select the correctly decoded string as well as to determine the language
this poses a problem for current statistical and machine learning approaches to natural language understanding where a new instance representation is typically required for each linguistic task tackled
then when a new problem is encountered the most similar case is retrieved from the case base and used to solve the novel problem
the new approach is potentially more powerful than the decision tree method in that it can improve a baseline case representation in three ways rather than one NUM
until this point the best representation had been the combined recency representation which significantly outperformed the default heuristic but not the baseline case representation
more specifically the technique uses linguistic biases to discard irrelevant features from the representation to add new features to the representation and to weight features appropriately
very briefly in addition to storing training cases in the case base we use them to train a decision tree for each of the selected lexical tasks
class do s entity v exists np2 human ppl entity prevl syntactic type prep phrase class np2
training and test instances to include every feature of the normalized feature set filling in a nil value if the feature does not apply for the particular instance
this is in addition to more obviously relevant features e.g. the morphology of the current wordi the part of speech of the preceding and following words
we have shown empirically that the feature set used to describe training and test instances plays an important role for a number of tasks in natural language understanding
for trees of depth NUM there are two cases trivially these trees have the required probabilities
in the section covering previous research we considered the most probable derivation and the most probable parse tree
unlike a pcfg the use of trees allows capturing large contexts making the model more sensitive
our main theorem is that this construction produces pcfg trees isomorphic to the stsg trees with equal probability
we therefore think that it is reasonable to use a maximum constituents parser to parse the dop model
after that put the most likely constituents together to form a parse tree using dynamic programming
because this reduction is so much smaller we do not discard any of the grammar when using it
using ithe optimizations experiments yield a NUM crossing brackets rate and NUM zero crossing brackets rate
subderivations headed by aj with external non terminals only at the leaves internal non terminals elsewhere have probability NUM aj
because phoenix is capable of skipping over input segments that 1o not correspond to any top level semantic concept it can far better recover from out of domain se gments in the input and restart itself on an in domain segment that follows
resuits in the parser picking up and mis translating a small parsal le phrase within an out of domain irccent work on a method for pre brcaking the utterance at sentence boundaries prior to parsing have signiii antly reduced this l rol lem
intermediate level tokens distingnish between points and intervals in time for example lower level tokens cat ture the speciiics of the utterance such as days of the week and represent the only words that are translated directly via lookup tables
additionally a parse quality heuristic allows the parser to self null judge the quality of tile parse chosen as best and to detect cases in which important information is likely to have been skipt ed
we are developing lattice parsing versions of both the glr and phoenix parsers so that multiple speech hypotheses can be efficiently analyzed in parallel in search of an interpretation that is most likely to be correct
unlike the gi r method which attempts to construct a detailed tur for a given input utterance the phoenix approach attempts to only identify the key semantic concepts represented in the utterance and their underlying structure
in an attempt to achieve both robustness and translation accuracy we use two lifterent translation components the glr module designed to be more accurate and the phoenix module designed to be more robust
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
translation between any of the four source languages english german spanisil korean and five target languages english german spanish korean japanese is possible although we currently focus only on a few of these language pairs
the main components of an ilt are the speech act e.g. suggest accept reject the sentence type e.g. state query i fragment and the main semantic frame e.g. free busy
at times the learner may acquire a good contextual pattern but may bc unable to extend it to closcly related cases that would occur naturally m a linguist
for example the following rule assigns a label of oa to an unlabeled phrase just in case the phrase is ended by the word inc
sentence analysis is the process that converts the dependency structure into file valency structure by referring to wdency patterns
figure NUM shows an example of a valency pattern for a verb shoukai sunl introduce
next binding the other modifiers with the valency elements in the valency pattern for the prcdicate is attcmpted
accordingly this section classifies the four types mid the characteristics of eadl type e described
in figure NUM the noun phrase zou no hana is fornled from both zou elcphant with wa and hzma 0lose with ga by converting wa into no and zou no hana is bound to the subjective case for the predicate nagai long
n1 sr agent suljective jr NUM h j n2 sr agent n3 sr agent l objective2 jr
proxy myhousc destination school subjective hem case marking case marking particle ni pmticle ga type NUM in this wu iant adverbial parlicle wa is a proxy for case umrking panicle no representing a noun modifier pre nominal
as adverbial particle wa in type i cases is a proxy for a case marking particle such as ni de and so on type i cases can be processed in the way described in section NUM
he advenial her case marking like particle wa particle ga a re rcsenls r roxytor a
words are represented as structural tags n bit numbers the most significant bit patterns of which incorporate class information
the first author wishes to thank british telecom and the department of education for northern ireland for their support
we can replace the maximum likelihood bigram estimator in our interpolated trigram model with the smoothed bigram estimator
much work has been carried out on word based n gram models although there are recognized weaknesses in the paradigm
using the structural tag representation the computational overheads for using class information can be kept to a minimum
membership of the erb is se cm program manager chair the tipster application contracting officer s technical representative cotr supporting agency specific representative tipster expertise cm manager secretary contractor group representative s member software architecture engineer member the objectives of the erb are disposition of problems classification of proposed changes and disposition of class ii changes
tipster configuration management procedure cmp NUM change control tbd prc inc tipster configuration management procedure cmp NUM configuration auditing tbd prc inc tipster configuration management procedure cmp NUM configuration status accounting tbd prc inc
the program manager pm assists the chair of the architecture committee in meeting the objectives of the tipster architecture and demonstration programs with respect to requirements definition coordination of disparate contractors and approaches and supervision of the configuration management and verification validation efforts
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
verification and validation testing engineer the verification and validation v v testing engineer will develop and implement tests to determine if modules in tipster applications are consistent with the architecture design document by ensuring that they conform to the specifics in the architecture design document
when a constituent is removed from the keylist the system considers how this constituent can be used to extend its current structural hypothesis
this kind of over generalization can occur early in the learning process as new rules need only improve over an approximate initial labcting
as can be seen in order to disambiguate the case of cr the phonemic symbol ks and the graphemic state cr must be introduced
the system can be adapted to almost any language with little effort and can be implemented in hardware to serve in real time speech recognition systems
the proposed system assumes that the word boundaries are known that is it is a subsequent stage in an isolated word speech recognition system
examples of other useful linguistic biases to make available include minimal attachment right association lexical preference biases and a syntactic structure identity bias
the feature associated with the constituent farthest from the relative pronoun receives a weight of one and the weights are increased by one for each subsequent constituent
also easy and proper names the interesting hard part to be treated by the rule sequence processor
when we incorporated them into our information extraction 2our performance vis a vis other muc NUM participants placed us in the top third of participating systems
there are cases where different but related senses of a predicate have distinct feg possibilities
this paper describes a large scale system that performs morphological analysis and generation of on line arabic words represented in the standard orthography whether fully voweled partially voweled or unw weled
the mismatch between the concatenated root and pattern on the lexical side and the intersected stem on the lower side also creates an arabic system that is substantially larger than the other languages
NUM it had to be efficient and accurate successfully analyzing hundreds or thousands of words per second on commonly available workstations and higher end pcs
NUM the system had to be large and open ended with each root coded to restrict the patterns with which it can in fact co occur
this rule fst is then composed on the bottom of the lexi null string to unvoweled surface string c con fst yielding a single lexical transducer
NUM to facilitate lookup of words in printed and on line dictionaries and for pedagogical purposes the system had to return the root as an easily distinguished part of the analysis
it had to deal with real arabic surface orthography as represented on line in standards such as asmo NUM or the macintosh arabic code page iso8859 NUM
NUM various diacritical fcatures inscrted into the lexical strings to insurc proper analyses made this and other kimmo stylc systems awkward or in practical for generation
a typical transduction is shown in figure NUM where the final t is the surface realization of the third person feminine singular suffix at
an argument interpretation strategy has been developed which analyses arguments in a uniform fashion regardless of whether they precede or follow the verb
on the other hand the pronoun sic is regarded as the argument of a following infinitival clause i.e. marked as uninterpreted
clearly the semantic or informational relations among discourse entities can in principle be the determinant of a separate linguistic structure
this could occur when the expression of the segment purpose is more elaborate than simply stating what the hearer should do or believe
here we introduce a concept which is not part of the g s theory but which will be important to our discussion below
finally while g s recognize that informational structure is a cue to recognition of intentional structure the theory does not provide detail
this component determines which discourse entities will be most salient and thereby imposes constraints on available referents for pronouns and reduced definite nps
a second approach to constraining informational structure is to define it as a network of domain relations with type restrictions on the relata
mann and thompson claimed that for each two consecutive spans in a coherent discourse a single rst relation will be primary
the satellite b c is intended to facilitate this adoption by providing the hearer with a motivation for doing the suggested action
the intentional relations specify the ways in which a speaker can affect the hearer s adoption of a nucleus by including a satellite
all these operations can create new edges so operations to calculate new scores from old ones are attached to them
as in standard active chart parsing an edge is passive if e ncxt nil otherwise it is active
furthermore there are smaller channels connecting several modules which are used for the top down interactive disambiguation data flow
furthermore pathological examples have been found in which a single unification takes much longer than all other tasks combined
pieces of output have to be transferred to the next module as soon as possible
the probability of future unifications is made dependent from the result type of earlier unifications
trans x a l is the specilic transition penalty a model will give to two edges
we counted only those words as recognized which could be built into a valid parse from the beginning of the utterance
the resulting grammar is then stripped down to a pure type skeleton which is actually being used for syntactic parsing
the word accuracy results can not be compared to word accuracy as usually applied to an acoustic decoder in isolation
the results are summarised in table NUM
esl with locally low mcp1 values
null the first four figures give a global overview of the method
hand corrected attachment instances in order to trigger the acquisition process
for sake of brevity we do not re discuss the matter here
ssa to derive the noise prone set of observations
examples of the generalized esl s were presented in the previous section
put it in the hell set otherwise ei is a limbo esl
singleton cs as possible but is robust against persistently ambiguous phenomena
in the experiments the performance over a typical nlp task NUM i.e.
resolving syntactic ambiguities with lexico semantic patterns an analogy based approach simonetta montemagni l m o la sas via d i l orgl wj to NUM i isa NUM italy siln oq i c
conventional algorithms are based on word by word models which require bilingual data with hundreds of thousand sentences for training
in this paper we propose an algorithm for aligning words with their translation in a bilingual corpus
esp in the stated bank dips table NUM factor types with empirical probability
to illustrate how sensealign works consider the pair of sentences NUM e NUM c
the word sense information can provide a certain degree of generality which is lacking in most statistical procedures
such advantages do have their costs tot class based models may be over generalized and miss word specific rules
if such a case is true the algorithm presented here should be adaptable to other language pairs
NUM lookup the words in lexicon and c1lin to determine the classes consistent with the part of speech analyses
the first experiment is designed to demonstrate the effectiveness of an naive algorithm dictalign based on a bilingual dictionary
thanks are also due to keh yih su tbr many helpful comments on an early drall of this paper
third we note their position with respect to the text lines
character shape codes are defined differently by the selection of graphical features
table NUM accuracy of the ocr based
table NUM categorization accuracy for the word
the ocr speed was highly dependent on image quality
we were then left with NUM NUM distinct word entries
this would seem to be very ambiguous
figure NUM scanned image samples from nth
we start merging with the same output unigram constraint to reduce computation time
the time needed to derive a model is drastically reduced by abbreviating these initial merges
the constraints and the point of changing the constraint are chosen for pragmatic reasons
we ascribe the equivalence in the experiment to the particular size of the training corpus
the derived models and an additional previously test part consisting of NUM NUM words
we see that the perplexity curves approach very fast their counterparts from the previous experiment
pp and lp are defined such that higher perplexities log perplexities resp
since we do n t give any specific instructions to human subjects one of them tends to group consistently phrases as words because he was implicitly using semantics as his segmentation criterion
encode the set of possible semantic values for the preposition as a list or a tuple where each position in the tuple is going to correspond systematically to a particular type of np
what we have to do is to give the bv functor another argument whose values are those of the original feature in our example all the different verb stems of english
having a definition like that just given implies that it is just an accident that there are no massaplur nps since they are a linguistically valid combination of features according to the declaration
since we have only one entry for a verb then any semantic differences that are associated with variant subcategorizations will have to be built from the complement constituents in a completely compositional way
lcb subcategorizer subcat s selectors s x irest rcb lcb subcategorized cat x rcb
in order to pick the correct value for the position in question we associate with the verb a feature whose value is a list of the constructs called selectors that we used earlier
since this value is itself a list now consisting of a kleene category and a variable tail the resulting structure can again be combined with a following kleene category having the appropriate values
some i believe to be original others have been described elsewhere in the literature in some form although often in a way that makes it difficult for computational linguists to appreciate their significance
in general to identify the equivalent object for some virtual type we take the type description x and find the least object y such that the generalization of x and y equals y
traditionally agreement in nps is taken to be governed by at least three features person and number often combined in a feature agr and something like mass count
the diphones were placed in the middle of logatoms pronounced with a steady intonation
t igure NUM gives an example of the diphone am along with its spcctrtuu
a phone level description is obtained using the orthographic transcription and a pronunciation dictionary
so the transition between two phones is encapsulated and does not ueed to be calculated
preparation recording segmentation and pitch labeling of slovenian diphone inventories are described
finally pitch markers are to be determined for voiced parts of the signal
by applying the viterbi alignment procedure the training logatoms are automatically labeled using our monophone inventory
finally the global intonation contour of a phrase is determined sorin87
the second part of the diphone extraction procedure is to find the concatenation point of each phone
the nodes are provided with a dedicated hmm in order to acoustically represent the corresponding speech event
we will discuss the issue in some detail shortly
as a result q is the uniform distribution
recreate the empirical distribution using fewer features than before
first let us introduce some terminology and notation
let us back up to c again
the derivation fails and no dag is generated
for this application gibbs sampling is appropriate
i will take a somewhat different approach here
these properties of dutch separable verbs boost the likelihood of infinitival forms for the low frequency ranges but they also boost the likelihood of higher frequency finite plural forms such as zeggen since the separated finite plural form zeggen is identical to the finite plural of the underived verb zeggen say any separated finite forms will accrue to the frequency of the generally much more common derivational base
for the dutch example at hand this presupposition predicts that if we were to classify en forms according to their frequency and then for each frequency class thus defined plot the relative frequency of infinitives and finite plurals the regression line should have a slope of approximately zero
these verbs include unergative intransitives like walk for which one would not expect to find the adjectival usage given the above characterization but they also include clear transitives like move try and ask and unaccusative intransitives like appear which are not generally felicitous in this usage
in the two examples we have just considered the hapax based mle while being a better predictor of the a priori lexical probability for unseen cases than the overall mle does not actually yield a different prediction as to which function of a form is more likely
for example in many morphologically complex languages it is often the case that several slots in a paradigm are filled with the same form put in another way it is common to find that a particular morphological form is in fact ambiguous between several distinct functions
for example auxiliaries such as hebben have are among the most common verbs in dutch but they have rather different syntactic and hence morphological properties from other verbs these properties in turn contaminate the high frequency ranges and thus the overall mle
for each figure of merit we compared the performance of best first parsing using that figure of merit to exhaustive parsing
however a wider context exponentially increases the possible number of features which will exceed current limitations of computational resources
the interesting point here is that point in time seems to be more at
abstracted triple set this section describes a method for obtaining features of each synset
the surface words are merged into surface word lists as the following example shows
path length similarity methods are based on counting the links between nodes in a semantic network
there is a relatively big depth gap between synsets in the abstracted synset group
wordnet NUM NUM NUM corpus and brown corpus are utilized through the exi eriments
there are several methods to decide a set of synset groups using a hierarchical structure
f ven in the NUM level synset group there is a two depth gap
we selected NUM NUM NUM synset groups for candidates of feature description level
hindle rooth s lexical association strategy only uses one feature lexical aasociation to predict pp attachment but
possible values of this feature include one of the more frequent prepositions in the training set or the value other prep
if more than one explanatory variable wa s considered the variables were assumed to be independent
it is interesting to note that modeling variable interactions yields a higher perforlnanee gain than including additional explanatory variables
null the lexical association strategy does not perform well on the more difficult pattern with three possible attachment sites
if an english sentence e and its korean translation k are partitioned into a sequence of phrases p and t of all possible sequences s e k we can write p elk as in equation NUM where l and pk are phrase sequences and a p t denotes all possible alignments between pe and pk
in following algorithm ki a n denote ej v which has n th highest matching wdue with korean phrase ki among all possible matching korean phrase and u ki a n carry the weight tbr tile matching
he method we suggest integrates the l rocedures to solve the two critical robh ms deci l ing aligning units and aligning tim candidates of dilferent word orders and accoml lishes the atigu meat wi hout using any dictionary
the mignment of the pairs of structurally dissimilar languages such as korean and english rc quires different strategy to comp nsate the lack of structural information such word or ler and to handle the difli reu e of aligimwnt units
their result is promising with the demor stration of high accuracy o learning NUM ilingual lexicon between english aml hin se for fl equently use NUM words without t he consideration of word order
for the NUM narratives the probabilities that the observed distributions could have arisen by chance range from p NUM x NUM NUM to p NUM
as discussed in section NUM our initial aim was to explore basic issues about segmentation thus we used naive subjects on a highly unstructured task
to exemplify the computation we use the first two rows of table NUM giving a matrix of size i NUM x j NUM
a reliability measure indicates how reproducible a data set is by quantifying similarity across subjects in terms of the proportion of times that each response category occurs
partitioning cochran s q shows that the proportion of boundaries identified by at least three subjects was significant across all NUM narratives p NUM
meaning that the observed case of one agreement out of two potential agreements on boundaries in our example is not quite halfway between chance and perfect agreement
these observations indicate a need for further research regarding the interaction among variation in speaker style granularity of segmentation and richness of the linguistic input
in this section we present and evaluate a collection of algorithms that identify discourse segment boundaries where each relies on a different type of linguistic information
unfortunately the task of system integration has to obey some structural constraints which are mostly pragmatic in natnre
on a relatively unconstrained linear segmentation task the number of times different naive subjects identify the same segment boundaries in a given narrative transcript is extremely significant
cue phrase features o cue1 true false
both authors work was partially supported by darpa and
this problem is due not only to the frequencies of the source collocations or of the words involved but also to the frequencies of their official translations
NUM NUM developing new hypotheses by combining multiple knowledge sources
smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons failure rate of the translation algorithm with constant and increasing thresholds
such an approach would use lower values of the thresholds especially of tf for smaller corpora or less frequent collocations
this measure is also used to reduce the search space to a manageable size by filtering out partial translations that are not highly correlated with the source collocation
the two events are perfectly correlated in the positive direction each word group appears every time and only when the other appears in the corresponding sentence
the hapax based mle that we have proposed is not only observationally preferable to the overall mle it is also firmly grounded in probability theory
the smaller absolute perplexity scores they quote are a consequence of the much larger training data they use
consider the two sets of patterns below
in the right cohmm containing relational
strews a sl a tistica lly
NUM NUM NUM NUM NUM verification method inspection
all information extraction components are expected to be able to extract instances of these objects
i this material has been reviewed by the cia
nyu supported the tipster phase ii effort in the development of the tipster architecture with enhancements to both their detection and extraction systems and with experiments in the combined use of extraction and detection for document retrieval
dr ralph grishman steered the architecture working group and led its subset the contractors architecture working group cawg in the development of the architecture design and the implementation of that design in the two architecture demonstrations the first shown at the tipster phase ii NUM month workshop in november NUM and the second at the NUM month workshop in may NUM
enhancements to detection tipster detection research at nyu has been guided by dr tomek strzalkowski who began at nyu but moved to ge corporate research and development in january NUM
trec data will be used in these experiments
trec performance of this system which uses a nlp module to enhance the prise statistical core has steadily improved not only measured against itself but in relation to other participating systems
in the final six months of tipster phase ii the combined nyu system using the tipster architecture to enable integration of the detection and extraction systems will be used to experiment with ways of using extraction to improve detection
normalized one side effect from omitting the a and p to terms in the m only figure above is that inside probability alone tends to prefer shorter constituents to longer ones as the inside probability of a longer constituent involves the product of more probabilities
however so far the additional running time needed for the computation of o l terms has exceeded the time saved by processing fewer edges as is made clear in the cpu time statistics where these two models perform substantially worse than even the straight j3 figure
best first chart parsing is a variation of chart parsing which attempts to find the most likely parses first by adding constituents to the chart in order of the likelihood that they will appear in a correct parse rather than simply popping constituents off of a stack
the chart below presents the following measures for each figure of merit NUM e the percentage of edges or rule expansions in the exhaustive parse that have been used by the best first parse to get NUM of the probability mass
the rest of the paper is structured as follows
the members of the tuple will be categories each associated with a fixed position or a negative element here represented as no
thus the mother contains a record both of the distinguished daughter s store and what has been added to it by the subsidiary daughter
we will define a new feature that appears on every category that can be subcategorized for say scat whose values are tuples
this is analogous with the treatment described earlier in which this rule required a subcat list to be empty at this point
using the technique described earlier for encoding boolean combinations of feature values we could achieve the desired single entry for send very simply
note that since we need to be able to generalize over categories we are reverting to the basic untyped category notation
in rule NUM the kleene category is rewritten as an adj which will share all its features with the value of kcat
the encoding proceeds as follows for a feature f with values in lcb NUM NUM rcb lcb a b c rcb
of course where the ranges of feature values are finite hierarchies of non atomic types can always be expanded into hierarchies of atoms
indicating diffidence is better than incorrect recognition for categorization
also due to the salience of the condition part of the utterance the range role will be expressed first
NUM global sentence structure a sentence can be a hypotactic clause complex a paratactic clause complex or a simple clause
in some cases mutual dependencies exist between different linguistic phenomena i.e. also planning tasks that cause race conditions or deadlock
to implement transformation rules in the framework of system networks we define three new realization operators rewrite add and supplant
all such alternative pre spls are spawned and propagated onto the blackboard so that other modules can work on them all as parallel alternatives
null upon activation a module removes a pre spl from the blackboard refines and enriches it and replaces it on the blackboard
table NUM comparison between translation results on
table NUM ebl rules and ebl coverage
the last two tables summarize the results
the results are presented in table NUM
section NUM describes the constituent pruning method
the central problem is simple to state
the work reported in this paper was funded by telia research ab
after each level constituent pruning is used to eliminate unlikely constituents
in the social section and the entertainment ection there are many chinese personal names and organization names
the example george town is transformed into lcb lcb j4
the performance of identification of organization names ms not good enough especially for those organization names without keywords
the remaining colunms demonstrate the change of performance after the clues discussed m section NUM NUM are considered incrementally
the second type of errors results from the rare surnames which are not included in the surname table
based on our small training corpus the range of the application of the information should be narrowed down
keyword shows not only the possibility of an occurrence of an organization name but also its right boundary
when an informal style is used like in most english documents and in some recent italian german forms the personal distance between the interlocutors the citizen and the public institution is reduced using direct references to interlocutors by means of personal pronouns you we
in order to handle unknown words the dictionary function d returns a word hypothesis tagged as unknown word if the substring cpq is not registered in the dictionary such as i gf NUM in figure NUM
when we put equal importance on recall and precision the best value for the expected word frequency threshold is around NUM NUM where the recall were not extracted std matched when the frequency threshold was NUM NUM and reestimation was not carried out
c w o NUM those words that are not found in the dictionary and whose expected frequencies in the corpus are larger than the threshold o are extracted as the new words in the input texts
in other words they count an error only when the system segmentation is not acceptable to human judgemen while we count an error whenever tim system segmentation does not exactly match the corpus segmentation even if it is inconsistent
o p q wied c e o q q NUM the generalized forward algorithm starts from the beginning of the input sentence and proceeds character by character
since we can define the generalized backward algorithm in the same manner we can define the generalized forward backward algorithm to estimate the word n gram counts in japanese texts and to reestimate the word n gram probabilities in the segmentation model
in the generalized forward algorithm the forward probability o wi is the joint probability of the character sequence c and the event that the final word in the segmentation of cq0 is wi that spans the substring d
in the third analysis the system considers NUM vania as an unknown word because both NUM NUM and are registered in the dictionary
however the system segmented it into u data and NUM NUM NUM z communication both of which are found in the dictionary
when the japanese word segmenter is trained on a NUM NUM million word segmented corpus and tested on NUM sentences whose out of vocabulary rate is NUM NUM the accuracy of the new word extraction method is NUM NUM recall and NUM NUM precision
but what if the dependency in corpus NUM is not accidental
in other words this test can only give us a conservative lower bound for reliability
level on NUM narratives and for the remaining narrative p NUM
probabilities become more significant for higher levels of tj and the converse
the data contains the maximum number of disagreements yet NUM NUM
in our example do has a value of NUM NUM
NUM reflecting the fact that nonboundaries greatly outnumber boundaries
NUM and lowest on boundaries NUM max
the italicized parentheticals at each potential boundary site show the resulting boundary classification
significance increases exponentially as the number of subjects agreeing on a boundary increases
on the other hand there is wide performance variation around the mean
noun phrase features o coref coref coref na
what remains is the question of whether linguistic features correlate at all well with these segments
the contents are different from the second training corpus
the out of vocabulary rate of the test set is NUM NUM
f measure is used in information retrieval and is calculated by
the expected word unigram count of each word hypothesis in the sentence is
document sources shall be in machine readable form and may be from communication lines or from computer files
our subjects typically use a relatively gross level of speaker intention
table NUM shows the average performance of the cue word algorithm
there are a variety of document attributes that may be used by tipster processing functions
the remaining cases are judged as type NUM
figure NUM shows lhc processing flow
an example of a valency pattern
suppose the following code sequence is given to the algorithm
the string is first divided int asian and european parts
another problem is that it presupposes that the input document is correctly decoded
for documents on the www however these assumptions do not hold
table3 gives scores of tokens as regards to three languages
the problem is that different coding systems are applicable to the same code string
moreover there are a lot of non html documents on the www
this however does not handle multiple languages in one document
for example a document encoded with us ascii is not written in korean
the division procedure first tries to extract eastern asian characters from the given string
semi automatic development of glossaries the availability of a large glossary is the key for good quality translations
we define the lagrangian a p by
the complete x y pair is illustrated in figure NUM
first each word in e independently generates zero or more french words
we will denote by the set of all conditional probability distributions
we will have more to say about the stopping criterion in section NUM NUM
consider the common scenario which exemplifies the commercial transaction frame
null a realistic approach which also goes together with the above fundamental approach is to develop a more intelligent module that can estimate which coding system and language used for each on line document on the current www
the internet interface is written in html and cgi pet l which invokes the runtime image of the prolog code
in the tree a cooked lcb NUM NUM NUM rcb
japanese not only has typical voice conversions such as passivization but also appears to have similar phenomena that alter tile surface case markings such as the cases with causative construction
generating permutational subcategorization frame triggered by aux verbs we have generalized the notion of voice conversion for japanese auxiliary verbs and equivalents by abstracting NUM codes of case frame permutation
null term unconditionally matches and adds an extra case slot with a new deep case described within the bracket on the right term causer
discourse representing postpositions such as ha which had been erroneously treated as general subject mark various other cases and semantic roles including locative and time
any verb modifying np in a simple sentence in japanese can appear at any position or does not have to appear at all regardless of its surface case and deep case
it is a set of independent semantic heuristic rules that drops the autonomous reading of rareru and ahnost drops tile honorific reading of rareru
in these examples not the verb break but the semantics of the subject decides what deep case the subject should be allocated
the selectional one is the use of alternative deep cases and meets the needs of economical description of the lexicon and also the manageability of it
basic representation for japanese verb subcategorization frame empirical studies as we observed in the previous section have suggested that combining syntactic and semantic frames could lead to an optimum efficiency of lexicon descriptions
in this case the analyzer does not have to decide the deep case until when necessary at whatever point in the phases of mt NUM
note that although passonneau and litman are looking at the presence or absence of discourse segment boundaries measure NUM takes into account agreement that a prosodic phrase boundary is not a discourse segment boundary and therefore treats the problem as a two category distinction
which we will take as a representation of the logical form of the sentences john ran fast and john ran quickly
each is generated in its entirety though finally rejected because it fails to account for all of the semantic material
what concerns us here is a procedure for generating a sentence from a structure of this general kind
indeed more conventional formalisms with richly recursive syntax could be converted to this form on the fly
if the relative orders of the modifiers are unconstrained matters only get worse
the exponential factor in the computational complexity of our generation algorithm is apparent in an example like NUM
operationally the attraction is that the notations can be analyzed largely as free word order languages in the manner outlined above
this would be similar to allowing a given word to be covered by overlapping phrases in free word order parsing
the predicates that represent the semantics of a phrase will simply be the union of those representing the constituents
NUM consider each adjunct slot of a filled by a phrase adjp
NUM john sent the flowers to lucy betbre he did the chocolates
consider the contrast between 15a and 15b
a identify an antecedent sentence s for s
b john sang but not in new york at the concert
d construct a list of adjunct phrases for a as follows
vp ellipsis NUM john completed the paper before he expected to
this procedure will yield at least one appropriate reconstruction for the elided clause
figure NUM i uilding the state mmsitions given ttmt the thiha e
a4 aa aa NUM in hexidecimal which is incorrect
for example the super structural connection content specification in figure NUM names a kb accessor called find partonomic connection and the process participants description content specification names the make participants view accessor
some explanations were very terse e.g. those that occurred in glossaries whereas some were more verbose e.g. multipage explanations of physiological processes
the edps resulting from the analysis explain process and explain object can be used by an explanation planner to generate explanations about the processes and objects of physical systems
it is important to note that the authors and the domain experts entered into a contractual agreement with regard to representational structures in the biology knowledge base
on the face of it the second alternative involves less work and is preferable but designing explanation systems that can be easily modified is a nontrivial task
edps permit discourse knowledge engineers to specify the relative importance of each topic by assigning a qualitative value low medium or high to its centrality attribute
these expressions are instantiated at runtime by the explanation planner which then dispatches the knowledge base accessors named in the expressions to extract propositions from the knowledge base
when we normalized the grades by defining an a to be the mean of the biologists grades knight earned approximately NUM NUM a b
this tendency will certainly increase with the development of platform independent languages such as java
in sum there should be a balance between the information provided by the system and the user s competence
there have been quite a few attempts to introduce these new tools into the classroom
for example there are several well established mailing lists between japanese and foreign schools
what enabled this research group to build so quickly such a huge lexicon was the network
the users provided the developers with feedback by adding new lexical items to the original dictionary
the system searches then its database displaying those examples that exhibit this kind of relation
what role then can call system play in this new setting
the network as a motivational source for using a foreign language
the store and retrieve operations of the cache model casts discourse processing as a gradient phenomenon predicting that the contents of the cache will change gradually and that change requires processing effort
just as a cache can be used for processing the references and operations of a hierarchically structured program so can a cache be used to model attentional state when discourse intentions are hierarchically structured
the cache model consists of NUM basic mechanisms and architectural properties NUM assumptions about processing NUM specification of which mechanism is applied at which point
however there are certain cases where a pronoun alone is a good retrieval cue such as when only one referent of a particular gender or number has been discussed in the conversation
after describing one type of statistical model that is particularly well suited to modeling natural language data called a loglinear model we present einpirical evidence fi om a series of experiments on different ambiguity resolution tasks that show that the performance of the loglinear models outranks the performance of other models described in the literature that a ssume independence between the explanatory variables
for example in administrative forms in full sentences for entities anchored to the reader english and german typically use possessive noun phrases like your spouse whereas italian prefers simple definite forms e.g.
relations between text spans that will be signaled to enhance the coherence null alreadymentioned the history of mready mentioned entities stylepars the l arameters that define l he style of the output text focusstate the state of the attention of tile reader organized as tetailed in section NUM NUM
when we choose to realize a referring expression with an anaphora we fulfill a double fnnction we introduce some form of economy for the reference avoiding the repetition of a long linguistic expression and we enhance the coherence of the text since we signal meaning relations cohesive ties between portions of the discourse
therefore for the choice of the head of non anaphoric expressions the gist system adopts the strategy of using the most specific superconcept of the entity that has a meaningful lexical item associated e.g. the specific term decree absolute is used instead of the more basic term certificate
at present this is represented by a list of all the entities the reader is supposed to know e.g. the department for social security the anchored entities rt the rhetorical tree specifying how the selected content units will be organized in the final text and which are the semant ic
to tile tomain entities have to fulfill several properties null they must allow the non ambiguous identification of the entities NUM they should avoid redundancies that could hamper lluency they should contribute to the cohesion of the text by signaling semantic links between portions of text they should conform to the formmity and politeness requirements imposed to the output texts
to decide on a data structures are maintained that keep track of the evolving textual context discourse structure and focus history and record the seato cultural background of the reader 2in some genres the use of ambiguous references may be possible or desirable for exantple in jokes but in administrative genre clearness and unambiguity are the primary goals
in particular sentences we might find such words or phrases as john the customer etc instantiating the buyer or a chicken a new car etc instantiating the goods
the number and arrangement of semantic tags must be constrained lest the size and complexity of the tagging sets tagsets used for semantic annotation become unwieldy both for humans and computers
in both cases we were d le to exploit part of speech tagging and some existing word lists fbr person names and locations
consider the following examples NUM he lived in bray for five years
in the approach outlined here every sentence reports a set of events
i have only dealt with a small subset of the relevant phenomena here
similar analyses of other aspects and other aktionsarts are also easy to devise
no argument with his analysis of NUM and NUM
NUM describes a simple homogeneous state of af null fairs
different people use different terminology or this prese nts
as another example of robust processing consider an interaction later in the dialogue in which the user s response no is misheard as now now let s take the train from detroit to washington do s x albany instead of no let s take the train from detroit to washington via cincinnati
figure NUM shows the task completion time results and figure NUM gives the solution quality results each broken
all subjects were given a choice of whether to use speech or keyboard input to accomplish the final task
half of the subjects were asked to use speech first keyboard second speech third and keyboard fourth
the results from this experiment indicate that even if the language model of the sr can be modified then the post processor trained on the same new data can still significantly improve word recognition accuracy on a separate test set
had there been something on the stack e.g. a question of a plan the initial confirm might have been taken as an answer to the question or a confirm of the plan respectively
du sollst das fenster nicht bffnen
for each permutation the dispersion of each word type in that particular permutation was obtained
this approach requires multiple entries for verbs but has the advantage that it eliminates the need for different vp rules for each type of complement
datr is a language that allows the lexicon writer to define sets of partial functions from sequences of atoms to sequences of atoms
likewise words occurring after s in the source sentence will likely translate to words occurring after t in the target sentence
this is the price we have to pay if we want to produce programs that are of interest not only in the research labs but also into the arena of real world
in building call systems we will realize that there are ninny problems in the area of natural language processing that have been either overlooked or been posed in inadequate terms
surprising as it may be one of the biggest markets for products of computional linguistics cl has been largely overlooked the classroom
strangely enough in the past we had neither the right tools nor a decent theory see NUM NUM NUM yet people were optimistic and went ahead
actually there seems to be a communication problem and a mutual lack of interest concerning the work done in the neighbouring disciplines
yet if we really want to get a real tmderstanding of the fimctioning of natural languages how they are used how they are learned
all too often we look at language only from the point of view of the machine i.e. how can languages be processed by computers
this being so it is hardly surprising to see that the domain is never mentioned in textbook on artificial intelligence or psycholinguistics
while machine translation has attracted a considerable amount of research hence resources call a domain with a comparable potential has hardly ever received the attention it deserves
we do have very powerfid tools fast computers with well designed graphical interfaces browsers cd roms authoring languages and a whole set of quite promising theories yet we hesitate
d f ale rel resents the
NUM NUM dialogs were used for training and the NUM dialogs for testing
this work was supported in part by national science foundation grant iri NUM
the singular forms of nouns and the base forms of verbs are used
table NUM results of training and testing on the NUM
this advantage is obtained at the expense of storing all the patterns seen
notice that all of these results involve either word classes or partial patterns
table NUM results of training and testing on the NUM
table NUM results of training with the NUM dialogs and
because it was given a high verbosity specification and the inclusion conditions are satisfied both temporal information and process details are used to determine additional content
does the nominal attach null ment site include a definite determiner
it was smoothed with a loglinear model that includes all second order interactions
part of speech of the object of the pp
this is labeled split hindle rooth in figure NUM
this is labeled hindle rooth in figure NUM
does the word carry one of a list of frequently occurring prefixes
the initial set of features consisted of the following includes number
does the word carry one of a list of frequently occurring suffixes
the results for this model are lm eled NUM loglinear features
NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
indicates near miss and indicates incorrect
many of japanese translation of english technical terms are automatically detected
i saw the man with the telescope figure NUM initial sentence
NUM a d b c NUM NUM b d a c NUM
when NUM the original distributions are compatible when c NUM they are in complete conflict and the result is undefined
NUM a b c d NUM NUM h d s c NUM
using the pairwise probabilities from table NUM the results of the model as applied to the example are swe use these probabilities for ease of comparison
first in our task all coreference relationships among templates are modeled regardless of the referentiality of the phrases that led to their creation
we refer to the collection of such characteristics for a given example as its context x and the value denoting the output of the process as y
first as an absolute baseline we compared the model with the uniform distribution that is the distribution that assigns equal probability to each alternative
scribed with an indefinite phrase a definite phrase including pronouns or neither of these e.g. a bare non pronominal noun phrase
in general however a text can give rise to any number of distinct coreference sets each of which will be assigned its own probability distribution
since met was designed to tackle the muc NUM named entity task in foreign languages the government needed to acquire a corpus of articles rich in references to people places and organizations
this material has been reviewed by the cia
references to f NUM israel or u s for example were readily identified by the systems
percent NUM an arabic numeral the katakana foreign loan word script for percent e.g. NUM NUM and a kanji numeral
in addition the most prevalent entities properly identified as organization in these texts included groups offices labs etc that is noun phrases which could be proper nouns depending upon context
by conu ast for example the NUM article tipster phase i test corpus contained only NUM instances of person names or just NUM of all corpus tags NUM
however there is another problematic aspect called implicit spelling error that should be solved in morphological processing level
this same problem affects all of the possible ways of extending measure NUM to more than two coders
however we have yet to discuss the role of expert coders in such studies
this makes it applicable to the studies we have described and more besides
measure NUM is a different approach to measuring over multiple undifferentiated coders
computational linguistic and cognitive science work on discourse and dialogue relies on subjective judgments
research was judged according to whether or not the reader found the explanation plausible
this makes it impossible to interpret raw agreement figures using measure NUM
we suggest that this measure be adopted more widely within our own research community
we concur with krippendorff that what counts is how totally naive coders manage based on written instructions
we would argue that in subjective codings such as these there are no real experts
speed was particularly an issue for muc NUM because of the relatively short time frame NUM month for training
this is a prerequisite for generating the various restructurings such as the passive
in particular we have been participants in the message understanding conferences mucs since muc NUM
if not it attempted a parse covering the largest substring of the sentence which it could
our group at new york university has developed a number of information extraction systems over the past decade
on the other hand we also experienced first hand some of the shortcomings of the semantic pattern approach
we expect that users would enter patterns by example and would answer queries to create variants of the initial pattern
this will be difficult however if the scenario requires the addition of some grammatical construct albeit minor
they are useful because they are a representation of the text which is i susceptible to automatic objective manipulation
as we have seen for all but purely random populations o e NUM e tends to increase with frequency
how then to gain the benefits of clause level syntax within the context of a partial parsing system
the only question then is whether there is enough evidence to say that they are not with confidence
as in the lob brown comparison for very many words including most common words the null hypothesis was defeated
looking at the lob brown comparison we find that very many words including most very common words are marked
while a text may be coherent i in its me uing a corpus comprising multiple texts can scarcely be
th softwar is need d to cr ate tim store f informal ion
extraction of new words in texts expected word unigram counts expected word frequencies in the corpus equation NUM can be used as a measure of likelihood that a particular substring in the input texts is actually a word
by construction there are no auxiliary trees and no interior nodes in any initial tree
there is an exact one to one correspondence between derivations in g and derivations using the initial trees
however one way of doing this is particularly important in the context of this paper
the next three lemmas describe ways that tigs can be transformed without changing the trees produced
if the right hand side of r is empty t is given one child labeled with c
more promising are approaches in which grammatical constraints are integrated into processing systems that coordinate linguistic and non linguistic information as the linguistic input is processed
we have reviewed results establishing that with welldefined tasks eye movements can be used to observe under natural conditions the rapid mental processes that underlie spoken language comprehension
unsupervised learning of a rule based spanish part of speech tagger
tag set NUM NUM vs NUM NUM
table NUM jomplex verb tag set
it helps some candidates with lower score to pass the threshold but it can not avoid the incorrect candidates to pass the threshold thus the performance is dangling
consider an example shown below richard macs ll l those strings following english names have the same pronunciations
the following shows a counter example the first pronoun NUM 1f tie refers to the personal name
the research was supported in part by national science council taipei taiwan r o c under contract nsc83 NUM e002 NUM we are also thankful for the anonymous referees comments
of these gender is also a clue from character level title mutual information and punctuation marks come from sentence level tile paragraph information is recorded in cache
character syllable and frequency conditions are presented to treat transliterated personal names to deal with organization names keywords prefix word association and parts of speech are applied
the following shows three different types a single character like rcb and NUM
in other words it focuses on type NUM personal names
the major difference of pronunciation between chinese and english is syllables
language has a formally distinct component that of closed class forms whose specific role is to serve a structuring function for example schematizing the spatial relations between entities and the temporal relations between events
figure NUM error rate on unknown words
note that the average word length is the only parameter of the word length model
this choice can be made because the kb contains the process of replacement as a possible consequence of an implant being worn out loosened or having failed
as suggested by gibson and pearlmutter
within our research on interactive system architectures we developed a modular communication framework ice in cooperation with the university of hamburg
using full structure sharing in the syntactic chart which contains only packed edges we achieve a complexity of o n3
so it seems the right place to handle some of the global strategic control issues like l omain error trundling
modules have to colnmunicate with one another and their local behaviors have to be coordinated into a coherent global possibly optimal behavior
here lword is the last word of the edge to the left and rword is the first word spanned by the edge to the right
combined score is a linear combination of the outside components of an edge c which would be created by a and e in a combine operation
uses a new approach for the interaction of syntax and semantics and a revision of the interaction of the parser with a new decoder
this evaluation method is much harder than standard word accuracy but it appears to be a good approximation to rule accuracy
the fruitful interactions that occur here are between ran fast
figure NUM shows a simple example stsg
the rules that sanction a phrase e.g.
an esl has the following structure esl h mod p w where h is the head of the underlying phrasal structure and rood p w denotes the head modifier and w as the modifier head
given an esl the value of its corresponding mcpl is defined by the following def mutual conditioned plausibility the mutual conditioned plausibility mcp1 of a prepositional attachment esl w mod p n is
the threshold o is therefore a crucial parameter because it must establish the best trade off between precision of choices i.e. it must classify as hell truly noisy eslls and impact on noise compression i.e. it must remove as much noise as possible
in table NUM notice that precision and recall evaluate the ability of the system both at eliminating truly wrong esl s and accepting truly correct esl s since as remarked in section NUM our objective is noise compression rather than full syntactic disambiguation
furthermore the small size of the corpus is likely to anticipate 7unfike h r we did not use the t score as a decision criteria but forced the system to decide according to different values of the thresholds r for sake of readabifity of the comparison
the consequence is that statistically based lexical learning methods are faced not only with the problem of data sparseness events that are never or rarely encountered but also with the problem of systematic ambiguity events that occur always in the same sequence
the model has been partially implemented in a tool stk for detecting the textual clues related to metaphors and adding specific marks when found
description if the classical views of the metaphor overlook the textual clues in other domains especially those concerning explanation they have been wisely reintroduced
martin revealed at least that the heuristic of the ill formness of meaning representations issued from classical analysis is not sufficient at all to deal with all the possible metaphors
to demonstrate this a certain critical observation must be made
some appear in many languages but not in all e.g. the category of number
some differences can be illustrated with the english preposition in
broader cognitive science connections the overlapping systems model
typologically different languages regularly exhibit different forms of such partitioning
semantically open and closed class forms have complementary functions
four such imaging systems have to date been mapped out in some detail
open class forms are free to express virtually any conceptual content
the references of such closed class forms are thus largely magnitude neutral and shape neutral
but closed class representation of these categories in language yields only a few forms
once classification has been performed the resulting character shape codes are grouped by word boundary and used as word shape tokens for the downstream processing
unlike conventional methods that use optical character recognition ocr we convert document images into word shape tokens a shape based representation of words
in the photo copy process documents were degraded due to spreading toner low print contrast paper placement angle paper flaws and so on
NUM scanworx outputs a word with a reject mark when it is unable to recognize or is unsure in recognition e.g. meterii g
in this process it may also remove some non stop words because of the one to many mapping between word shape tokens and words
this can be explained as follows in the statistical categorization it is generally difficult to get good accuracy when the size of the test document is small
when we extend the word shape token processing to other applications it is important to note that the word shape token representation is only meaningful for the computer and hardly human friendly
the application may define this to be a form NUM form NUM or form NUM type of document
these objects are expected to be part of an expanding library that shall be augmented as applications are implemented
user and user group refer to the analyst s controlling and interacting with the application through the user
these may include such varied items as processing network configuration access security control information or document collection names
various algorithms for combining weights when there are multiple matches in one document may be selected
appendix c gives a list of the more common generic types that may be used by an application
this shall include the use of a test corpora although actual evaluation is not part of the architecture
the scope of marking of data may be at the document paragraph data item or object level
the goal of persistent knowledge is to further the use of common knowledge items as new applications are built
depending upon the specific task the user may direct the task to operate interactively or in the background mode
an analysis of the order in which the data was provided the timing of the distribution of the data and the reliability of that data suggest that the results reported here are really the floor of what the technology is currently capable of rather than the ceiling given that the systems are performing so close to human pelrformance it will be necessary to perform significance testing in the future
it is obvious that this is an unacceptable time delay for real time applications
this has decreased the execution time of the conversion by nearly NUM times
these phenomena are called hidden markov processes
a small percentage indicates a high similarity
this is a significant advantage in very large or unlimited dictionary applications
the ptgc system has been established and tested on various multilingual corpora
it could not produce more than one solution homophones
rentzepopoulos and kokkinakis phoneme to grapherne using hmm NUM NUM multiple output n best conversion algorithm
this feature significantly increases the number of states that have to be defined
this is only partially correct for natural languages including mute letters and diphthongs
in addition rather than creating all possible or some preferable parses we construct the best syntactic structure preserving local ambiguities
but in our approach morpheme candidates NUM are extracted either from the morpheme dictionary or using allocation rules based on character type
the word number of words which can be treated using rules like those given above is so great that the dictionary size is substantially reduced
however such analysis techniques using semantic informations are not yet adequate and seinetimes i ctmdly lead to adverse results NUM
tile at pli ation uscr s kakari ukc pairs corrections rest rts the selection qjp first selects the corrected kakari ukc
p fir s maxked by i in figure NUM and then re selects remaining kakari ukc pairs
NUM we have already developed an information extraction system for the management succession domain and that corresponds to one of the trec topics
having every reference to ibm count as a mention of ibm will result in the first document having a much higher score than the second
under the auspices of the tipster ii program we have developed in fastus a mature effective efficient robust information extraction system
these are the kinds of features that basic and complex phrase recognition in fastus can spot and the texts could thereby be rejected
the terrorist domain of muc NUM and muc NUM the joint venture domain of muc NUM and the management succession domain of muc NUM were of this character
but it does not apply if my path extends between two points rather close along the shore thus dividing the lake into two portions of very different size
thus in addition to the intentional linguistic structure discussed so far a discourse may simultaneously have an informational structure imposed by domain relations among the objects states and events being discussed
building on the correspondence between dominance and nuclearity a partial synthesis of g s and rst would be roughly the following a segment span arises because its speaker is attempting to achieve a communicative purpose
in g s the intentional structure consists of the set of the speaker s communicative intentions throughout the discourse and the relations of dominance and satisfaction precedence among these intentions
for our present purposes we wish to consider the possibility of a coreless segment only because such a segment would complicate the mapping between the two theories presented above
in turn ds1 is a segment span designed to achieve the purpose i1 by first manifesting i1 in the expression of the core nucleus b and then providing evidence in the embedded segment satellite c
because cores are a central aspect of the mapping between the two theories and because cores are not part of the g s proposal it is natural to ask whether a segment necessarily has a core
in this paper we have argued that two of the most important theories of discourse structure in computational linguistics g s and rst are not incompatible but in fact have considerable common ground
in this paper we focus on the most commonly occurring rst schema which consists of two text spans a nucleus and a satellite and a single rst relation that holds between them
the concept description dictionary describes the semantic binary relations such as agent implement and place between concepts that co occur in a sentence
however these electronic dictionaries are referred to and used by people unlike true electronic dictionaries machine tractable dictionaries which in the strict sense are intended for use in machine processing
the objective of the project will be the creation of a software that will allow the linguistic knowledge base to automatically expand by feeding the output of analyzed text into the knowledge base itself
the first version of edr dictionary v1 NUM and its revised version v1 NUM are already released and are now utilized at many sites for both academic and commercial purposes
the concept dictionary contains information on the NUM NUM concepts listed in the word dictionary and is divided according to information type into the headconcept dictionary the concept classification dictionary and the concept description dictionary
the technical terminology dictionary covers the field of infbrmation processing attd is split into four types of dictionaries of word bilingual concept classification and co occurrence
the new project will be funded by information technology promotion agency ipa of japan and will be carried out in conjunction with tokyo institute of technology and tokyo university
the linguistic data which the edr corpus contains has been obtained by collecting a large number of example sentences and analyzing them on morphoh gical syntactic attd semantic levels
as we mentioned in chapter NUM we have already released the first cd rom version of edr electronic dictionary v1 NUM in april NUM after the nine year r d project
the headword information of the bilingual dictionary is a subset of the word dictionary that is headword notations parts of speech concept identifiers headconcepts and concept explications
over the past several years there has been very significant and continuing progress in the development of accurate parsers for unconstrained text much of this breakthrough has depended crucially on the use of statistical methods which estimate model parameters from a tree bank of hand parsed sentences
we can conclude that even if current automatic generation systems could do better and we believe that this will soon be the case one of the two main reasons for using linguistic and template hybrid systems such as that developed by la redoute md gsi erli rather than using semi automatic systems is the improvement in quality the other being of course productivity
however the dittemnce between automatic and semi autonmtic letters is considerable NUM out of NUM semi automatic system NUM out of NUM automatic hybrid system NUM NUM out of NUM human written letters NUM NUM out of NUM differences ideal hunmn letters vs automatic letters NUM NUM automatic letters vs sa letters NUM NUM ideal human letters vs sa letters NUM NUM here all the difli renccs are considerable
this paper summarizes work that the invited talk by the first author mkt was based upon
given these results approaches to language comprehension that emphasize fully encapsulated processing modules are unlikely to prove fruitful
if these nouns are disambiguated with respect to the relevant attribute reliability can be increased as in the case of right side
the directional sense of right can not be construed as a general default since the correctness sense is far more common overall
most of those used in this paper are already present in some available machine readable dictionaries such as the longman dictionary of contemporary english
this issue is finessed to some extent in the projected indicator nouns and thus in our application of attributes based on them
simple broadly applicable semantic features characterize most of the indicator nouns whereas broadly applicable syntactic features characterize many of their contexts
he was a strong old man he had lived through forty five years of those wretched casseroles but she missed him already
the other five senses are represented by more than NUM sentences each and every one provides some coverage of the NUM sentence samples
most by far have n NUM sense distributions i.e. the pairs occur in only one of the target s senses
it was a winter world without details a world of shapes in an expanse ranging in color from light to dark gray
in some cases semantic features such as those discussed above can be used straightforwardly as sense indicators just as nouns were
i would also like to thank rens bod stan chen andrew kehler david magerman wheeler rural stuart shieber and khalil sima an for helpful discussions and comments on earlier drafts and the comments of the anonymous reviewers
not surprisingly other researchers attempted to duplicate these results but due to a lack of details of the parsing algorithm in his publications these other researchers were not able to confirm the results magerman lalferty personal communication
this research also shows the importance of testing on more than one small test set as well as the importance of not making cross corpus comparisons if a new corpus is required then previous algorithms should be duplicated for comparison
if our performance evaluation were based on the number of constituents correct using measures similar to the crossing brackets measure we would want the parse tree that was most likely to have the largest number of correct constituents
after NUM NUM merges the constraint is discarded and from then on all remaining states are allowed to merge
furthexmore we assume that simply by exchanging data and doing simple extensions in the control tow everything will balan e out nicely on the system scale which is enorlnously naiv
the obvious benefits of such an arrangement are twofold the system does not have to wait for a speaker to stop talking and top down constraints from higher level to lower level modules can be used easily
besides that an edge consists of a grammar rule e rule and f next a pointer to some element of the right hand side of lpsrule or nil
the parallel parser had to obey the tbllowing restrictions running on our local shared memory lnnltiprocessor sparcserverl000 with NUM processors parallelization should be controlled by inserting solaris NUM NUM
in intarc we use three classes of boundaries b0 no boundary b2 phrase boundary b3 sentence boundary and b9 real break
an analysis of profiling data shows that even the heavily optimized ug formalism causes between NUM and NUM of the compntational load in the serial c e
in a couple of tests we already achieved a reduction of edges of about NUM without change in recognition rate using a very simple trigram with only five word categories
lb achieve lr behavior the singular modules must fulfil the following requirements processing proceeds incrementally along the time axis left to right
an example of analyzing doume sub iect
an example of analyzing double suhject construction type NUM
NUM NUM problems in processing the japanese double
this is a question we are going to address in the following sections
rule NUM instructs us to label the i child b but we can not inasmuch as it is already labeled a
representing time is optional for most predicates
analyzing japanese double subject construction having an adjective predicate
the growth curve of the vocabulary
diagnostic functions for alice in wonderland
this section addresses two additional questions
m NUM NUM of the trouw data
words do not occur randomly in texts
this is not what we normally find
if right adjunction is possible at add a new rightmost child of labeled zi and mark it for substitution
the new roots and their children labeled yi and zi created in step NUM allow arbitrarily many simultaneous adjunctions at a node
the predicate rightaux px is true if and only if px is the root of an elementary right auxiliary tree
step NUM in this step we modify the set of initial trees further until every one is left anchored
further they can be easily optimized to require at most o n4 time when applied to a tig
the predicate leftaux px is true if and only if px is the root of an elementary left auxiliary tree
after that the first two a2 initial trees are converted into auxiliary trees as shown on the third line of figure NUM
the main part of the gnf procedure operates in three steps that are similar to steps NUM NUM NUM
the ak rooted initial trees must be left anchored or have leftmost nonempty frontier nodes labeled with aj where j k
step NUM of the procedure converts the cfg at the top of the figure to the tig shown on the second line
there is a main broad channel connecting all modules in bottom up direction i.e. from signal to interpretation
this section explores the role of the instance representation in case based learning of natural language
the first column of results shows the effect of memory limitations on the baseline representation
the lamp near the paintings of the houses that was damaged in the flood
the lamps near the painting of the houses that was damaged in the flood
the class information assigned to each case describes the location of the correct antecedent
this subject accessibility bias is an example of a more general focus of attention bias
in this framework a model consists of NUM an av grammar g whose purpose is to define a set of dags l g
like johnson s their system looks at the underlying and surface realizations of single segments
if errors are still found the pruning operation is undone
as discussed in section NUM this is computationally quite expensive
the decision is then read off from the leaf node reached
context phonological rules need access to variables in their context
subsequential transducers are essentially the most general type of deterministic transducers
finally his transducers explicitly include both accepting and non accepting states
when this occurs an attempt is made to merge the destination
in the alignment of figure NUM for example there are rifts at positions j NUM NUM NUM NUM in the french sentence
we begin as in the word translation problem with a training sample of english french sentence pairs e f randomly extracted from the hansard corpus
smlilarly the core of t he vs patterns m NUM above is NUM mpic ll atuiia s temperature s
we start with a n illustration of the pa rsing problem i o move on to a onsideratio
rise tenll era ture is to i e inl erprel ed t y sonsoi lcb
conventionally these two groups are often treated as one adverbial form aldlough many functional diiferences have been pointed out between them
first the discourse structure reference module reduces the number of possible syntactic structures using the level of conjunctive particles described in the previous section
the japanese language has some words that indicate or emphasize the fact that the word toki is being used as the if reading
when wa is attached to toki that is in the form of toki wa the supposition reading is enhanced
we are now in the process of developing a speech synthesis system with this method by defining the default pause length for each conjunction level
we have proposed a practical method for a global structure anmyzing algorithm of japanese long sentences with lexical information lexical discourse grammar ldg
wc extracted conjunctive particles and verbs auxiliary verbs and adjectives in adverbial form from the speech data and classified these words by the ldg conjunction level
compound and complex sentences it is difficult to grasp the proper structure of sentences having a large nmnber of possible dependency modifiermodifee relation structures
iience we refined the concept of ldg lmrticularly tile conjunction level of function words and explain the outline of the refilled ldg in tilts paper
in addition the japanese language does not have any parts of speech to clearly indicate either the beginning or end of a phrase or a clause
using our definition of rifts we can redefine a safe segmentation as one in which the segment boundaries are located only at rifts figure NUM illustrates an unsafe segmentation in which a segment boundary denoted by the ii symbol lies between a and mangd where there is no rift
fa x y lcb NUM otherwiseify pen antand weeks i i i i i j here fl NUM when april follows in and en is the translation of in f2 NUM when weeks is one of the three words following in and pendant is the translation
update the value of hi according to i i aai NUM go to step NUM if not all the i have converged the key step in the algorithm is step 2a the computation of the increments aai that solve NUM
the event consists of a context x containing the six words in e surrounding in and a future berger della pietra and della pietra a maximum entropy approach table NUM several actual training events for the maximum entropy translation model for in extracted from the transcribed proceedings of the canadian parliament
otherwise the model p ylx will begin to fit itself to quirks in the empirical data
segment alignments dramatically reduces the scale of the computation involved in generating a translation particularly for large sentences
that is we would like p to lie in the subset c of NUM defined by c lcb pep p fi p fi fori e lcb NUM NUM n rcb rcb NUM figure NUM provides a geometric interpretation of this setup
for each sentence pair we use the basic translation model to compute the viterbi alignment between the words in e and f using a we construct an x y training event as follows we let the context x be the pair of french nouns nounl nounr
a subsequent regularization process where alternative structures are reduced to a normal form helps to achieve a desired uniformity for example college junior will represent a college for juniors while junior college will represent a junior in a college
if the query is indeed made to resemble a typical relevant document then suddenly everything about this query becomes a valid search criterion words collocations phrases various an alternative to term only expansion is a full text expansion which we tried for the first time in trec NUM
the major rules we used are NUM a sequence of modifiers vbnlvbgljj followed by at least one noun such as cryonic suspend air traffic control system NUM proper noun s modifying a noun such as u s
for example it has helped us reduce the time and manual effort needed to develop and maintain our on line document indexing and classification schemes
this means that except for the semantic or conceptual resemblance which we can not model very well as yet much of the appearance of the query which we can model reasonably well may be and often is quite misleading for search purposes
table NUM gives the performance of cornell s now sabir inc smart system version NUM using advanced lnu ltu term weighting scheme and query expansion through automatic relevance feedback rel fbk on the same database and with the same queries
syntactic phrases for example appear reasonable indicators of content arguably better than proximity based phrases since they can adequately deal with word order changes and other structural variations e.g. college junior vs junior in college vs junior college
the following are the primary factors affecting this process NUM document relevancy scores from each stream NUM retrieval precision distribution estimates within ranks from various streams e.g. projected precision between ranks NUM and NUM etc NUM the overall effectiveness of each stream e.g.
the system consists of two components a preprocessing component for the automatic construction of key terms and the front end component for userguided graphic interface
as can be seen the key terms ranging from single word terms to four word terms are organized in a tree structure
special thanks are due to mathis h c chen lbr work of preprocessing the mill
candidate fable NUM lists the empirical probabilities of various factors
see table NUM lbr examples of sense definition and acquired rules
the algorithm is a greedy decision procedure for selecting preferred connections
the proposed algorithm can produce effective word alignment results with NUM read a pair of english chinese sentences
each sense is assigned codes fi om the two thesauri according to its definition in both languages
for NUM of the words the offset from correct alignment was at most NUM
in this section we present the experimental results of an implementation of sensealign and related algorithms
word phrases a spacing unit in korean may be decomposed into prop r
taking t and l as constants the order of complexity becomes o mn
the total training corpus tbr our experiments consists of NUM NUM english words and NUM NUM korean word phrases
the errors that is generated by morphological analysis and tagging cause many of the alignment errors
figure NUM ov a view of the l roposed aligmnent method
alignment methods tend to approach the problem differently according to the alignment units the methods adopt
on top of this strategy we introduce filtering heuristics which sort out unreliable flata using heuristics based on the statistical properties of the data
ler the length of a surface word list associated with an abstracted synset is called a surface support of the abstracted synset
the templates supply most of the format specific to a particular kind of spl plan thus reducing the need for the user to memorize spl plan syntax
distribution based methods are based on the idea that the similarity of words can be derived frorn the similarity of the contexts in which they occur
the chief problem with distribution based methods is that they only permit the formation of first order concepts definable directly in terms of the original text
the user may want to make use of existing plans either using them to guide the construction of new plans or modifying them to create new ones
splat draws on some aspects of modelling theory in helping the user build spl plans specifically by giving examples of spl plan templates and prefabricated spl plans
introduction segmentation of sentences into words is trivial in english because words are delimited by spaces
one of the hardest problems in handling unrestricted japanese text is the identification of unknown words
we need a criteria to select the most likely word hypothesis from among the overlapping candidates
we present a novel new word extraction method from japanese texts based on expected word frequencies
it also assigns a small probability to a character sequence that appears across a word boundary
we think of an unknown word as a word having a special part of speech u ik
the maximum entropy parser presented here achieves a parsing accuracy which exceeds the best previously published results and parses a test sentence in linear observed time with respect to the sentence length
new translatio examples are also produced by replacing the words of translation examples with those of translation rules
we confirmed that the accuracy rate of translation increased from NUM NUM to NUM NUM by applying genetic algorithms
however no analysis has been done with real document images which are usually degraded in quality
in the learning process new translation examples are automatically produced by crossover and mutation and various translation rules are extracted fl om the translation examples by inductive learning a translation exampie includes the source sentence and a translated sentence
note that they are distinguishable from alphabetical characters in the character shape code representation table NUM
null the top k bfs described above exploits the observed property that the individual steps of correct derivations tend to have high probabilities and thus avoids searching a large fraction of the search space
this means that the mapping ambiguity between word shape tokens and original words was acceptable for the categorization purpose
we then use the models pr a pehusk pbuild and pongee to define a function score which the search procedure uses to rank derivations of incomplete and complete parse trees
they convert images into text files using optical character recognition ocr to utilize existing nlp techniques
next we extracted nouns which are important content representing words for information retrieval from the lexicon
the comprehensive guidelines or templates for the contextual predicates of each tree building procedure are given in table NUM the templates use indices relative to the tree that is currently being modified
for example if the current tree is the 5th tree cons NUM looks at the constituent label head word and start join annotation of the 3rd tree in the forest
the ff idf method gave the poorest results while the smart
many of the high frequency words were soviet union locations or military related
calculating the probabilities of getting these outputs we get the following table
an additional weight is calculated which is necessary for the multinomial distribution
term set is calculated by the following equation NUM
in each document the header information up to the headline is removed
overall though the stemming works much more often than it fails
do nothing if the word is four characters or less
if the last three characters are ing remove them
as shown in section NUM NUM this is quite an advantageous form
however the ambiguity can be retained if duplicate rules are maintained
NUM NUM improving the efficiency of the tig parser
they can be represented individually as follows
step NUM finally consider the auxiliary trees created above
in contrast the gnf procedure converts them into right recursive rules
an example figure NUM illustrates the operation of the ltig procedure
such trees must have their first nonempty frontier node labeled with a1
adjunctions are converted into substitutions via the new non terminals yi and zi
hence although instances of agent and theme codelets were present in the coderack they were being overwhelmed by the ubiquitous answer codelets
the responsibility of this type of codelet is to explore the possibility of establishing a classifier relation between a classifier and an object name
this word has activated the classifier node in the conceptual network which in turn causes the posting of classifier codelets to the coderack
we are therefore motivated to study how our statistically emergent model can be used to simulate the interactions between word identification and sentence analysis
the problem also exists in continuous speech recognition research where correct interpretation of word boundaries in an utterance requires linguistic and nonlinguistic information
our system figures out where the word boundaries of a sentence are by determining how various constituents in a sentence can be meaningfully related
later the system progresses to identifying and constructing chunks in other words phrases and to establishing connections between chunks
examples NUM and NUM illustrate that although syntactic information has been incorporated in word segmentation there are still errors
it should be emphasized that if k NUM the parser does not commit to a single pos or chunk assignment for the input sentence before building constituent structure
the performance of this perfect scheme is then an upper bound on the performance of any reranking scheme that might be used to reorder the top n parses
any contextual predicate cp derived from table NUM which occurs NUM or more times in a training sample with a particular action a is used to construct a feature fj
for example the checkcons m n predicate of the maximum entropy parser may use two words such that neither is the intended head of the proposed consituent that the check procedure must judge
thus the problem solver is called to extend the path with the currently focused engine enginel from detroit to washington
to explore how well the system robustly handles spoken dialogue we designed an experiment to contrast speech input with keyboard input
act NUM is an incorrect analysis and results in the system generating a clarification question that the user ends up ignoring
significant effort was made in the system to detect and handle a wide range of corrections both in the grammar the discourse processing and the domain reasoning
three of the four selecting keyboard input had actually experienced better or similar performance using keyboard input during the first four tasks
while we could clearly improve the output of the system even in this small domain the current generator does not appear to drag the system s performance down
for example an actual contextual predicate based on the template cons NUM might be does cons NUM lcb np he rcb
starting from the left chunk assigns each word pos tag pair a chunk tag either start x join x or other
central to the skill of speaking is our ability to select words that appropriately express our intentions to retrieve their syntactic and phonological properties and to compute the ultimate articulatory shape of these words in the context of the utterance as a whole NUM
the accuracy of the simpler method levels off around at around NUM NUM while the loglinear model reaches an accuracy of NUM NUM
in both cruses the loglinear model with four features obtains higher accuracy than the method that assumes independence between the same four features
for example most pps with of attach to nouns and most pps with f o and by attach to verbs
evaluation of the combined syst em was performed on different configurations of the pos tagger on NUM NUM different samples containing NUM NUM words each
the characteristics of rare words that might show up ms unknown words differ fi om the characteristics of words in general
next exploratory data analysis was perfornled in order to determine relevant features and their values and to approximate which features interact
even for the sequential architecture implied by the case of isolated local control we have to solve a whole plethora of uew problems that corne along with interaetivity a module that comes close to possessing the inte grated view ot a centralized blackboard control the dialogue module
the proposed method makes use of two kinds of word correspondences in aligning bilingual texts
however the method can not be applied to general translations in structurally different languages
the proposed method involves iterative alignment which simultaneously uses both statistics and a bilingual dictionary
the texts are then partof speech pos tagged and separated into original form words z
for example t scores above NUM NUM are significant at the p NUM NUM confidence level
we would like to thank nikkei science co for permitting us to use the data
in japanese english translations the method does not capture enough word correspondences to permit alignment
they show that statistics and on line dictionaries are complementary in terms of bilingual text alignment
dictionary performs the iteration of the algorithm by using corresponding words of the bilingual dictionary
the parameter setting used for each method was the optimum as determined by empirical tests
the conversion to choresky normal form makes a lot of other changes as well which are largely counterproductive if one wants to construct a left anchored grammar
however a dtla can not handle structured attributes like nouns which are classified under a thesaurus
9we basically disambiguated the word senses manually and there were not a disastrously large number of such cases
finally we report an application of our new algorithm to verbal case frame acquisition and show its effectiveness
the most popular touchstone for the dtla in this community is the verbal case frame or the translation rules
further several promising recent approaches to information extraction rely on little more than finite state machines to perform the entire extraction analysis appelt et al
the sequenced phrase finding rules then grow the boundaries of phrases or set their label according to a repertory of simple lexical and contextual tests
that is it must partially align some kind of candidate phrase for every phrase that is actually present in the input
this rule effectively patches tile errors caused by its predecessor in the rule sequence and simultaneously eliminates both a recall and a precision error
as our tcclmiques mature this validates not only ottr particular approach io phrase finding but the whole field of language processing through rule sequences
after preliminary experiments we decided to eliminate c from the delimiters because if it is used a pattern such ts equal to NUM the length of b is limited to less than or equ d to NUM if the length of a is the length of b is limited to less than or equal to NUM additional explanation will be given later in this subsection
the pattern matchers gather evidence such as as lcb u oc big change j university NUM large j ldi lcb large retail shop law etc as given in NUM NUM
pm q o pm qilqi NUM pm o ilqi pm qelqi i NUM
however the n gram models make the implicit assumption that all words belonging to the same category have a similar distribution in a corpus
in such a case starting with bigrams does not lead to an optimal model and a trigram model must be used
we use dialogues that were recorded in NUM and NUM and which are now available from the bavarian archive for speech NUM NUM
NUM usage of the viterbi path best path only instead of summing up all paths to determine the corpus probability
models are derived by starting with a large and specific model and by successively combining states to build smaller and more general models
it can never decrease the maximum likelihood model assigns the highest probability to the training part and thus the lowest perplexity
between NUM NUM and NUM NUM merges the log perplexity roughly linearly increases with the number of merges and it explodes afterwards
im provements the derivation of the optimal model took about a week although the size of the training part was relatively small
so one could in fact at least in the shown case start with the bigram model without loosing anything
once the mwls listed in the dictionary have been manually changed into their canonical base form including possible lexical variants and modifiers and indicating morphologically flexible components and the scope of alternative components the idaaex res describing all possible contexts in which the mwls can occur can be produced automatically
using this word order macro the mwls den schsnen schein wahren and die ohren spitzen to prick up one s ears lit the ears sharpen can now both be expressed very simply according to the same schema as
maurel NUM roche NUM and silberztein NUM idakex res provide a convenient way to mix inflected and uninflected word forms morphological features and complete word classes thus greatly relieving lexicographers from the burden of explicitly listing all the possible forms
null for instance in g den schgnen schein wahren to keep up appearances lit the nice pretence preserve the presence or absence of the adjective does not change the meaning at all whereas in g das handtueh werfen to throw in the towel lit the towel throw any modification would evoke the literal meaning
for example in the definition of german verbs we exclude contracted forms of verbs and the pronoun es such as geht s it goes we simply state any expression with the morphological feature v followed by anything that must not contain a letter i.e.
firom machine reada i le lictiouaries ml lcb i s bolj ta xo lomie in orma tion
the architecture shall also support tagging of text that caused a particular slot to be filled
compiled queries shall indicate and retrieve the document sub sets that are of interest to the user
the architecture allows the specification of criteria for correct filling of each template slot and object
some items will be filled grow or be augmented as applications are implemented under the architecture
this shall also allow vendors to develop alternative components that also meet the specifications of the icd
a form NUM document is the input to tipster and form NUM is the internal tipster form
note NUM not all requirements supported by the architecture will necessarily be included in an application
the specific values for each type are placed in template slots to fill the template
it must be noted that in all experiments the testing material was not included in the training of the model although it may belong to the same domain
see the muc6 conference proceedings for official scores
fourth our recognition of organizational noun phrases needs improvement
this is difficult for an automatic extraction system to decipher
the answer key for this scenario contains two organization entities
for efficiency it is necessary to estimate these distributions with relatively simple models by making independence assumptions
while the corrected transcriptions are not perfect they are typically a better approximation of the actual utterance
similarly hanzi sharing the ghost radical tend to denote spirits and demons such as gui3 ghost itself mo2 demon and yan3 nightmare
in this paper we present a stochastic finite state model for segmenting chinese text into words both words found in a static lexicon as well as words derived via the above mentioned productive processes
this class based model gives reasonable results for six radical classes table NUM gives the estimated cost for an unseen hanzi in the class occurring as the second hanzi in a double given name
it is formally straightforward to extend the grammar to include these names though it does increase the likelihood of overgeneration and we are unaware of any working systems that incorporate this type of name
gan s solution depends upon a fairly sophisticated language model that attempts to find valid syntactic semantic and lexical relations between objects of various linguistic types hanzi words phrases
a totally non stochastic rule based system such as wang li and chang s will generally succeed in such cases but of course runs the risk of overgeneration wherever the single hanzi word is really intended
if one is interested in translation one would probably want to consider show up as a single dictionary word since its semantic interpretation is not trivially derivable from the meanings of show and up
in various asian languages including chinese on the other hand whitespace is never used to delimit words so one must resort to lexical information to reconstruct the word boundary information
many thanks to alex rudnicky ronald rosenfeld and sunil issar at cmu for providing the sphinx ii system and related tools
this is a rather important source of errors in name identification and it is not really possible to objectively evaluate a name recognition system without considering the main lexicon with which it is used
without using the same test corpus direct comparison is obviously difficult fortunately chang et al include a list of about NUM sentence fragments that exemplify various categories of performance for their system
overall problems were solved using speech in NUM of the time needed to solve them using the keyboard
while our evaluation appears similar to hci experiments on whether speech or keyboard is a more effective interface in general cf
for instance chinese law articles have many terms beginning with the word deg i.e.
finally we discuss the practical situations in chinese string searching
dividing the alphabet into equivalent classes of identical transition vahms
since the space time complexity in constructing the automaton depends on the size of the alphabet i.e.
for example the pattern string p i.e.
this is important because it is added to the total time complexity of searching
the mapping is implemented as an array f
table NUM the values of the patterns indexed byj
otherwise recurrence patterns in p occur only incidentally e.g.
these hand engineering advantages are also conferred upon learning programs that attcmpt to acquire these rules atttomatica ly
we handle this through a heuristic scoring ftmction that estimates the wtluc of moving a boundary in such cases
we present a novel approach to parsing phrase grammars based on eric brill s notion of rule sequences
in our approach phrases are initially built around word sequences that meet certain lexical or part of speech criteria
other rule patterns arc handled with constructions of a similar flavor space considerations preclude their description hcre
by manipulating the paraaneter one is able to control for the relative importance of recall or precision
except for the absolute highest performer all these top tercile systems were statistically not distinguishable from each other
beyond this the only real complexity arises with phrase finding tasks that require one to maintain a temporary lexicon
in this paper we maka the case that rule sequences succeed at this task through their simplicity and speed
learning rule sequences automatically our experience with writing rule sequences by lt md in this approach has been very positive
by applying these two tests and removing all closed class words from the list we greatly reduce the number of words that must be considered
we accomplish it by constructing a mapping from the uniform distribution to each allowed value for the n s using combinatorial methods
lcb p rcb patient the baby recovered
the new path is returned and presented to the user
the following empty tell act is uninterpretable and hence ignored
take the last train in go from albany to is
now let s take the train from montreal to lexington
the trains NUM system is organized as shown in figure NUM
simply classifying such utterances as sentences would miss the point
and task based evaluation seems one of the most promising candidates
the prioritized rules are used in turn until an acceptable result is obtained
we use a viterbi beam search to find the s that maximizes the expression
the speech synthesizer is a commercial product the truetalk system from entropies
hanshin publishing co seoul south korea
the different levels that we have defined correspond to levels of difficulty lying in the linguistic forms proposed broader and broader coverage and finer and finer sentences decomposition in the guided mode and in parallel to an extension of the cognitive possibilities in particular in space locating
in the guided mode the software is operated by means of a series of graphics selections on the screen
the guided mode allows to begin rehabilitation as soon as possible even for very young or seriously disabled children
sentence composition using partial synthesis enables the system to offer the possibility to generate sentences in a guided mode
a lexicon and a set of semantic composition rules have been developed and are used in the same way
e change le cargo nolr avec le fond figure NUM example of a logic game in erel
put the gray triangle at the left of the pawn which is situated under the pawn which is
the idea is to encourage the children to use language for doing tasks composing a logical sequence of actions
how different streams perform relative to one another ll pt avg
NUM gives some additional data on the effectiveness of stream merging
the above clearly simplistic technique has produced some interesting results
a long query is obtained through our full text expansion method manual or automatic
an automatic run means that there was no human intervention in the process at any time
we then describe our first attempt at automated expansion and discuss the results from both
these results were sufficiently encouraging to motivate us to investigate ways of performing such expansions automatically
we would also like to thank ralph weischedel for providing the bbn s part of speech tagger
this also gives the pair based representation sufficient flexibility to effectively capture content elements even in complex expressions
NUM eliminate stopwords original text words minus certain no content words are used to index documents
it is obvious that case a contradicts with case b
section NUM the international section has better precision and recall than other files
if a transitive verb precedes a possible keyword then no organization name is found
the postulation is the words to compose a name part usually have strong relationships
if the syllable does not belong to the training corpus the character is deleted
they play the similar role of the surnames in the identification of chinese personal names
in our model total NUM characters are trained from our transliterated personal name corpus
after combining the model and the heuristic accuracy improves to NUM which is the same as ours
in general priority is given to the solution containing more subtrees which directly reflect the observed evidence
he stays out of the street lcb c and rcb lcb fuh rcb ifl catch him i call him and he comes back
ex NUM b oh editing terms are usually restricted to occur between the restart and the repair and have some semantic content e.g.
as long as errors such as this are in the minority we can evaluate the method and then go back and refine it if it proves useful
in contrast with shriberg s work no internal structure of the restart is included e.g. which words are repeated substituted or deleted
asking what my opinion about whether it s possible laughter to have honesty in government suspect that it is possible
note that this is not necessarily because the algorithm is dividing the sentences into two equal portions as we can see in the example above
the segment boundaries provide an additional source of information to the language model and hence it appears intuitively correct to use linguistic segmentations for training language models
we trained three trigram language models two using acoustic segmentations and linguistic segmentations respectively and a third model trained on data with no segment boundaries
as explained in the previous section one of the important annotations of the switchboard corpus involved the issue of sentence boundaries or segment boundaries
let us suppose that a developer has been given the task of building a tipster compliant text processing application that accomplishes certain tasks
as the transformation range for prosodic speech parameters needed for synthesising naturally sounding speech is large the recording should thus be carefully controlled to achieve medium pitch and duration values
although new sources format changes and erroneous or ill behaved data can cause processing errors adept identifies these problem occurrences generating diagnostics that describe the nature of the problem such as where it occurred and why it did not match
thus automatic procedures can speed up the segmentation process but they are not likely to suppress manual corrections at least for obtaining highest synthesis quality with a given corpus
a list of slovenian sampa symbols together with their audio samples is available on the www on the address http 1uz fer uni lj si english sqel sampa eng htlnl
therelbre in order to be able to synthesise speech in a variety of different voices we decided to use procedures for automatic segmentation and pitch marking of spoken logatoms
given that speech recognition errors are inevitable robust parsing techniques are essential
to test if we found a model that predicts new data better than the bigram model and to be sure that we did not find a model that is simply very specialized to the test part we use a new previously unseen part of the verbmobil corpus
of all possible merges generally there are k k NUM NUM possible merges with k the number of states exclusive start and end state which are not allowed to merge we take the merge that results in the minimal change of the probability
NUM and NUM respectively of the available training data
notice also that because of their different classification objectives learning and testing use different decision thresholds
for lexical tagging tasks for example the class information is the syntactic or semantic category associated with the current word for structural attachment decisions the class information indicates the position of the preferred attachment point
in theory both statistical and machine learning techniques can significantly reduce the knowledge engineering effort for building large scale nlp systems they offer an automatic means for acquiring robust heuristics for a host of lexical and structural disambiguation tasks
the disappointing perforsentence null baseline representation right to left labeling it was the hardliners in congress who
thus far we have incorporated three such biases into the feature set selection algorithm NUM a recency bias NUM a restricted memory bias and NUM a subject accessibility bias
we incorporate the subject accessibility bias into the baseline representation by increasing the weight associated with the constituent attribute that represents the subject of the clause preceding the relative pronoun whenever that feature is part of the normalized feature set
more importantly the linguistic bias cbl approach to natural language learning offers a mechanism for explicitly combining the frequency information available from corpus based techniques with linguistic bias information employed in traditional linguistic and knowledge based approaches to natural language processing
he found that the most recent noun phrase np3 was initially preferred as the antecedent and that recognizing antecedents in the np2 and np1 positions were significantly harder than recognizing the most recent noun phrase as the antecedent
to do this the algorithm keeps track of every attribute that occurs in the training instances and augments the si the man from oklahoma who
in our current implementation the learning algorithm rather than the parser is also responsible for interpreting any conjunctions and appositives that are part of the antecedent as shown in sentences NUM and NUM of figure NUM
a relatively small corpus was used because the domain specific semantic class tags and the tags for another lexical tagging task not described here were not available as part of any existing annotated corpus and had to be provided manually
it involves the following sub tasks NUM map thematic structure onto syntactic roles e.g. agent process possessed and pcssessor onto subjec t verb group direc t objec t and indirect object respectively in NUM
NUM provide defaults for syntactic features e.g. definite for the nps of NUM
it has been successfully reused in eight generators that have little in common in terms of architecture
a lexical process is a shallower and less semantic form of input where the subcategorization constraints and the mapping from the thematic roles to the oblique roles NUM are already specified instead of being automatically computed by the grammar as is the case for general process types
this interpreter has two components NUM the functional unifier that fleshes out the input skeletal tree with syntactic features from the grammar and NUM the linearizer that inflects each word at the bottom of the fleshed out tree and print them out following the linear precedence constraints indicated in the tree
NUM select closed class words e.g. she the and to in NUM NUM provide linear precedence constraints among syntactic constituents e.g. subject verb group indirect object direct object once the default active voice has been chosen for NUM
NUM prevent over generation e.g. fail when adding the same dative move yes feature to an input similar to i1 except that the possessed role is filled by cat pers pro for personal pronoun to avoid the generation of NUM she hands the editor it
after performing the final task the subject completed a questionnaire
sections NUM NUM and NUM describe the tree building procedures the maximum entropy models and the search heuristic respectively
after build control passes to check which finds the most recently proposed constituent and decides if it is complete
oviatt and cohen NUM this comparison was not our goal
in particular we demonstrated that natural language processing can now be done on a fairly large scale and that its speed and robustness have improved to the point where it can be applied to real ir problems
the information base may directly be distributed through ftp server or indirectly accessed by the language tools on the higher layer of the http server configuration
i have therefore used a randomization test to ascertain which words are significantly underdispersed
all lines including the nonparametric regression line are interpretable as in figure NUM results of a tenfold cross validation are shown in table NUM clearly in this case the magnitude of the difference between the overall mle and the hapax based mle is smaller than in the previous example indeed in cross validations NUM NUM and NUM the overall mle is superior
unsupervised segmentation i.e. segmentation on acoustic principles only often results in segments and sub segments boundaries being misplaced or just missing while undefined ones appear
markers NUM and r are set at the pitch periods the left part of the do hone and of the right part respectively
iable NUM average l honeme duration cot fidence interval and xtandard deviation of the population br manual and automatic segmentation
this is exactly the distribution employed by the basic translation model
a graphical representation of this approximation is provided in figure NUM
algorithm NUM is not a practical method for incremental feature selection
now at renaissance technologies stony brook ny
then px is the solution of the primal problem that is p
the optimal values of these parameters are obtained by maximizing the likelihood of the training data
to split input sentences in order to speed the translation process
we have thus taken our first step toward context sensitive translation modeling
of course there are an infinite number of models p for which this identity holds
with this definition in hand we are ready to present the principle of maximum entropy
the exception is in the case where the silence phone is part of the required pair there the diphone was word initial or word final
we participated in both main evaluation categories category a ad hoc and routing working with approx
this creates an effect of locality somewhat similar to that achieved by passage level retrieval
in each category NUM official runs were performed with different set up of system s parameters
we would like to thank donna harman of nist for making her prise system available to us
therefore a query containing the former may be expected to retrieve documents containing the latter
at this time we have no data how these results compare to those obtained via parsing
automatic routing with NUM queries from NUM NUM range NUM
as a result of the tipster application reviews described in section NUM NUM below a summary matrix will be available as shown in appendix a figure a NUM below
one of the goals of the architecture is to provide a catalog of previously developed tipster modules which may be adapted for use in other applications thus saving time in developing that module
this is because while output symbols can be pushed back the state merging process can not push the symbols forward if the alignment has caused them to be placed too far down the tree
if the length NUM of the arc s output string is greater than n it is necessary to push back the last i n symbols onto arcs further down the tree
but explicitly modifying the ostia algorithm with these biases allowed it to learn more compact accurate and general transducers and our implementation successfully learns a number of rules from english and german
NUM rather our model is intended to suggest the kind of biases that may be added to empiricist induction models to build a learning model for phonological rules that is cognitively and computationally plausible
ostia s default behavior is to emit the remainder of the output string for a transduction as soon as enough input symbols have been seen to uniquely identify the input string in the training set
in addition giving the model more training data does not seem to help it induce a smaller or better model the best transducer was the one with the smallest number of training samples
the variable s index is the relative index of the corresponding input segment as calculated by the alignment the features specified by the variable are only those that have changed from the input segment
while our decision tree augmentation adds these concepts to the algorithm it only does so only after the initial transducer has been induced and so can not help in building the initial transducer
upon receipt and disposition of a problem the erb will analyze it for type of problem priority and verification of the proposed solution
military standard configuration management mil std NUM NUM december NUM configuration management manual software engineering guideline may NUM prc inc
in the application discussed by della pietra della pietra and lafferty representing english orthographic constraints gibbs sampling can be used to estimate the needed expectations
there is indeed a dependency in the corpus in figure NUM in the trees where there are two a s the a s always rewrite the same way
however i will show that a more general sampling method the metropolis hastings algorithm can be used to compute the maximum likelihood estimate of the parameters of av grammars
when a new feature is added to the field the best value for its initial weight is chosen but the weights for the old features are held constant
if each dag in an infinite set of dags is assigned a constant nonzero probability e then the total probability is infinite no matter how small e is
at each iteration we select a new feature f by considering all atomic features and all complex features that can be constructed from features already in the field
the case we consider is a special case in which the proposal probability is independent of x the proposal probability g x y is in our notation p y
hence there is no way to increase the weight for trees xl and x2 improving their fit to without simultaneously increasing the weight for xs and x6 making their fit to worse
the tacad may be used by vendors to facilitate teaming with other vendors or insertions of new capability into existing tipster systems
we have contrasted two types of statistical language models a model that derives a probability distribution over the response variable that is properly conditioned on the combination of the explanatory variable and a simpler model that treats the explanatory variables as independent and therefore models the response variable simply a s the addition of the individual main effects of the explanatory variables
the relevance of each clue can be used to help disambiguating multiple meaning representation when it occurs
we would argue that the two previous views already considered metaphor as a kind of anomaly
the syntactic structures may also give information about the source and the target of the metaphor
thus describing the syntactic regularities surrounding a lexical marker improves its relevance as a marker
there are two central decision making points that affect the outcome of the query expansion process following the above guidelines
NUM expanded queries are sent through all text processing steps necessary to run the queries against multiple stream indexes
this includes both content as well as some other aspects such as composition style language type etc
in this paper we discuss both manual and automatic procedures for query expansion within a new stream based information retrieval model
the topic field provides a single concept of interest to the searcher it was not permitted in the short queries
our main effort in the immediate future will be to explore ways to achieve at least partial automation of this process
software employing nlp methods may track communication between distanced learners keep logs of errors and suggest areas of further study
although emphasizing the importance of bringing language learners together network based environments will continue to contain thesauri and lexicons such as glen d
emphasis on cognitive processes in language learning inspired the building of small environments in which students could learn through exploration in ways similar to those proposed for mathematics by logo advocates
housed in stanford s sybase this polish english learners dictionary offers lookup of word flwms returning sound and graphics and offering access to a discourse database
just the reverse contemporary network based multimedia environments can increase the amount of comprehensible input both in and out of the classroom supporting language acquisition from diversified input
glen d is consulted by skryba whenever specific information e.g. part of speech is needed to facilitate blanking or interpretation
in addition to investigating human computer interactions call employing cl methods will focus on the mediational use of computers in which identification access and sharing of resources and human to human contact play a significant role
new solutions to issues surrounding language and its acquisition are more likely to emerge from exchange of ideas between disciplines rather than from convictions entrenched within a single field
as for the second word however one has to be careful because a word with length NUM is very likely to appear through an over segmentation error
the word s local frequency is so low that we know it would be impossible for the dice coefficient between it and the source collocation to be higher than the threshold td
the list also contains the local frequency of these words i.e. frequency within this subset of the french corpus and is sorted by this frequency in decreasing order
it is also necessary to know the expressions used in the sublanguage since we have seen that idiomatic phrases often have different translations in a restricted sublanguage than in general usage
while the french translation en prenant des mesures does use the french for take the object is the translation of a word that does not appear in the source measures
it then attempts to find all words that can be part of the translation of the collocation producing all words that are highly correlated with the source collocation as a whole
since each word group appears two times with the other group and three times by itself we would normally consider the source and target groups somewhat similar but not strongly related
note however that the change is in the opposite direction from the appropriate one that is the new variables are deemed far less similar than the old ones
we describe a program named champollion which given a pair of parallel corpora in two different languages and a list of collocations in one of them automatically produces their translations
NUM NUM matches should be completely ignored otherwise they would dominate the similarity measure given the overall relatively low frequency of any particular word or word group in our corpus
the two main characteristics of the formalism are underspeeification and monotonic contextual resolution
consider the word boys in la above
contain points where the context is inaccurately considered identical
the words are automatically classified using our top down algorithm
in the following we follow hughes method
its m t t value is calculated
we carried out another experiment to support this claim
improving statistical language model performance with automatically generated word hierarchies
this mapping is constructed by making random word to tag assignments
first eighty classes but worse at lower levels
the summed score is then normalized as a percentage
this version of the algorithm raised the number of correctly transcribed phonemes to NUM NUM for most of the languages the system was tested on
an unsigned long integer has a NUM bit dynamic range which results in a maximum value of NUM NUM NUM NUM NUM NUM
in conclusion the proposed method has the following advantages it is language independent making it adaptable it to any language with little effort
another advantage of this system is that it can work in any language in which the pronunciation of the words is statistically dependent only on their spelling
if the n best state sequences are needed then the algorithm must be modified to keep the n best state sequences from ql through qt
architecture and software support for rapid development of extensible machine translation functionalities
figure NUM the lexicai editor with the spanish dictionary
translator can of course manually add any phrase to the glossary
the gbmt engine is the core component of the workstation machine translation function
the arabic english glossary for example was built in six man months
funded by dod maryland procurement office mda904 NUM r NUM a0001
heterogenecms linguistic resources are parsed and mapped to a common multilingual representation
corpus based utilities to automatize the acquisition of bilingual glossaries
u s department of defense mtvanni afterlife ncsc mil
lections document NUM NUM extraction and detection support
tipster compliant extraction modules can be easily integrated with the tuit gui
appropriate for other projects that require multilingual text display and edit capabilities
this space induced by the lref is characterized by most of the authors in the literature by a two part spatial system consisting in the inside and the outside of the lref
this has allowed us to classify col verbs into NUM classes on the basis of which zones the mobile is inside at the beginning and at the end of its motion
but we have seen that this level of detail and complexity is necessary if one want to understand to formalize and to compute a right spatiotemporal semantics for motion complexes
we focus on the spatial and the temporal intrinsic semantic properties of the motion verbs on the one hand and of the spatial prepositions on the other hand
then we address the problem of combining these basic semantics in order to formally and automatically derive the spatiotemporal semantics of a motion complex from the spatiotemporal properties of its components
motion verbs can be used directly with a location when they are transitive to cross the town or with a spatial preposition when they are intransitive to go through the town
we have so defined a structuration of the space based on NUM zones the inside o the external zone of contact o the outside of proximity o the far away outside
when we describe a motion the fact to choose a verb instead of another a preposition instead of another a syntactic structure instead of another reveals our mental cognitive representation
inside outside before and after the n otion
a judgement of similarity rnns the risk of meaninglessness if a homogeneous corpus is compared with a heterogeneous one
our comparisons are more global and therefore can result in more effective pruning
the second method machine learning automatically induces decision
we wholeheartedly thank the anonymous reviewers for their very thorough commentary
the bracketed numbers represent pauses as explained below
our results indicate that the agreement among subjects is extremely significant
frequency that n subjects identify any boundary slot as a boundary
in section NUM NUM we discuss the coding and evaluation methods
the movie contained NUM sequential episodes about a man picking pears
the seven subjects are differentiated by distinct letters of the alphabet
the prosodic features were motivated by previous results in the literature
firstly a remarked earlier it generalizes tagging it not only adjudicates between possible labels for the same word but can also use the existence of a constituent over one span of the chart as justification for pruning another constituent over another span normally a subsumed one as in the d l example
recognizer output is being processed however the estimate from each criterion is in fact multiplied by a further estimate derived from the acoustic score of the edge that is the score assigned by the speech recognizer to the best scoring sentence hypothesis containing the word or word string for the edge in question
for example after the string show flight d l three one two is lexically analyzed edges for d and l as individual characters are pruned because another edge derived from a lexical entry for d l as an airline code is deemed far more plausible
this fits in with the fact that on the basis of local information alone it is not usually possibly to predict with confidence that a particular edge is highly likely to contribute to the correct analysis since global factors will also be important but it often is possible to spot highly unlikely edges
in other words our training procedure yields far more probability estimates close to zero than close to one
in the absence of pruning processing takes over eight times as long and produces NUM analyses in total
the work reported here is a logical continuation of two specific strands of research aimed in this general direction
section NUM describes experiments where the constituent pruning grammar specialization method was used on sets of previously unseen speech data
the results of this randomization test applied to alice in wonderland moby dick and max havelaar are shown in the right hand panels of figure NUM by means of symbols
eat agent john a goal b e at goal grow and their corresponding temporary graphs
the expansion forward phase would further add the temporary graphs for the semantically significant words lcb send package rcb during the first step and then would terminate with the second step as no more semantically significant words not yet explored have a maximal common subgraph with the cckg that exceeds the graph matching threshold
the t wo operations we will need are the maximal common subgraph algorithm and the maximal join algorithm
so a word from the dictionary is deemed to be semantically significant if it occurs less than NUM times
haw sub john l on paper inst t crayon sui graph and maximal join algorithms semantic distance between concepts
this is a case where an arnbiguons preposition left in the temporary graph is resolved by the integration process
unfortunately we can not evaluate word using prediction accuracy as we did above as we do not always have access to the system s predictions sometimes it suppresses its predictions in an effort to filter out the bad ones
table NUM gives the results of the trigram method as well as the bayesian method of the next section for the NUM confusion sets NUM the results are broken down into two cases different tags and same tags
if ibm for example occurs in a document without international business machines nominator does not type it rather it lets later processes inspect the local context for further clues
quirk et al s description of names seems to indicate that capitalized words like egyptian an adjective or frenchmen a noun referring to a set of individuals are not names
the professional conduct of lawyers in other jurisdictions is guided by american bar association rules or by state bar ethics codes none of which permit non lawyers to be partners in law firms
nominator forms a candidate name list by scanning the tokenized document and collecting sequences of capitalized tokens or words as well as some special lower case tokens such as conjunctions and prepositions
as noted above paris and washington are highly ambiguous out of context but in well edited text they are often disambiguated by the occurrence of a single unambiguous variant in the same document
sections NUM NUM elaborate on nominator s disambiguation heuristics
shared knowledge and context are crucial disambiguation factors
victoria and albert museum in london remains intact
otherwise it is removed from the list
null our measure of ambiguity is very pragmatic
i am writing in w i NUM to your request concerning the o lowing postponed merchandise ealzligan NUM size NUM
it can be seen that the jury considers the tone of the human letters as being not very good only NUM NUM out of NUM
the direct generator could be used without the other submodules to generate texts in an automatic but non linguistic way manipulation of character strings
i s la rentrde en stock de ces ehaussurcs de sport jc vous lcs envcrrai immddiatement cn priorit NUM
the main points for improvement for lhe automatic system are as follows in decreasing order of variation in relation to the human averages
experiments indicate that the case based learning algorithm improves on the relative pronoun task as relevant biases are incorporated into the underlying instance representation
to learn heuristics for prepositional phrase attachment for example the parser would create a case whenever it recognizes a prepositional phrase
in all experiments below the same ten training and test set combinations as in the baseline experiments of table NUM will be used
all features added to the case as a result of feature normalization not shown in table NUM receive a weight of one
each constituent is described in terms of its syntactic class and its position in the sentence as it was encountered by the circus parser
as above all results are NUM fold cross validation averages and the parser used to generate training and test cases was the circus system
table NUM shows the effects of merging the subject accessibility bias with both recency biases and the restricted memory bias rm
this is not surprising given that the current implementation of the bias is likely to discard relevant features as well as irrelevant features
the table shows that the recency weighting representation alone tends to degrade prediction of relative pronoun antecedents as compared to the baseline cbl system
finally we have argued that the linguistic bias approach to feature set selection offers new possibilities for case based learning of natural language
results of multiple runs of the procedure on a data set were then averaged to give a representative performance rating for that data set
the 6th segment corresponds to the 6th episode plus the beginning of the 7th while the 7th segment corresponds to the end of the 7th episode
unlike hearst s work these studies either use segmentations that are not empirically justified or present only qualitative analyses of the correlation with linguistic devices
note that after a viterbi forward pass identical word hypotheses do always come in sequence differing only in ending time
a second dimension to consider in comparing performance is that humans and np assign boundaries based on a global criterion in contrast to pause and cue
in a left right incremental architecture lri higher level modules can work in parallel with lower level modules
to quantify algorithm performance we use the information retrieval metrics shown in figure NUM recall is the ratio of correctly hypothesized boundaries to target boundaries
were previous results due only to the choice of test data or are the differences in implementation partly responsible
in that case there is significant future work required to understand which differences account for bod s exceptional performance
we will need to create one new non terminal for each node in the training data
thus we compare paired differences of entire runs rather than of sentences or constituents
the probability of the parse tree is the sum of the probabilities of the derivations
each with probability so that the parse tree has probability
each of these trees will have a lower probability than if their counts were merged
now use these trees to form a stochastic tree substitution grammar stsg
researchers are thus left with the difficult decision as to how to clean the data
null it follows trivially from this that no extra trees are produced by the pcfg
we will denote by p ylx the probability that the model assigns to y in context x
the method is proposed and is evaluated with japanese english parallel corpora of three distinct domains
the results are shown in table NUM where the correctness is checked by human inspection
all of the pairs are extracted from a parallel corpus of a specific domain
al used mutual information to construct corresponding pairs of french and english words
this is done for all the pairs of such japanese and english word sequences
to avoid possible combinational explosion some heuristics is introduced to filter implausible correspondences
the former two corpora reveal a worse performance with the pairs with low frequency threshold
table NUM lists the top NUM pairs from the experiment on the business contract letters
it is not the case for a pair that already appeared in the translation dictionary
for each japanese word sequence w t its candidate set is constructed in the same way
the extent of tipster conformance will be determined on a per module basis and documented in the tacad
as stated in section NUM once an element is part of the baseline it is placed under change control
the cm process will document the conformance of each tipster application to the architecture design document and with any applicable apis
section NUM NUM lists the referenced documents and the documents under cm which in turn define the architecture at any time
in this way the cawg will be able to provide comments and recommendations to the architecture committee
as a minimum the cawg will receive the rfc when it is formally submitted to the se cm
submissions from applications developers will be they are as provide for consistency in the development of the architecture
thus the erb chairman shall render final decision as to the course of action to be taken
NUM documentation detailing any design discrepancy or deviations from the tipster architecture on a per module basis
section NUM NUM describes the general approach to cm cm responsibilities and cm phasing and milestones
if the dice coefficient is below the threshold td x is discarded from further consideration otherwise x is saved in a set p
collocations labeled flexible are pairs of words that can be separated by intervening words or occur in reverse order possibly with different inflected forms
in section NUM we explain why this incremental filtering process is necessary and we show that it does not significantly degrade the quality of champollion s output
while some researchers are attempting machine translation through purely statistical techniques the more common approach is to use some hybrid of interlingual and transfer techniques
aujourd hui has evolved from a collocation au jour d hui which has become so rigid that it is now considered a single word
the predicate init x is true if and only if x is the root of an initial tree
the predicate foot px is true if and only if x is the foot of an auxiliary tree
however these are again only weak lexicalizations because the trees produced are not preserved
this reduces the prediction rules to a time complexity of only o igin
as with the gnf procedure one typically begins the rosenkrantz procedure by converting the input to chomsky normal form
the elimination of infinite ambiguity is essential because the gnf procedure will not operate if infinite ambiguity is present
since the input grammar is finitely ambiguous and has no empty rules it can be operated on as is
positions which are depicted as dots in figure NUM are used to represent the state of this traversal
all the trees produced must be left anchored because all the initial trees resulting from step NUM are left anchored
the text generation system komet penman receives input from a dialogue module colt dialogue history and perhaps several other information sources e.g. confidence measure from a speech recognition unit which will be made more precise below see section NUM
in the sample output shown below each canonical name is followed by its entity type and by the variants linked to it
the architecture shall provide for the use of a common complete template library that can support various document processing tasks in different applications
the result of making the transducer of figure NUM onward is shown in figure NUM
class probabilities for noun and verb synsets are calculated using the brute force method based on 280k nouns and 167k verbs extracted fl om the brown eortms NUM million words
or instance a distributional system could easily identify that an article involves lawyers based on recurring instances of words like sue or court
various working modes the el tel system has a modular architecture which allows to select the linguistic complexity of the activity for a gradual work each activity uses among a set of available grammars and lexicons of gradual complexity those from which the sentences will be constructed
examples of patterns for the management succession domain are as follows as the patterns are recognized event structures are built up indicating what type of event occurred and who and what the participants are
an obvious improvement would be to determine whether the person occurs as the subject of a verb of speaking but an informm examination of the data suggested this would not result in a significant improvement
but in addition to writing a rule for this pattern we would have to write rules for all the syntactic variations of the simple active clause to recognize cars are manufactured by gm
for complex noun groups it attaches possessives of phrases controlled prepositional phrases and age and other appositives to head nouns and it recognizes some cases of noun group conjunction
this work represents a productive synergy between the tipster project and another fastus based project at sri the message handler for processing a large number of types of military messages
the user marks a string in the text and then either copies the string to a template entry or enters the set fill that is triggered by the string
we allow all of the current sentence including material to the right of the pronoun since quotes frequently precede the designation of the speaker as in i was robbed said john
this rule creates an event structure in which the event type is talk the parties are the subject and the object of with matched by the patterns and the talk status is bargaining
this is done by matching the output of the complex phrase recognizer with a set of patterns specifying the subject verb object and prepositional phrases in which the events are typically expressed
for the management succession domain there is an event structure for a state specifying that a person is in a position at an organization and a structure for transitions between two states
therefore starting with an n gram model yields a model that is at most equivalent to one that is generated when starting with the trivial model and that can be much worse
by comparison j is the positional index of the pattern string p and the position is in terms of characters
section NUM explores more technical issues relating to the language including functionality and consistency multiple inheritance modes of use and existing implementations
first if node2 path2 is not defined then nodel pathl is unconstrained so this is a weak directional equality constraint
starting from a query local descriptors alone are used to determine either a value or a global descriptor associated with the queried node path pair
both are initialized to the query node and path and the machine operates by repeatedly examining the definition associated with the current local settings
the NUM however in principle there is nothing to stop an extensional statement from being specified as part of a datr description directly
in this case we can use datr paths as dag paths more or less directly referent referent referent np referent
in some cases this can lead to gratuitous extensions to paths path attributes specifying detail beyond any of the specifications in the overall description
but here we can say that the dpl approach allows us to a certain degree to account for the resolution of anaphora without having to leave the field of linguistic descriptions
the problems with the drt representation are more of methodological nature since on the treatment of those cases dpl and drt are empirically equivalent
with the only means of the grammar and the formalism we have we are able to provide a tirst and simple description of those phenomena
the resolution of cross sentential anaphora is one of the problems we have to deal with when we switch towards the analysis of such larger linguistic units
the fact that NUM and NUM translate into the same semantic representation is also reflecting the non compositionality of the classical predicate logic in this case
indefinite nps into the representational language once as an existential quantifier a NUM and once as an universal quantifier a NUM a NUM
and in order to deal with real fife texts it is also necessary to consider the processing of linguistic units which are larger than sentences
the authors arc considering and discussing the cases which contradict the assumptions and give some hints in order to integrate those cases
tte whistles a man i walks in the park or her whistles this is too simple and for sonre english examples it seems to be wrong
l he left daughter is considered to be the head structure sharing of head features as one can see in the following simplified presentation of the rule
in other words the system sometimes erroneously recognizes a noun phrase as a word
our idea of filtering erroneous word hypothesis by expected word frequency is simple and straightforward
new word extraction accuracy is described in terms of recall precision and f measure
to create this merged representation we first establish the right to left labeling of features and then add together the weight vectors recommended by the recency weighting and subject accessibility biases
the fii sl li i he inii cleaj rcb x hliologjy rcb ol tls
the graph enables to distinguish the various meanings of words a crucial feature in the ontological perspective since the meaning level is closer to the concept level than the word level
table NUM shows the precision and the recall are both good for sections NUM and NUM i.e. the entertainment and the international sections
a single characters after segmentation there nmy be a sequence of single characters preceding a possible keyword the character may exist independently
the perforlnance precision recall for the identification of chinese personal names transliterated personal nmnes and organization nmnes is NUM NUM
the character b town results in translation and fq george comes from transliteration
among a set of connected words where w is similar to wi and wj cliques bring out coherent subsets where wi and wj are also similar to each other
the coll6cations which get the greatest cooccurrence scores seem to characterize medecine phraseology facteur de risque milieu hospitalier but not the coronary diseases as such
translation candidates of word sequences are evaluated by a similarity measure between the sequences defined by the co occurrence frequency and independent frequency of the word sequences
since both methods show very inaccurate results for the words of one occurrence only the words of two or more occurrences are selected for inspection
therefore this account does not cover the full range of bare ellipsis cases
d john sang but not in new york at the concert for three hours on tuesday
given the equations in 13a c higher order unification correctly generates 13d as the interpretation of NUM
the studems sent invitations to the professors yesterday and to each other today
in an experiment with two thousand sentence pairs NUM NUM correctness is achieved by the best correspondences and NUM NUM correctness by the top three candidates in the case of compound nouns
spelling lezical item part of speech grammatical semantic people were making a new ship
this feature also helps reduce the possibility of syntactic errors in plan building
the user need only frill in appropriate values on the selected template
these support words are grouped with their associated semantically meaningful words into tokens
the cryptic syntactic part of speech tags are from the penn treebank tagset
the corpus consists of transliterated dialogues on business appointments NUM
the initial log perplexity of the training part is NUM NUM
the constraints allow only some of the otherwise possible merges
this model has the advantage of directly representing the corpus
figure NUM log perplexity of the training part during merging
finally we calculate the perplexity for the additional test part
1many thanks to the verbmobil project for providing these data
the length of the test part is ntest NUM NUM
for this reason the curve is discontinuous at NUM NUM merges
it is low until about NUM NUM merges then drastically increases
the linguistic knowledge needed to support sentence classification includes the definitions of NUM verb types such as intransitive transitive and alltransitive NUM verb definitions and NUM concepts that define the links between the gfs and verb argument structures as represented by events
first a test to identify legal linkings is necessary since this can not be directly expressed in the language and second set membership tests have instances of and have no instances of are necessary since this type of expressiveness is not provided in classic
the semantic class of the verb can be identified once the example sentences are classified by their alternation type
in addition semantic information nmst be stored in the dictionary
there axe a great mmly compound words such as these
in addition users must mmntain additional terms in dictionaxies for specialized fields
such rules also greatly reduce the diction u y size
the resulting parser named qjp is portable fast and robust
in figure NUM solnc pairs are not the new rest ones
figure NUM segmented morphemes with tugs figure NUM segmented words with tugs
qjp is a portable and quick softwaxe module for japanese processing
so it is difficult to segment a sentence into words
for instance if we add an evidence relation to an existing rst tree the ideation which functions as evidence is selected for expression
my sense of relevance is derived from relevance in generation what information has been selected as relevant to the speaker s unfolding discourse goals
null the sole communication between the two systems is through a sentence specification the text planner produces a sentencespecification which the sentence realiser takes as input
the following sentence specification indicates that the speaker is proposing in ormation and that the leaving process is to be the semantic head of the expression
a mathematical measure of the uniformity of a conditional distribution p y x is provided by the conditional entropy NUM
voice selection if the spoken output mode is used wag will select a voice of the same gender as the speaker entity
vs integrated approaches typically a text planner has a knowledge base kb to express and produces a set of sentence specifications to express it
null 4wag s upper model has been re represented in terms of system networks rather than the more loosely defined type lattice language used in penman
template NUM features are useful in dealing with translating noun de noun phrases in which the interchange decision is influenced by both nouns
berger della pietra and della pietra a maximum entropy approach at first glance it is not clear what these machinations achieve
to emphasize the dependence of the entropy on the probability distribution p we have adopted the alternate notation h p
one model satisfying the above equation is p dans NUM in other words the model always predicts dans
figure NUM illustrates how using this improved translation model in the candide system led to improved translations for the two sample sentences given earlier
for instance the example in figure NUM does not specify a choice between negotiate information or negotiate action the first is the default
the participants are given a set of known or training topics along with a set of documents including known relevant documents for those topics
the participants ran the various tasks sent results into nist for evaluation presented the results at the trec conferences and submitted papers for a proceedings
this is done by computing the precision after every retrieved relevant document and then averaging these precisions over the total number of retrieved relevant documents for that topic
note that even though the number of runs has increased by more than NUM adhoc the number of unique documents found has actually dropped
all four trec conferences have centered around two main tasks based on traditional information retrieval modes a routing task and an adhoc task
each assessor constructed his her own topics from some initial statements of interest and performed all the relevance assessments on these topics with a few exceptions
note that whereas the universities tend to participate every year the companies often skip years because of the amount of effort required to run the trec tests
many interesting experiments were done in the tracks including NUM groups that worked with spanish data and NUM groups that ran extensive experiments in interactive retrieval
the runs are ranked by the average precision and only one run is shown per group both official cornell runs would have qualified for this set
it points to some of the other experiments run in trec NUM where results can not be measured completely using recall precision measures and discusses the tracks in trec NUM
of these only NUM of esl s are initially unambiguous while all the remaining are limbo esl s
s entity v exists do human do ppl entity prevl syntactic type prep phrase
the third strength we found was the use of contextual probabilities to predict from the previous word and previous category the likelihood of the next word and the next category
ap tion feat es message message reader i morphological analyzer lexical pattern matcher sgmi annotation generator oat format sgml handling identification of entities output entities
while not shown in figure NUM an alias prediction algorithm was shared by both languages using patterns unique to each language
with japanese changes in the character sets used in running text can be used to detect many of the word boundaries
when we first trained and tested the same model in spanish the results were so encouraging that we decided in april to enter the learned system in met
probabilistic finite state models which had been previously successful in continuous speech recognition and in part of speech tagging can be applied successfully to multilingual entity finding
the current probability model is a hidden markov model hmm which is more complex than is typically used in part of speech tagging and is therefore more general
the strength is that once significant progress is made in one such as location names it can contribute to improved performance in the other categories
patrick jost was very effective in mining available online data to find very large lists of person names critical vocabulary items and organization names
patterns one of the challenges was self imposed because we were interested in seeing how far the technology could go without purchased linguistics resources we restricted ourselves to using only prelinguistics resources
NUM to be sure it is not always true that a hanzi represents a syllable or that it represents a morpheme
note that the backoff model assumes that there is a positive correlation between the frequency of a singular noun and its plural
the model we use provides a simple framework in which to incorporate a wide variety of lexical information in a uniform way
but we also need an estimate of the probability for a non occurring though possible plural form like i nan2 gual meno pumpkins
for languages like english one can assume to a first approximation that word boundaries are given by whitespace or punctuation
for derived words that occur in our corpus we can estimate these costs as we would the costs for an underived dictionary entry
much confusion has been sown about chinese writing by the use of the term ideograph suggesting that hanzi somehow directly represent ideas
they achieved good results using a hill climbing technique to explore the space of possible weights
fortunately there are methods that do converge to the right weights
this table is identical to the one given earlier in the context free case
using the erf method we estimate rule weights as in table NUM
now we face the question of how to attach probabilities to grammar g2
in this case we choose rule NUM to expand the root node
the distribution determined by the training corpus is known as the empirical distribution
in my usage dag edges are labeled with attributes not features
the final four rows compare the estimates for these numbers of tokens given the overall mle eoino infl and eo no pl versus the hapax based mle eh no inf and eh no pl
in other cases a particular instance of syncretism may be displayed only in some paradigms for example russian feminine norms such as loshad horse cyrillic aoma b have the same form for both the genitive singular wundtlaan NUM NUM xd nijmegen the netherlands
in some cases syncretism is completely systematic for example the case cited in dutch where the en suffix can always function in the two ways cited or in latin where the plural dative and ablative forms of nouns and adjectives are always identical no matter what paradigm the noun belongs to
as we shall see the reasons are different from case to case but nonetheless share a commonality in all four cases idiosyncratic lexical properties of high frequency words dominate the statistical properties of the high frequency ranges thus making the overall mle a less reliable predictor of the properties of the low frequency and unseen cases
at the low end of the frequency spectrum we find a great many verbs derived with separable particles such as afzeggen cancel note that separable prefixation is the most productive verbforming process in dutch
in information seeking human machine dialogue it is crucial to signal to the user as unambiguously as possible at which stage in the dialogue she is and what action verbal or non verbal she is supposed to take see s NUM NUM and NUM have described this for english NUM adapted halliday s approach for german
we call this the flat depth grouping method
we call this the flat size grouping method
womnet and uses these to determiue similarity
if the test set were larger or the out of vocabulary rate were higher we believe that the effectiveness of reestimation would be more clearly shown
besides these questions we are also thinking of assigning the part of speech to the extracted new words in order to construct a japanese dictionary automatically
we approximate the spelling probability given word length p o ck k by the word based character bigram model regardless of word length
on the other hand most of the new words not extracted by the system can be divided into shorter words that are registered in the dictionary
we assume that word length probability p k obeys a poisson distribution whose paraineter is the average word length in the training corpus
when the input to the algorithm computational linguistics volume NUM number NUM included a grouping of discourse referents into focus spaces derived from discourse segments performance improved by NUM
we conclude that the segment boundaries identified by at least three or four of our subjects provide a statistically validated annotation to the narrative corpus corresponding to segments having relatively coherent communicative goals
across the NUM narratives statistical significance arises where at least three or four out of seven subjects agree on the same boundary location depending on an arbitrary choice between probabilities of NUM
for example for t NUM because only NUM of the training examples are boundaries c4 NUM achieves an error rate of NUM by always predicting nonboundary
the line labeled learning NUM shows the results from another machine learning experiment in which one of the default c4 NUM options used in learning NUM is overridden
elseif corer coref then if infer infer then nonboundary elseif infer na then boundary elseif infer infer then if after sentence final contour
any other np type provides a referential link if its index occurs in the immediately preceding ficu figure NUM illustrates the two decisions made by np for each pair of adjacent ficus
the algorithms in this section are developed by tuning the previous algorithms e.g. by considering both new and modified linguistic features such that performance on the training set is increased
because our study is exploratory we took the conservative approach of defining a very open ended segmentation task that allowed subjects great freedom in the number and size of the segments to identify
without explicit coding of the substructure of the patient the sentence he cured the leper lcb h pd rcb would stand as a counter example to this generalization
we will investigate how semantic information can be integrated in such a framework and if the bidirectional interface between semantic tagging and nlp system described above can be adopted to this architecture
the corresponding extracted core is shown in NUM
rai e slightly falls down o NUM NUM
NUM NUM NUM oui a ill8
restrictions that a verb or a noun imposes on its context
lioiild exl loii bolh hiherenl soiiin ililic
possil ly stored in ki3 more a bsl ract
in french macros describe for example the verb complex for mwls involving a reflexive verb
this compactness and flexibility are as far as we know specific to our approach
the target position in a connection high depends that of adjacent connections
NUM perfbrm the part of speech tagging and analysis tbr sentences in both languages
his establishes anchor points for calculating the relative distortion score
the left dummy in the source and target sentences align with each other
the inside test consists of fitty sentence pairs from lecdoce as input
they advocated applying a statistical approach to machine translation smt
the first two models have been used in research on word alignment
however the degree of success in word alignment was not reported
however this work has presented a workable core for processing bilingual corpus
however those deviation are largely limited within the classes defined by thesauri
the model generates the space of features by scanning each pair hi ti in the training data with the feature templates given in table NUM
in addition the search procedure optionally consults a tag dictionary which for each known word lists the tags that it has appeared with in the training set
the model with specialized features does not perform much better than the baseline model and further discovery or refinement of word based features is difficult given the inconsistencies in the training data
conclusion the maximum entropy model is an extremely flexible technique for linguistic modelling since it can use a virtually unrestricted and rich feature set in the framework of a probability model
the tbl representation of the surrounding word context is almost the same NUM and the tbl representation of unknown words is a superset s of the unknown word representation in this paper
since most realistic natural language applications must process words that were never seen before in training data all experiments in this paper are conducted on test data that include unknown words
this paper briefly describes the maximum entropy and maximum likelihood properties of the model features used for pos tagging and the experiments on the penn treebank wall st journal corpus
let w lcb wl w rcb be a test sentence and let sij be the jth highest probability tag sequence up to and including word wi
it is better able to use diverse information than markov models requires less supporting techniques than sdt and unlike tbl can be used in a probabilistic framework
using the set of NUM difficult words the model performs at NUM NUM accuracy on the development set an insignificant improvement from the baseline accuracy of NUM NUM
here we briefly review our statistical results and summarize the motivation for our method of abstracting a single segmentation for a given narrative from a set of subjects responses
in their talk they presented results showing that the occurrence and placement of a discourse usage of a cue word correlates with relative order of core versus contributor utterances
but since the words cousin and inheritance evoke frames of their own the same sentence could easily come up in our exploration of the semantics of those words as well
corpus examples in which wound and disease are both instantiated are of course rare and given this complementary distribution we might be tempted to identify these as variants of a single frame element which we might call affliction
the 2nd version will be released on solaris at the address http kibs kaist ac kr kle kibs
keyword in context kwic manager deals with word usage of text corpus
this work provides users such terminological details
we initially focused on word level voice data
the third class is applications technology
ontology based lexicon currently awnlable dictionaries are semantically oriented
therefore it guarantees the standardization and straightforward de
this is also the ease for the dictionary management
the data are stored in server disks and cd roms as a wave form
sunos version NUM platform and web pages are only in korean
and an adjective expressed in predicate logic
this task has not been addressed in previous work
tary learning step as descril ed in NUM he previous section and a nmta hiterpretcr for ll i p grainiiiars which serves the processes of interpretai ion
as negative and l roceeds fl n ther
not violating word order rules or negative i.e.
wlmther NUM his rule covers allluost specific instnnces
we expect these features to be relevant when the decision of whether to interchange the nouns is influenced by the identity of the left noun
in other words given a collection of facts choose a model consistent with all the facts but otherwise as uniform as possible
earlier we divided the statistical modeling problem into two steps finding appropriate facts about the data and incorporating these facts into the model
a search of kyodo newswire data using the keyword press conference yielded the desired NUM article development and NUM article test corpora
in the present setting however the linear constraints are extracted from the training sample and can not by construction be inconsistent
however the met domain focused on political rather than commercial entities so there were very few instances of this designator
as table NUM shows the group average f measure for these tag types was over NUM on the test d a
the government s intuitive assumption concerning the relative difficulty of identifying enamex types people places and organizations was borne out
this task was complicated however by the prevalence of embedded i ocation elements within organization expressions and the effects of context upon tag type
kleene operators like NUM or more or NUM or more are frequently used in semiformal linguistic descriptions
however in the context of a typed unification formalism like ours the exact interpretation of kleene operators is not completely straightforward
finally we will point out that multiple equations for the same feature on a category are permitted where they are consistent
we turn now to descriptive devices not present in the formalism as defined so far and to ways of making them available
if an agent phrase is present the logical form of the np in it replaces the something and becomes the out value
this may in turn re introduce some inefficiency since there will be an extra level of structure that is not linguistically motivated
top l person witch monarch i i adult child queen i i teenager wicked queen btm
secondly and perhaps more importantly for the grammarian in many cases using the boolean combination would be a linguistically inaccurate solution
because of the type of boolean mechanism we are using we are restricted to atomic symbols to represent the subcategorized for elements
the way to solve this problem is to expand our boolean combination of subcat feature values to include some special finish symbols
an annotation to an annotation is permitted
however there are degrees of persistency
new annotation types may be created
NUM NUM NUM common parts of speech word lists
modules are used to build components
omle and hmle are respectively the overall and hapax based mles
in still other cases the syncretism may be partial in that two forms may be identical at one level of representation say orthography but not another say pronunciation
this does not hold generally however and the bottom panel of figure NUM presents a case where the hapax based mle does yield a different prediction as to which function is more likely
NUM NUM english verb forms in ed
the results are shown in table NUM
we now have to consider why this result holds
NUM NUM dutch verb forms in en
for that reason we expect an even greater level of accommodation in the human interpreted setting than in the human human setting
in lexical accommodation one conversant adopts the lexical items used by the other conversant
finally we show how the results can be used for a variety of applications closing with a discussion of the limitations of our approach and of future work
as a partial solution for pairs of hanzi that co occur sufficiently often in our namelists we use the estimated bigram cost rather than the independence based cost
this suggests that the backoff model is as reasonable a model as we can use in the absence of further information about the expected cost of a plural form
NUM there were NUM marks of punctuation in the test sentences including the sentence final periods meaning that the average inter punctuation distance was about NUM hanzi
the problem with these styles of evaluation is that as we shall demonstrate even human judges do not agree perfectly on how to segment a given text
first of all most previous articles report performance in terms of a single percent correct score or else in terms of the paired measures of precision and recall
NUM as one reviewer points out one problem with the unigram model chosen here is that there is still a tendency to pick a segmentation containing fewer words
this wfst represents the segmentation of the text into the words ab and cd word boundaries being marked by arcs mapping between c and part of speech labels
unfortunately there is no standard corpus of chinese texts tagged with either single or multiple human judgments with which one can compare performance of various methods
let h be the set of hanzi p be the set of pinyin syllables with tone marks and p be the set of grammatical part of speech labels
in addition the user is provialed with information regarding term frequencies and term relevance ranking scores
the user can select individual nodes in the tree structure by pointing and clicking the corresponding folders
thus rewriting each output symbol in variable notation is done in constant time and adds nothing to the algorithm s computational complexity
the layout of the graphic design is intended to facilitate the quick comprehension of the displayed information
two sets of questionnaires were filled out by the domain experts who participated in the usability testing
a preprocessor generates a set of key terms from a text dataset which represents a specific topic
in this paper we present an applied research prototype system that intends to accomplish two major tasks
currently each partition contains NUM root nodes or folders representing single word terms
this paper presents a prototype system for key term manipulation and visualization in a real world commercial environment
there are three subwindows the document identifier window the document window and the navigation window
once a partition has been selected the corresponding document collection is loaded into the document browser
yet it is not clear whether speech segments should be extracted from nonsense plurisyllabic words called iogatoms existing isolated words or meaningful senteuces
then segmental prosodic paramete w are determined tbr each allophone on the basis of the accent position within a word and its type
the dictionary is supposed to cover the most fl equent words in a given hmguage and a second dictionary helps with pronouncing proper names
the ls adora system builds a large network of nodes that correspond to different speech events like phones phonemes words or sentences
as most phonological units originate via phonological considerations rather than on acoustic grounds isolating them requires a deep prior knowledge of their specilic features
when constructing the algorithm s original tree transducer variables can be included in the output strings of the transducer s arcs
surface showing how lambda varies with frequency log scale of previous word and bigram class granularity
NUM pp attachment for the verb np pp pattern is relatively easy to predict because the two possible attachment sites differ in syntactic category and therefore have very different kinds of lexical preferences
in addition we search for restatements in the text
the most prevalent discourse topic will play a big role in the summary
b they re in jail for such things as bad checks or stealing
NUM NUM NUM NUM verification method demonstration
these are marked with two aas at the beginning of the segments above
they re in jail for such things as bad checks or stealing
the component shall return a single ranked list of documents in multiple languages
it functions like an electronic probation officer
in order to do the classification we relied on three kinds of annotations that were available for the switchboard corpus sentence boundaries part of speech and dysfluency annotation
comparison with table NUM shows that for t NUM learning NUM rather than learning NUM is the better performer
the best performing algorithm resulted from the machine learning experiment in which certain default options were overridden learning NUM in table NUM
the input to the algorithm consisted of semantic information about utterances in a pear narrative such as the referents mentioned in the utterance
this is strong evidence that the tuned algorithm is a better predictor of segment boundaries than the original passonneau and litman discourse segmentation np algorithm
the test results of ea are of course worse than the corresponding training results particularly for precision NUM versus NUM
note that for each iteration of the crossvalidation the learning process begins from scratch and thus each training and testing set are still disjoint
finally table NUM shows the results from a set of additional machine learning experiments in which more conservative definitions of boundary are used
in our experiments we investigate the correlation of linguistic cues with boundaries identified by both i NUM and i NUM subjects
again in contrast with cochran s q it is simply a ratio rather than a point on a distribution curve with known probabilities
NUM word types appeared twice in the test set
therefore the reported performance could be greatly underestimated
the first type is the truncation of long words
known words appeared only once in the test sentences
forward probabilities can be recurslvely computed as follows
statistical language model segmentation model tagging model
the other NUM word types appeared only once
most of extraction errors are of this category
there are three types of obvious extraction errors
the second type is the fragmentation of numerals
the maximum entropy model allows arbitrary binary valued features on the context so it can use additional specialized i.e. word specific features to correctly tag the residue that the baseline features can not model
figure NUM which again uses integers to denote pos tags shows the tag distribution of about as a function of annotator and implies that the tagging errors for this word are due mostly to inconsistent data
introduction many natural language tasks require the accurate assignment of part of speech pos tags to previously unseen text
since such features typically occur infrequently the training set consistency must be good enough to yield reliable statistics
figure NUM represents each pos tag with a unique integer and graphs the pos annotation of about in the training set as a function of the articles the points are scattered to show density
furthermore this paper demonstrates the use of specialized features to model difficult tagging decisions discusses the corpus consistency problems discovered during the implementation of these features and proposes a training strategy that mitigates these problems
the total accuracy is higher implying that the singly annotated training and test sets are more consistent and the improvement due to the specialized features is higher than before NUM but still modest implying that either the features need further improvement or that intra annotator inconsistencies exist in the corpus
the convergence of the accuracy rate implies that either all these techniques are missing the right predictors in their representation to get the residue or more likely that any corpus based algorithm on the penn treebank wall st journal corpus will not perform much higher than NUM NUM due to consistency problems
table NUM interactions between homogeneity and similarity a similarity measure can only be
it is of interest as a preliminary to a measure of corpus similarity
that information can not be readily used to make e.g. similarity judgements
however it is effectively unable to distinguish among words that have the same part of speech
rather we believe that the difficulty with r deletion is the broad context in which the rule applies after any vowel and before any consonant
in fact adding input pairs makes finding the smallest possible automaton more likely and reduces the number of states at which pruning is necessary
our reason for choosing subsequential transducers then is solely that efficient techniques exist for learning them as we will see in the next section
the next sections NUM and NUM introduce the idea of representing phonological rules with transducers and describe the ostia algorithm for inducing such transducers
the subsequential transducer does not emit any output until enough of the right hand context has been seen to determine how the input symbol is to be realized
figure NUM shows a resulting decision tree that generalized the transducer in figure NUM to avoid the problem of certain inputs falling off the transducer
the use of alignment information also reduced the learning time the additional cost of calculating alignments is more than compensated for by quicker merging of states
the context is represented by the current state of the machine which in turn depends on the behavior of the machine on the previous segments
the effects of adding decision trees at each state of the machine for the composition of t insertion t deletion and flapping are shown in table NUM
summarization an application for nl generation
the malysis then lfies to bind lhe modifier with adverbial particle wa to a non bound valency element
all valency patterns for each usage with each predicate are prepared beforehand and held in the valency pattern dictionary
semantic category a semantic category is a class for dividing nouns into concepts according to their meaning
valency structure the sentence structure can be considered as a combination of a predicate and its modifiers
therefore as shown in figure NUM the only non bound valency element is n1 which is a subjective case
ihe main aim of lhese standards is to establish objective test sets for machine translation system evaluation
that we need about the constituents
yiyiyi fiyiyhyiyiyi figure NUM an arabic document and its raw translation
deep source tree structure j source phrase matching j flat source tree structure jkf creation of target phra s partial target tree structure morphological transfer j partial target tree structure morphologlcal agreement full target tree structure lqgure j process ot ulossary based maclnne h anslatlon glossary based machine translation
the navigation window enables the user to navigate through the documents to view the selected key terms in context
the measures are reported anonymously as a high and a low score
the f measures in table NUM were produced by the automated scoring program
it shows the document id and the total frequency of the selected key term in the document collection
the set of nodes on each traverse is called a traversal node set
the essence of dtlas lies in the recursive search and division
these two requirements are formally defined below using the notations in table NUM
decision tree learning algorithm with structured attributes application to verbal case frame acquisition
table NUM classification results on open data
another factor to consider is the measurement of the goodness of a generalization
figure NUM optimally generalized decision tree for take
it then divides the table with values of the attribute
we will consider this prol lem in section NUM
there will be many unknown nouns in the open data
the last two lines in the table point to the differences between general corpora and specific corpora
high scores for heterogeneity will be for general corpora which embrace a number of language varieties
the first point to make is that there is no obvious way to approach the question
table NUM variation of o e NUM e term with word frequency for same variety corpora
such work is highly salient for customizing parsers for particular domains
in section NUM compare these methods with the approach developed here
this is why a higher proportion of common words than of rare ones defeat the null hypothesis
the question arises wimch words and how many should be used in the comparison
the hansard flavor the rather specific domain of parliamentary discourse related to canadian affairs is easy to detect in many of the features in this table
in the table the symbol o is a placeholder for a possible french word and the symbol is a placeholder for a possible english word
we begin with a training sample of english french sentence pairs e f randomly extracted from the hansard corpus such that e contains the english word in
this is just a slightly recast version of a longstanding problem in computational linguistics namely sense disambiguation the determination of a word s sense from its context
employing a larger or even just a different sample of data from the same process might result in different estimates of NUM f for many candidate features
computational linguistics volume NUM number NUM table NUM the duality of maximum entropy and maximum likelihood is an example of the more general phenomenon of duality in constrained optimization
in reamife applications however we are provided with only a small sample of n events which can not be trusted to represent the process fully and accurately
translating a french sentence into english involves not only selecting appropriate english renderings of the words in the french sentence but also selecting an ordering for the english words
we begin with a sample of english french sentence pairs e f randomly extracted from the hansard corpus such that f contains a noun de noun phrase
the authors wish to thank harry printz and john lafferty for suggestions and comments on a preliminary draft of this paper and jerome bellegarda for providing expert french knowledge
the longest common subsequence between two words can be computed as a special case of their edit distance in time proportional to the product of their lengths wag74
using a lcsr cut off of NUM NUM optimized using bible of course cognates were found for NUM of the source tokens in the training corpus counting punctuation
for example gouvernement which is NUM letters long has NUM letters that appear in the same order in government
a predicate filter is one where the candidate translation pair s t must satisfy some predicate in order to pass the filter
so it throws out that pairing along with several other english noun candidates allowing first to move up to third position
finally the mrbd filter narrows the list down to just the three translations of french premier that are appropriate in the hansard sublanguage
words that the most precise lexicon did n t know about which were found in the second most precise lexicon were translated next
evidently some information that is useful for inducing translation lexicons can not be inferred from any amount of training data using only simple statistical methods
futhermore since y NUM NUM and z2 c z a multiplicative fimtor of the space tim complexity can be reduced if mapping is also carried out for single byte as well as NUM byte characters in NUM
to achieve o NUM tpi d the function found uses an array f of size 1el where i2 is the alphabet to store the equivalent single byte characters
onwardness happens computational linguistics volume NUM number NUM figure NUM resulting initial transducer for importance
the logos system is an outstanding example of applying computational linguistics in the commercial sector
the analysis determines the semantic relationships between words as well as the syntactic structure of the sentence
these permit problem cases such as concepts with completely separate meanings or subtle variations to be translated suitably
today the logos system is one of the best commercial mt systems on the market
the logos system is fundamentally different from other computer based translation programs
parsing is only source language specific generation is target language specific
logos machine translation system logos corporation has been involved in machine translation r d for over NUM years
logos analyzes whole source sentences considering morphology meaning and grammatical structure and function
this comprehensive analysis permits the logos system to construct a complete and idiomatically correct translation in the target language
logos does not claim to replace human translators rather we seek to enhance the professional translator s work environment
the corresponding natural points of articulation in flat semantic structures are the entities that we have already been referring to as indices
it limits proliferation of the ill effects however by allowing only the maximal one to be incorporated in larger phrases
however it does not count as an output to the generation process as a whole because it subsumes some but not all of NUM
when run is moved the sequence john ran is considered as a possible phrase on the basis of rule NUM
with appropriate replacements for variables this maps onto the subset NUM of the original semantic specification in NUM
the first interacts with the active edge originally introduced by the verb saw producing the fourth entry in NUM
an active edge a c c should be thought of as incident from or accessible through the index c
we concentrate on the phrase tall young polish athlete which we assumed would be combined with the verb phrase ran fast by the rule NUM
we have adopted the following guidelines for query expansion
the paragraphs are subsequently pasted verbatim into the query
we obtain NUM expansion sub collections one per query
NUM when manual expansion is used
this explosion in numbers comes from the compounding of repeated substitutions in steps NUM NUM
the derivation of an appropriate english form is left to the morphological synthesis in the translation process
tsure tsure tsure tsure hakobu hakobu hakobu te iku te iku te iku te iku hakobu figure NUM partial thesaurus t
the first line for instance represents a sentence which is a column type text its location in text is in the rear its similarity to title is nil it is NUM character long its attidutinal type is NUM it has a tfidf value of NUM NUM it occurs at one third of the paragraph and finally its class is y meaning that it is judged important
raw corpus two factors are the genre of each source text that is related to the objective s in using the corpus and the category of the text that represents the internal structure of the text
the corpus based approach inevitably encounters tile sparseness problem
table NUM shows the results of the proposed method
for longer word length the difference is greater
thus the wl initially contains these five words
an NUM kanji compound noun usually contains four nouns
particles c j q a wo and l ni roughly indicate agent object and goal respectively
figure NUM illustrates the processing i ow
the words are used as keys lot the search
null unsupervised methods have on their side serious limitations first the type of occurring syntactic ambiguity phenomena are in the average much more complex than the standard verb noun preposition noun patterns analyzed in literature
an incremental architecture for unsupervised reduction of syntactic ambiguity the previous section shows that we need to be more realistic in approaching the problem of syntactic ambiguity resolution in large
but if one of the two accidentally the wrong has an even minimal additional evidence with respect to its competitor this initially small advantage may be emphasized by the plausibility redistribution rule NUM
in section NUM we describe an unsupervised classbased incremental syntactic disambiguation method that is aimed at reducing noisy collocates to the extent that this is allowed by the observation of corpus phenomena
to measure the complexity of the ambiguous structures we collected from fragments of the two corpora all the ambigous collision sets i.e. those with more than one esl
these measures have been derived from the early thus noisy state of knowledge where just the ssa grammar and no learning was applied to the corpus
the nature of ambiguous phenomena in untagged corpora has not been studied in detail in the literature although one such analysis would be very useful on a language engineering standpoint
if the mutual information is high it means that the measured phenomena productive word tuples do not independently occur in collision sets i.e. they systematically occur in reciprocal ambiguity in the corpus
however building a small size glossary such as the arabic english glossary which contains approximately NUM NUM entries is a relatively easy and fast task
this had karl with counted karl expected this b i mit gerechnet hatte da i karl
such corpora should contain among other things native tongue of the speaker the complexity of the text under consideration error correction markup etc
in real settings learners freely interact with their environment parents tutors taking turns asking for explanations shifting topics etc
compling hu berlin de st ef an pub e pvp html thanks to prank keller for comments oil earlier versions of this paper
it is sufficient to prove that a problem is np hard in order to prove that it is intractable
the same problem exists for analysises that treat verb second as verb movement kiss and wesche schema NUM shows how this is implemented
the np completeness of mpp can be easily deduced from the previous section
the threshold q and the requirements on NUM are also updated accordingly
we recognize several shortcomings with our approach which we hope to be able to address in the future
lcb w rcb wound the cut rapidly expressed perhaps as lcb h b t rcb
however we believe that such an analysis is a prerequisite to a theoretically sound semantic formalization s while any given frame description could be made more precise for other nlp ai purposes such as inferencegeneration the development of such a formalism is not a central part of our current work
the relationship between frames is frequently hierarchical for example the frame elements buyer seller payment and goods will be common to all commercial transactions the purchase of real estate contains all of them and typically adds a loan and a bank typically as lender
we believe therefore that any description of word meanings must begin by identifying such underlying conceptual structures
the probability of any such derivation is
and mpp NUM NUM 3sat to mppwg and mps
thus mppwg and mps also answer yes
the word graph is also the same word graph as in mps for stsgs
this justifies the choice to reduce reduction
the proof is easily deducible from the proof concerning mps for stsgs
crucially each elementary tree results in one unique ci g production
the trouble is there are many possible ways of choosing among potential attributes and one has to go through some trial and error experimentation to find a set of attributes that work best for his or her task
below we present a simplified rewrite rule version of the dialogue model
technically the confidence measure would come from the speech recognition unit
the relation between dialogue moves and tone types is however not trivial
the overall system architecture for speak is shown in figure NUM
this discussion is summarized in table NUM
d s r u p s
tone 4a indicates neutral involvement while tone 4b signals strong involvement
how can the mapping between speech function and mood be constrained then
for example schliefl das fenster close the window
it shows that tagging accuracy increases significantly when access to the available feature set is appropriately limited
the tirst two forlnation clauses pivot on the semantic form piled values
we define a language of wff s we ll fornmd fstructures
direct and underspecified interpretations of lfg f structures
we have provided direct and indirect undersl ified
is syntactic identity modulo permm ation
consider tile flagmeat without n and np modification
we tbrmalise this intuition in terms of translation functions r
the f strneture associated with our example sentence translate s
the search heuristic attempts to find the best parse t defined as
to support the development of the system and to delimit the linguistic coverage of the nlp application a small corpus has been semantically hand tagged where the semantic annotations have been added to the mainly syntactic annotation scheme of the tsnlp framework
he first applied sato s method and extracted statistical word correspondences from the result of the first path
this paper shows that the problem is analogous to n gram language models in speech recognition and that one of the most common methods for language modeling the backed off estimate is applicable
a possible criticism of the backed off estimate is that it uses low count events without any smoothing which has been shown to be a mistake in similar problems such as n gram language models
for example f NUM is revenue from research is the number of times the quadruple is revenue from research is seen with a noun attachment
for example p of might have a strong weight for noun attachment while v buy p for would have a strong weight for verb attachment
typically ambiguous verb phrases of the form v rip1 p rip2 are resolved through a model which considers values of the four head words v nl p and NUM NUM
NUM w g does not occur in any other english sentence that is a possible translation of jsentencei
their motivation is that structurally different languages such as chinese english and japanese english are difficult to align in general
our goal is NUM coverage of the words found in hedcndaagsc tans aud NUM coverage of the most fi equent NUM NUM words and we arc close to it
the following example illustrates the derivation of syriac ktab NUM he wrote in the simple p al measure NUM from the pattern morpheme lcb cvcvc rcb verbal pattern root lcb ktb rcb notion of writing and vocalism lcb a rcb
it takes a surface string or lexical string s and returns a list of partitions and a 9for efficiency variables appearing in left context and center expressions are evaluated after lexical transitions since they will be fully instantiated then only right contexts are evaluated after the recursion
there are two current methods for implementing two level rules both implemented in semi lcb e NUM compiling rules into finite state automata multitape transducers in our case and NUM interpreting rules directly
apart from beesley s work seem to have been implemented over large descriptions nor have they provided means by which the grammarian can develop non linear descriptions using higher level notation to test the validity of one s proposal or formalism minimally a medium scale description is a desideratum
it takes three arguments result is the output of partition usually reversed by the calling predicate hence coerce deals with the last partition first prevcats is a register which keeps track of the last morpheme category encountered and partition returns selected elements from result
this means that in many cases unless we work in absence of noise the correct and wrong associations in an ambiguous phrase acquires the same or similar statistical evidence
the lexical items are supplemented by a morphology table which has about 317k entries for various grammatical inflections
the scores for each formula are sorted and the one which is less than NUM of the personal names is considered as a threshold for this fornmla
specifically it has correctly recognized the confirmation of the previous exchange act NUM and recognized a request to move a train from albany act NUM
NUM described a method for obtaining estimates of lexical a ssociation strengths between nouns or verbs and prepositions and then using lexical association strength to predict
as in the first set of experiments a number of methods were evaluated an the three attachment site pattern with NUM samples of NUM random pp cases
to look at it another way if we could n t build a successful system by employing whatever means available then there is little hope for finding more effective general solutions
NUM training corpora and testing corpus the proposed methods in this paper integrate the rule based and the statistics based models so that training corpora are needed
the expanded domain will require a much more sophisticated ability to answer questions to display complex information concisely and will stress our abilities to track plans and identify focus shifts
first we could attempt to improve the specific models that were presented by incorporating additional features and perhal s by taking into account higher order features
was evaluated oil a series of NUM random samples of NUM pp cases fi om the evaluation pool in order to provide a characterization of the error variance
this is included to account for correlations between attachment and syntactic category of the nominal attachment site such as pps disfavor attachment to proper nouns
when all three biases are included in the case representation the learning algorithm performs significantly better than the hand coded rules NUM NUM correct vs NUM NUM correct at the NUM confidence level
in the sections below we first present empirical evidence that the success of case based learning methods for natural language processing tasks depends to a large degree on the feature set used to describe the training instances
for the relative pronoun task for example we assumed that all three linguistic biases were relevant and then exhaustively enumerated all combinations of the biases choosing the combination that performed best in cross validation testing
both the right to left labeling and combined representations improve performance they perform significantly better than the default heuristic but do not yet exceed the level of the hand coded heuristics
although one would not expect monotonic improvement to continue forever it is clear that explicit incorporation of linguistic biases into the case representation can improve the learning algorithm performance for the relative pronoun disambiguation task
in particular three representations shown in italics now outperform the best previous representation which had the r to1 labeling recency weighting memory limit NUM and achieved NUM NUM correct
what all of these cases share is that the statistical properties of the high frequency ranges are dominated by lexical properties of particular sets of high frequency words
this query shall be in a format that is understandable by an interested user
in contrast c4 NUM assumes that both types of errors are penalized equally
in fact his mechanism also covered cases of parenthetical placement scrambling relative clause extraposition man eats apples
equating the np nodes i in cq and t72 preserving the linear precedence of the argu null ments
we also discuss the construction of a practical parser for ltags that can handle coordination including cases of non constituent coordination
NUM fhc ordering is given by the fact that the elements of the set re gorn tddresses
informally the conjoin operation works as follows the two trees being coordinated are substituted into the conjunction tree
the conjoin operation supplies a new edge between each corresponding node in the contraction set and then contracts that edge
krippendorff s c and siegel and castellan s k differ slightly when used on category judgments in the assumptions under which expected agreement is calculated
passonneau and litman have only naive coders but in essence have an expert opinion available on each unit classified in terms of the majority opinion
in passonneau and litman the reason for comparing to the majority opinion is less clean despite our argument there are occasions when one opinion should be treated as the expert one
worse yet since none of them take into account the level of agreement one would expect coders to reach by chance none of them are interpretable even on their own
it is possible and sometimes useful to test whether or not k is significantly different from chance but more importantly interpretation of the scale of agreement is possible
it is interpretable allows different results to be compared and suggests a set of diagnostics in cases where the reliability results are not good enough for the required purpose
we have chosen these examples both for the clarity of their arguments and because taken as a set they introduce the full range of issues we wish to discuss
priority scores can be weighted based on where in the document the match occurs such as title source attribute etc
the architecture shall provide for the use of common phrase lists that can be shared by various document processing tasks in different applications
the architecture shall allow requests for document clustering provided the appropriate clustering algorithms and interfaces to document management and user information output are established
the operating environment is concerned with such items as client server schemes file handling methods operating systems communications and support items
however it shall support the access to specific information to build a data base by allowing an application to use filled templates
if several such portions of the query match then an algorithm can combine priorities to get a net priority for the document
NUM the architecture shall provide for the use of a common lexicon that can support various document processing tasks in different applications
the architecture shall be documented in a formal language that describes the components and modules and how they interact using an object oriented methodology
the results from such processing or other internal tipster processes will be passed to the user interface via the user information output
relevance tags may provide guidance for the refinement also as an option documents already seen may be suppressed from the re run
the greater concern for communicational efficiency in the machine interpreted setting was not enough to generate as high a level of accommodation as that found in the human interpreted setting where there was the additional factor of concern for social position though it did generate a higher level of accommodation than did concern for social standing alone the human human setting
for exa inl le in NUM above sentence d is an evident elaboration on the envelope that appears in sentence e
entities may be presented as new in the discourse context through references composed by a nominal expression or a pronoun presenting
in our system pronominalization is decided according to new rules extending the centering model as explained in the following section NUM NUM
this is a standard metric f infer mation retrieval based on the assumption dmt the higher fre luency w irds provide less intormation about topics si arck jones NUM
for a senl enc l we will ul ilize the word prefercn s based on topic coherence to sele t tim b st hy pothesis
sift language set whi h will i e use l in analyzing the f llowing sentenc4 in the test m ti le
the first sentence is the correct transerii tion the second one is sri s best scored hypothesis and the third one is the hypothesis with the highest combined score of sri and our models
have been sevcr d attempts to incorporate other knowlcdg source s in particular long x range word tet nden ies in order to improve
in our experiment we achi wed a NUM NUM imi rovenmn in the wor NUM eiror rat on top of t h base lin
first of all we know that closed class words and high frequency words appear in most of the documents regardless of the topic st it is not useflfl to include these as keywords
the above is repeated for each i until i NUM is reached
however the first a2 initial tree has the al initial tree substituted into it
we modify the grammar to satisfy f inductively for increasing values of i
we modify the grammar to satisfy this property inductively for decreasing values of i
second the gnf procedure can reduce the ambiguity of the input grammar
this is the source of the most radical changes in the trees produced
however there are five important differences between the ltig and gnf procedures
step NUM every elementary tree t is now an initial tree
an ordering lcb a1 am rcb of the nonterminals nt is assumed
a single tunable parameter controls how steeply the thresholds are set for the study here this parameter was set to the middle of its useful range providing a fairly neutral balance between reducing false negatives and increasing false positives
fac de f definition s top
cityal 1nq201 x sierasl i citfi2
comparison of adhoc results for trec NUM and trec NUM
the observed running time of the parser on a test sentence is linear with respect to the sentence length
one of tile drawbacks of an tlmm based approach is that laborious manual tuning of symbol and transition biases is nec essary to achieve high accuracy
it is very ditiicult to compare performances between taggers when accuracy depends on quality of corpora and lexicons and maybe on characteris its of languages
q he l lcb ule tagger was given a moderately tight restrictiotb on using context for reduction rule application i.e. r lagj rccdom NUM
in addition in such a corpus rarely used pos tags of a word are less likely to occur and words are less likely to be ambiguous
the next tests performed involved using rules generated above and changing NUM arameters to the rule l agger to see how the scores wouhl be influenced
this nay well be due to the fact that the ordering of the rules as produced by the learner is dependent on the training texts
the third pass always alternates between the use of build and check and completes any remaining constituent structure
section NUM describes experiments with the penn treebank and section NUM compares this paper with previously published works
a longer term aim is to integrate the system with existing grammar develol ment environments
as a tutorial tool ci parts allows students to investigate certain tbrmalisms and their relationship
the result is a highly modular and we beliew a highly flexible system which
1a lh amework tor computational semantics f u ropean community lre NUM NUM
for this application such an approach had obvious inadequacies
the resulting display is given in t igure NUM
figure NUM initial ffepresentation of anna laughs with aq l lcb l
this paper describes an interactive graphical environment for computational semantics
however there are some extra library routines for example a very generalised form of flmction composition
there was also practical motivation since there is more chance of finding errors in shared code
backed off predicates are not enumerated in table NUM but their existence is indicated with a and t
we only apply it in a candidate of length NUM this is because it is easy to satisfy the character condition for candidates of the shortest length
if the input text is not segmented beforehand it is easy to regard q j as a chinese personal name
the second training corpus is extracted from three newspaper corpora china times liberty times news and united daily news
because the surnames w th two characters are always surnames model b neglects the score of surname part
this paper deals with three kinds of proper nouns say chinese personal names transliterated personal names and organization names
ostia now attempts to generalize the transducer by merging some of its states together
if name part of a candidate is a word word association is used to measure the relationship between the surrotmding words
in other words NUM george bush will have a feature of male transliterated personal name when it is identified
when a candidate can not pass the threshold its last character is cut off and the remaining part is tried again
the overall performance is good except for section NUM the international section and section NUM the economic section
the nonrandom clustered appearance of lexically specialized words often the key words of the text explains the main trends in the overestimation bias both quantitatively and qualitatively
constituent label of parent x and constituent or p0s labels of children xz xn of proposed constituent pos tag and word of the nth leaf to the left of the constituent if n NUM or to the right of the constituent if n NUM
in NUM however the m structure is projected from the e structure parallel to the f structure through annotations similar to the usual f structure annotations
NUM this is not lcb lesirable flom l crosslinguisti lcb point of view nor is it helpful f lcb rcb r mt
thus an application of this treatnmnt not only provides an adequate grammatical analysis of the np in ger nan but also facilil ates mt
with e very auxiliary subcategorizing for an xcomp the two nps could conceivably be arguments of three different verbs wird haben or gedreht
language particular idiosyncratic requirements are thus separated out from the language universal information l quired for further semantic interpretal ion or machine translation
finally those features are combined under the maximum entropy framework yielding p a b
in case of a derived nominal however a genitive is interpreted according to tile thematic roles assigned to tile arguments of the verbal base
with regard to genitive nips the standard analysis tbr german yields strtlctures which are too alnbiguous for a succesflfl at plication of madfine translation
this paper tbeuses on two disparate as i e ts of german syntax from the perspeetive of paral ej grammar developmenl
a variety of evidence combination and parallel hypothesis selection mechanisms will be considered for the dynamic case including dempster shafer and bayesian approaches best evidence and chart management methods
other issues that could be discussed include do we assume there is always a single correct tag or do we allow a set of equally correct tags
combining knowledge sources for automatic semantic tagging
if so this complicates evidence combination
daj NUM baonysh afterlife nc s c mi i NUM goal of the session in this working session we will discuss methods which could plausibly be used for combining evidence for assigning semantic tags to words in a text
contrast work in combining wordnet and levin classes
do we rank or assign probabilities for all senses
it should be noted that the above feedback strategy has three main phases step NUM NUM statistical induction of syntactic preference scores step NUM NUM testing phase which is necessary in order to quantify the performance of disambiguation criteria derived from the current statistical distributions step NUM NUM NUM
for the current experimental setup our data show a significant reduction of noise with a significant NUM compression of the data after step NUM and a correspondent slight improvement in precision recall given the complexity of the task see the lexical association performance in table NUM for a comparison
accordingly section NUM is devoted to an experimental analysis of complexity and recurrence of ambiguous phenomena in sublanguages
reaches a kernel of hard cases for which there is no more evidence for a reliable decision
the approach that we have undertaken is to attack the problem of syntactic ambiguity through increasingly refined learning phases
in fig NUM a step NUM a significant improvement in precision can be observed
for r NUM NUM the improvement in recall NUM and precision NUM is more sensible
ambiguity is generated by multiple morphologic derivations and intrinsic language ambiguities pp references coordination etc
the learning steps have then be performed with a threshold value o NUM NUM over the ld corpus
thus bod achieves an extraordinary fold error rate reduction
the hierarchical relations of their linguistic structure are isomorphic with the two other levels of their model intentional structure and attentional state
a result of this pattern is that almost any verb will look like an indicator for these same senses of hard and right
alternatively a second linear constraint could be inconsistent with the first for instance the first might require that the probability of the first point is NUM NUM and the second that the probability of the third point is NUM NUM this is shown in d
all we know is that the expert chose exclusively from among these five french phrases given this computational linguistics volume NUM number i these questions how do we go about finding the most uniform model subject to a set of constraints like those we have described
it thrives on raw unannotated monolingual corpora the more the merrier
bureau de poste post office taux d int ot interest rate compagnie d assurance insurance company gardien de prison prison guard in this table the symbol is a placeholder for either interchange or no interchange and the symbols NUM and NUM are placeholders for possible french words
final decision list for plant abbreviated
plant tissues can be plant and animal life plant is in orlando
step 3b apply the resulting classifier to the entire sample set
a component of the unsupervised algorithm below
step 3d repeat step NUM iteratively
one visual method of determining whether a rift occurs after the french word j is to try to trace a line from the last letter of yj up to the last letter of e if the line can be drawn without intersecting any alignment lines position f is a rift
NUM compare the problem case p to each training case t in the case base and calculate for each pair
the case representation for the rp task creates a minor problem for the cbl algorithm no two instances are guaranteed to have the same features
furthermore using the modified instance representation the case based learning algorithm is able to outperform a set of hand coded heuristics designed for the same task
in theory it allows system developers to use the same underlying case representation for a variety of problems in nlp rather than developing a new representation as each new task is tackled
it simplifies the process of designing an appropriate instance representation for individual natural language learning tasks because system developers can safely include in the baseline instance representation features for all available knowledge sources
each of these marginal constraints translates into an adjustment scaling factor for the cell entries
this method obtained a median accuracy of NUM this is labeled loglinear model in figure NUM
each cell ill the contingency table records the frequency of data with the appropriate characteristics
the interaction terms in the loglinear nmdels represent constraints on the estimated expected marginal totals
the median accuracy for our reimplementation of hindle rooth s method was NUM
this moves the cell entries towards satisfaction of the marginal constraints specified by the nmdel
this resulted in estimates that combine the structural feature directly with the lexical association strength
the results for this strategy are labeled basic lexical association in figure NUM
probabuistic uwm using the probabilistic model that assumes independence between the features
the overall tagging error rate increases significantly as the proportion of new words increases
the ccb will then make a recommendation to the architecture committee as to whether or not the control gate has been satisfied
the tipster pdr and foc control gates will piggy back on the projects control gates to the maximum extent possible
configuration management has this status accounting responsibility where in monthly status reports for changes problems and action items are reported
the ccb is the focal point and the source of direction for implementation of class i changes to the tipster ii architecture
as reviewer the se cm will recommend minor modifications to the rfc to maintain a level of consistency in names and methods
allow for changes proposed on the basis of different versions of the architecture to be combined into a single change
the cmm is also responsible for performing and supporting both formal and informal audits conducted at appropriate milestones during the architecture life cycle
as reviewer the se cm may recommend minor modifications to the rfc to maintain a level of consistency in names and methods
it is expected that tipster applications will require control gates at the time of design and at the time of final delivery
to support the development of this tacad the vendor will demonstrate by inspection module by module compliance with the tipster architecture
null the algorithm model merging induces markov models in the following way
figure NUM shows the log perplexity of the test part during merging
the procedures are shown to be applicable to a transliterated speech corpus
making this explicit we c m view an elementary tree as a ordered pair of the tree structure n l a ordered set NUM of such nodes fi om its frontier NUM e.g. the tree for cooked will be represented s cooked lcb NUM NUM rcb rcb
the structure we derive is shown in fig NUM contractions on the anchor allow the derivation of sluicing structures such as NUM where the conjunct bill too can be interpreted as john loves bill too or as bill loves mary too NUM
in fig NUM the d riw tion graph for sentence NUM accounts or the coordinations of the traditionm nonconstituent keats steals by carrying out the coordination at the root i.e. s conj s no constituent corresponding to keats steals is created in the process of co
conj ml vi s s v cot i i i i cals el times drinks b r oc cats lcb i rcb a drinks derived structure NUM NUM NUM NUM lcb i cookies a beer
findroot returns the lowest node that dominates all nodes in the substitution set of the elementary tree NUM e.g.
the labels on the edges denote the address in the parent node where a substitution or adjunction has occured
another way of viewing the conjoin operation is as the construction of an auxiliary structure fi om an elementary tree
null the conjoin operation then creates a contraction between nodes in the contraction sets of the trees being coordinated
of our NUM NUM word vocabulary we note that the most frequent NUM words are clustered using the main NUM in a worst case analysis the mutual information metric will be o v NUM and we need to evaluate the tree on v occasions ach time with one word reclassified lower order terms for example the number of iterations at each level can be ignored
when applied to the brown corpus excluding the NUM allocated for interpolation and only using n grams up to NUM the model still performs well achieving a perplexity score of NUM NUM adding the extra training text should remove the disadvantage suffered by the weighted average model but at the probable cost of introducing new vocabulary items making the test set perplexity comparisons even more difficult to interpret
finally we note that in the current implementation of the structural tag representation we allow only one tag per orthographic word form although many of the current word classification systems do the same we would prefer a structural tag implementation that models the multimodal nature of some words more successfully
while this may not be appropriate for the designers of every automatic classification system such as researchers whose main interest is in automatic classification in statistical language modeling it has many advantages over qualitative inspection by an expert as an evaluation method which to date has been the dominant method
once the positions of high frequency words has been fixed by the first algorithm they are not changed again we can add any new word in order of frequency to the growing classification structure by making NUM binary decisions should its first bit be a NUM or a NUM
we also studied cases where the algorithm does fail
xtract has been developed and tested on english only input
e maih smadj a c netvision net
sions of the charter and to enforce provisions
consider sentences la and 2a again we would like to construct a clustering algorithm that assigns some unique s bit number to each word in our vocabulary so that the words are distributed according to some approximation of the layering described above that is boys should be close to people and is should be close to eat
guage that satisfy the following two conditions NUM
separating corpus dependent translations from general ones
information retrieval is another prospective application
it is strongest for immediately adjacent collocations and weakens with distance
this research is a step in the same direction
another essential issue is the grain size of description
these values arc a crucial pmt of the lexical mapping lex map from language milts to tmr units included in the senmntics sem struc zone of their lexical entries
each type of lexical entry lctennines a type of mtxlilication relationship between the adjective md the kind of nouns it modifies most significantly whether this relationslfip is property based or not property based
the finest grain size analysis requires that a certain salient property of the modified noun is contextually selected as the one on which the meaning of the noun and that of the adjective is colmected
if formi perfect active and passive are defined as single symbols and if formi perfect maps to cvcvc and if active maps to aa and passive to ui the analyses can be constructed as in figures NUM and NUM
the rules allowed and controlled the variations between the lexical strings and the surface strings being analyzed thus the arabic surface word wdrsl NUM ja could be matched with the lexical string wa daras al among others via appropriate rules
because the upper side string is returned as the result of an anmysis it is often more helpful to define the upper side string as a baseform here a root folh wed by a set of symbol tags designed to represent relevant morphosyntactic features of the attalysis
while it is possible to devise strict roman transliterations of arabic orthography that are unambiguously convertible back and forth into real arabic orthography most existing romanizations are in fact transcriptions that contain more or less information than the original and so represent different orthographical systems
arabic morphology though considerably more difficult than the morphology found in the commonly studied european languages is fully susceptible to finitc state analysis techniques either in an enhanced two level morphology or in the mathematically equiwdent but much more cornputationally efficient xerox finite state format
althongil lexc by itself is largely limited to concatcnative morphotactics just like traditional two level morphology it was noted that the interdigitation of semitic roots and patterns is nothing more or less than their intersection an operation supported in the xerox finite state calculus
because short vowels are seldom written in surface words dvst is also analyzed as the form i perfect passive third person singular which would be fully roweled as dnrisat and as several other forms
stems like davas t r NUM and duris tg4 and especially those like banay based on weak roots are still quite abstract and idealized compared to their ultimate surface realizations
as there was no automatic rulc compiler available to us the rules had to bc compiled into tinite state transducers t y hand a tedious task that often influences the linguist to simplify the rules by postulating a rather surfacy lexical level
for examph daras o happens to be the form NUM perfect active stem based on the root drs ty a with cvcvc being the form passive voweling ui is the parallel passive example
we would also like to thank lynette hirschman aravind joshi and marti hearst for helping us organize the aaai workshop on empirical methods in discourse that provided the impetus for this issue
a survey of recent acl papers shows that the percentage of empirical papers in semantics pragmatics and discourse hovered between NUM and NUM until NUM when it increased to NUM
while this approach led to many theoretical advances models developed in this manner are difficult to evaluate because it is hard to tell whether they generalize beyond the particular examples used to motivate them
the lack of tools greatly increases the cost of accurate coding which could be reduced with coding tools that structure the coder s input and checks that it is within the coding scheme s constraints
third we must increase the number of and representativeness of dialogue and text corpora
while a great deal of progress has been made several obstacles impede empirical research
the discourse community must develop more shared methods tools and resources
second we must develop more shared tools
in NUM and NUM NUM of the acl papers in semantics pragmatics and discourse used empirical methods
in order to develop a large shared resource of tagged materials the discourse community must share efforts across sites
finally all correlations found for global parameters can also be computed based on relative change in acoustic prosodic parameters in a window of two phrases
this is because as discussed above trigrams is essentially acting like baseline in this condition
a probabilistic approach to compound noun indexing in korean texts
where trees s are all the complete parses for an input sentence s
attempting to develop methods for automatically detecting out of domain segments in an utterance see section NUM
by keying on high conlidence words l hoenix takes advantage of the strengths of the speech decoder
figure NUM shows the evaluation results for NUM unseen spanish dialogues containing NUM utterances translated into english
out of lexicon words are ignored unless they occur in specific locations where open concepts are permitted
top level tokens also called slots represent speech acts such as suggestion or agreement
we are also developing a parse quality heuristic for the phoenix parser using statistical and other methods
another approach we are pursuing is to use word salience measures to identify and reject out of domain segments
noise compression is performed essentially by the use of shallow nlp and statistical techniques
clustering the esl s would seem an obvious way to reduce this problem
this figure measures the probability of cooccurrence of two esl s in a collision set
more effort should thus be de voted in evaluating the performance of lexical learning methods in real world noisy domains
in fact the legal and environmental sublanguages are very different in style and not so narrow in scope
l lu ough this paper we showed the multiple steps leading us to tile building of concept clustering knowledge graphs cckgs
for example for a text understanding tusk we can build before hand the cckgs corresponding to one or multiple keywords from the text
the autlnors would like to thank the anonymous referees for their comments and suggestions and petr kubon for his many comments on the paper
table NUM shows the undergeneration probabilities for each of these possible techniques for handling unary productions and n ary productions
in section NUM we first apply an bottom up approach we will determine the kinds of knowledge the generator needs to make intonational choices and based on this we develop a stratified model with three strata grammar semantics and extra linguistic context
montemagni et a l NUM i e derici et al NUM NUM
nn x suffixing a lowel case string specifies the sort of semantic rela tion
mechallisl l kllowll as core extl ac lcb io l
the nature of this similarity va ries from case to case and remains implicit in the ditfcrent groupings
any training set of words from a language is likely to be full of accidental phonological gaps
in the rest of this section we will describe how these generalized transducers are produced and tested
consider again the english flapping rule which applies in the context of a preceding stressed vowel
figure NUM shows the final transducer induced from this corpus of NUM NUM words with pruned decision trees
the system which uses the vector space classifier consists of three main processes as shown in figure NUM
section were achieved with a slightly different method than those for the english data
the ostia algorithm can be proven to learn any subsequential relation in the limit
note that output symbols have been pushed back across state NUM during the construction
this phonological feature knowledge may be innate or may merely be learned extremely early
for example NUM NUM words were with suffix tion in the lexicon of NUM NUM distinct word entries
most of them were transformed into ill formed unl lown words NUM rather than mistaken for other words
table NUM and NUM show the accuracy of the two approaches as a function of the size of test documents
NUM NUM and NUM NUM for the first the third and the fifth generation copies respectively
NUM introduction lexical aggregation aggregation is the process of removing redundant information during language generation while preserving the information to be conveyed
picard enables similar results for the field of text planning by recasting localized means end planning instances into abstractions connected by usage constraints that allow hunter gatherer to process the global problem as a simple constraint satisfaction problem
in this way the cawg may be able to provide timely input to the application developer as to previously debated issues or known work arounds for the difficulty encountered
as a result of the tipster application reviews described in section NUM NUM below a summary matrix will be available as shown in appendix a figure a i below
to the extent possible and in the government s best interest existing code and capability to be incorporated into the tipster application will be re engineered in accordance with the tipster architecture
the following table NUM NUM cm responsibilities and relationships is a matrix of the major cm functions and responsibilities of various management and technical personnel
compliance with the guidelines and procedures specified in this plan is the primary responsibility of the program manager along with the cm manager cmm
the overall tipster text phase ii program organization depicted in figure NUM NUM identifies the relationship of the se cm support function with other tipster components
configuration management personnel also act as the recording secretary for the two project change review organizations the engineering review board erb and the configuration control board ccb
these tasks consist of reviewing analyzing problem reports and change requests and preparing recommendations to effect proposed changes or preparing a statement why the proposed change should not be made to the architecture
the meeting between the union and the company
for example the union met with the company
can this be used to improve inquery s performance
a new rule is then hypothesized in phase n l
the modality structure is the key to comprehending japanese long sentences
hereafter we propose the prosodic control system based on ldg
by text slice NUM ahab has been firmly established as a principal character in the novel and the main key words have
no trend emerges from the proportions of new underdispersed types and tokens third row f NUM in both analyses
the proportion of underdispersed new types as k increases f NUM NUM NUM NUM p NUM
in other words the key figure of moby dick induces a somewhat more intensive use of the key words of the novel
our data supports the difference with respect to the pause length
to answer this question it is convenient to investigate the nature of the new types that arrive with the successive text slices
what we find is that following sentence randomization all traces of a significant divergence between the observed and expected vocabulary size disappear
a predicate or an auxiliary verb correspond to each other
we are now in a position to investigate where underdispersed words appear and how they influence the observed growth curve of the vocabulary
for each word we calculated the proportion of permutations for which the dispersion was lower than or equal to the empirical dispersion
of course if both of these rules are used there will be a lot of ambiguity in these sentences they are just to illustrate the different possibilities
another problem is that since we need to be able to generalize over whole categories we can not as things stand use compilation into terms for feature structures
we encode the set of values as something like lcb be have do anon rcb where anon is some distinguished atomic value standing for any other verb
in english at least this type of conjunction is the only construction for which a kleene analysis is convincing and they can all be described satisfactorily in this manner
have some of the same features e.g. those used for threading gaps then these can be incorporated directly into in our case the vp subcategorization schema rule
this simple device enables us to have a single entry for each preposition while still allowing for it to have multiple senses conditional upon the type of np it combines with
we can formulate this generalization in terms of defaults the default subject for a passive is something unless an explicit agent is present in which case it is that agent
from the basic traditional types of count sg and pl and mass nouns we construct two supertypes sing ular and opt ional det erminer
instead of using a high level grammar formalism we describe mwls with finite state local grammars
john gave a book to mary
a full query is derived from any or all fields in the original topic
generally a more effective stream will more effect on shaping the final ranking
our preliminary results from trec NUM evaluations show that this approach is indeed very effective
the resulting expanded queries undergo the usual text processing steps before the search is run again
unfortunately an average search query does not look anything like this most of the time
will rogers and paul over provided valuable assistance in installing updated versions of prise
the original portion of the query may be saved in a special field to allow differential weighting
NUM nlir retrieval is run using the NUM original short queries
it is more likely to be a statement specifying the semantic criteria of relevance
are there other types of differences among senses that might affect evidence combination
where t denotes a semantic parse tree and ms denotes pre discourse sentence meaning
first a large corpus of atis sentences already exists and is readily available
in content however the parse trees are as much semantic as syntactic
each parse tree t corresponds directly with a path through the recursive transition network
for fields labeled tacit the corresponding field in y is filled with either inherited or not inherited
the statistical discourse model maps a NUM element input vector x onto a NUM element output vector y
this inheritance phenomenon similar in spirit to one anaphora is illustrated by the following dialog
we present a natural language interface system which is based entirely on trained statistical models
figure NUM shows a sample semantic frame corresponding to the parse in figure NUM
in the folk wing secl ion our t ropos d method for aligning lpsor an t nglish sentences is described md l aranmt r
in the experimenl s carried out on NUM NUM english words and its korean translations the proposed method achived NUM NUM in accuracy at phrase level and NUM NUM in accuracy with the bilingual dictionary induced from the alignment
a g h is a hi NUM j z b although the aligmnent algorithm described above with the coml texity of o i NUM mn is simple and c licicnt this algorit hm has the limit alion caused by the assumption of dynanfic programming
p clk ti ltmotcs the alignment atolldates tha l or alculating p clk only constant t ases of a ligntnenl s nt cd to be onsidered in tim prol os d alignnttutt algorithm t ecause most digntnc nl
in equation NUM n and m are the nmnl er of words in the english sentence e and its correspoudil g korean sentence k respectively cj and kl are tit lcb aligtdng unit between l 2nglish sentence e and korean sentence k cj rq resenl s j th word in i nglish sentence and k represents i th word in korean sentence
null lit the example d the houst is gradually iisintegratiug with llg aml c utl ku sl cctiwqy
igure shows how tip l rol lem o1 word unit hismatch can t e dealt wit h in the phrase level aligu lien
the c hinesc t nglish alignmeat consists of segmentation of an inl u hinese sentence and aligning the segmented seltteiic with the c mdidate english selltellce
this shows that the ioglinear model significantly improves the part of speech tagging accuracy of a stochastic tagger on unknown words
it should he noted theft the pr processing l hase is n t ne ssary for the nlort hologieal a na lysev
church and tlanks NUM uses a word window of set size to characterize the context of a word based on the immediately adjacent words
since no large scale corpus data with semantic tags is available the current implementation of ifsm has a word sense disambiguation problem in obtaining class probabilities
mism holds the similarity io be the same NUM eeause in higher lower levels of hierarchy the assertion of c2 adds no information given the assertion of c3
however in contrast to those models here we hope to make the level of the representation of features high enough to capture semantic behaviors of objects
synset v28 is an abstraction of synset v123 and synset v224 which corresponds to chase and run after respectively
because ifsm assumes that some distinctive features exist for c3 sire el NUM and sire el c3 are unlikely to be identical
in the NUM level synset group there is NUM depth gap between capitalist depth NUM and point in time depth NUM
v123 and n5 are synset ids corresponding to word chase and dog respectively these deep triples are sorted and merged
globalpro if the referent of a definite pronoun in cj is mentioned in a previous utterance but not prior to the last time a boundary was assigned else global pro
for our study each narrative was segmented by seven naive subjects as opposed to trained researchers or trained coders using an informal notion of communicative intention as the segmentation criterion
while none of the algorithms approached human performance the fact that performance improved with the number of features coded and by combining algorithms in a simple additive way suggested directions for improvement
the learning NUM decision tree predicts the class of a potential boundary site based on the features before duration cue1 word1 word2 corer infer and cue prosody
note that although not all available features are used in the tree the included features represent three of the four general types of knowledge prosody cue phrases and noun phrases
we will refer to such a row as a bitstring although as we have represented it it is a list rather than a string
where l is the length of a narrative i the actual frequency of cases where n subjects agree in narrative i is multiplied by NUM l where NUM is the average narrative length
each matrix has a height of i NUM subjects and width of j n prosodic phrases less NUM table NUM is a partial matrix of width j NUM
once this set of words is identified champollion iteratively combines these words in groups so that each group is in turn highly correlated with the source collocation
rather there are rules for temporal adverbial modification e.g. at eight o clock locational modification e.g. in chicago and so on
collocations also include rigid groups of words that do not change from one context to another such as compounds as in canadian charter of rights and freedoms
now the desired one to one relationship holds for every derivation in the new stsg there is an isomorphic derivation in the pcfg with equal probability
this is not surprising since each parse tree can be derived by many different derivations the most probable parse criterion takes all possible derivations into account
the minimally edited atis data the differences were statistically insignificant while on bod s data the differences were statistically significant beyond the NUM th percentile
finally we analyze bod s data showing that some of the difference between our performance and his is due to a fortuitous choice of test data
however this is dominated by the computation of the inside and outside probabilities which takes time o rna for a grammar with r rules
furthermore although the maximum constituents parse should not do as well on the exact match criterion it should perform even better on the percent constituents correct criterion
however unlike in the hmm case where the algorithm produces a simple state sequence in the pcfg case a parse tree is produced resulting in addi
improved performance can be obtained by making interpolation parameters depend upon some distinguishing feature of the prediction context
this should be useful for dealing with the range of frequencies of n grams in a statistical language model
an initial structure is built by using the computer s pseudorandom number generator to produce a random word hierarchy
this reasoning also applies to all classes s NUM NUM NUM see figure NUM
let us ignore the fact that some alternations might be capturable by rule and let us also ignore the fact that different semantic properties might be involved
in order to mtomate this normalization we propose to post process parse trees so as to emphasize the dependency relationships among the content words and to infer semantic classes
more complex lml i erns ca n NUM e envisaged e.g.
x linking the sullixed string with the aettl0 exeme in uppercase
from two analogous i atl erns which shave cerl ain a nount of information
let the dispersion di of a word d i be the number of different text slices in which od i appears
at least three possibilities suggest themselves syntactic constraints on word usage within sentences global discourse organization and local repetition
this supports the hypothesis that the key words are primarily responsible for the deviation of the expected vocabulary size from its expectation
for literary studies however the discourse structure of a text is part and parcel of the object of study itself
once used words tend to be used again and this leads to a somewhat higher relative population frequency than expected
in the last step all v n f types sharing the same frequency f have been grouped together
a third question arises with respect to how one s measure of lexical concentration is affected by the number of text slices k
these panels reveal that the expected vocabulary size overestimates the observed vocabulary size for almost all of the NUM equidistant measurement points
the step of the gnf procedure corresponding to step NUM of the ltig procedure converts the cfg at the top of figure NUM into the rules shown in the second part of the figure
the tree u r must not be in i u a if it were there would be multiple derivations for some tree in g one involving u and one involving u and t
the tree t must not be in i u a if it were there would be multiple derivations for some tree in g one involving t and one involving t and u
the correctness of this space bound can be seen by observing that there are only ig n NUM possible chart states x aofl i j
for the tig parser igi is computed as the sum over all the non leaf nodes in all the elementary trees in g of one plus the number of children of
as illustrated in figure NUM substitution inserts an initial tree t in place of a frontier node that has the same label as the root of t and is marked for substitution
step NUM using lemma NUM we first convert g into an equivalent tig y nt i lcb rcb s generating the same trees
many variants of the ltig procedure are possible
the tree u must be different from the trees that are generated by substituting t in other trees u because u contains complete information about the trees it was created from
NUM NUM NUM NUM NUM NUM a b c a b c the reasoning behind this last step is that if all the possibilities are excluded then all the variables will be linked
the values of the ad3 daughters to kleene will be present as the value of kcat and so for all practical purposes this tree captures the kind of iterative structure that was wanted
to compile the value of a particular bool comb feature when in the grammar first using the declarations precompute the set of models i.e. the space of values
the term will have n i arguments where n is the length of the bitstring and adjacent arguments will be linked if their corresponding bitstring position is zero and otherwise not linked
or some convenient abbreviatory notation could be devised
our vp rule schema is exactly as before
the tree for a vp will look like
put this feature specification on the p category
this can grow rather big of course
this technique is used in several systems e.g.
NUM NUM NUM the architecture shall provide an interface to existing standard extraction module evaluation tools
however he opts to treat punctuation marks as clitics on words which introduce additional featural information into standard syntactic rules
NUM NUM NUM NUM NUM verification method demonstration
the language used to specify template schema should be that which is evolving under muc
identical or nearly identical documents may be viewed together or removed from the viewing list
query generation may be done automatically by the application or with help from the user
these include administrative handling of all changes from initial receipt and logging of change requests through issuance of change directives indicating final disposition
lloge aijk j where f NUM NUM is the factor that was used to facilitate fixed point arithmetic
this means that the maximum distance value must be NUM NUM NUM NUM NUM which results in a scaling factor f NUM NUM NUM NUM NUM NUM
experiments ignoring this ambiguity significantly improved the german results as can be seen from a comparison of figure NUM degradation of output vs input corruption
while l he ltser reads a text s he ran sclecl wil h a mouse a u unknown or unfamiliar word
their method is for context free grammars hence it can be applied to finite state recognition as well but it relies on introducing new productions to allow for errors this may increase the size of the grammar substantially
for spelling correction in turkish error tolerant recognition operating with a circular recognizer of turkish words with about NUM NUM states and NUM NUM transitions can generate all candidate words in less than NUM milliseconds with an edit distance of NUM
provide a statement that the application development which initiated a change is in compliance with the architecture or represents a deviation from the architecture
suppose the input sentence is NUM p NUM rcb eniac NUM NUM 3o which means university of pennsylvania celebrates the 50th anniversary of eniac where the words y5 jp NUM transliteration of pennsylvania and eniac the name of the world s first computer are not registered in the system dictionary
ni w w3 z denote the number of tunes the umgram w and the bigram w w appeared in tile jth candidate of tile ith sentence the estimate of the total unigram count c w and the total bigram count c w we can be obtained by summing the counts over all sentences in the corpus
an ideal example to confirm that reestimarion works well would have an unknown word appearing more than twice in the test sentences and it is trivial to extract the word in one appearance while it is difficult in the others because of for example successive unknown words
at each point q in the sentence it sums over the product of the forward probability of the word segmentation hypotheses ending at the point pq wl and the transition probability to the word hypotheses starting at that point p wi l wi
p o can d be cornputed by using the segmentahon model the bayes a posleriori estimate of the word unigram count ci wi and the word bigram count ci wi l wi ill the ith sentence can be computed as
in the part of speech trigram model pos trigram model the joint probability p w t is approximated by the product of parts of speech trigram probabilities p tilti NUM ti l and word output probabilities for given part of speech p wiitl
the first modification is that instead of using the relative frequency in an unsegmented corpus equation NUM and NUM we combine the n gram count in the segmented corpus with the estimated n gram count in the unsegmented corpus to increase estimate reliability
they combined viterbi reestimation using the word unigram model with a post filter called the two class classifier which is a linear discrimination function to decide whether the string is actually a word or not based on features derived from the character n gram in a large unsegmented corpus
marc r llgen david a rushall hnc software inc NUM cornerstone court west san diego ca NUM usa email mri hnc com
status reports for these projects and the other components of the program will be presented at the regularly scheduled tipster workshops
these sessions will start with the phase iii kick off meeting in october NUM and subsequently continue at six month intervals
the primary goal of tipster phase iii is to promote advancements in text processing technologies
to accomplish this goal the tipster program will continue to encourage the cooperation of researchers and developers in government industry and academia to achieve a balanced overall program
architecture goals build an architecture capabilities platform acp implement architecture compliance evaluation and testing procedures for the acp provide acp interoperability for software developers incorporate relevant industry standards evaluation goals continue to support muc and trec enhance the data set improve querying and scoring techniques foster cross pollination of ideas between the two evaluation forums
that reviewl neither constitutes cia authentication of information nor implies cia endorsement of the author s views
now we note that the conditional probability of the most probable parse tree will in general decline exponentially with sentence length
unfortunately while bod provided us with his data he did not specify which sentences were test and which were training
unfortunately the number of trees is in general exponential in the size of the training corpus trees producing an unwieldy grammar
we will call non terminals of this form interior non terminals and the original non terminals in the parse trees exterior
letting g represent grammar size and e represent maximum estimation error bod correctly analyzes his runtime as o gn3e NUM
we also assume that bod made the same choice we did and eliminated unary productions given the difficulty of correctly parsing them
let b k represent a tree of at most depth n with external leaves headed by b k and with internal intermediate non terminals
we will show that subderivations headed by a with external non terminals at the roots and leaves internal non terminals elsewhere have probability NUM a
unfortunately the number of subtrees is huge therefore bod randomly samples NUM of the subtrees throwing away the rest
for all NUM i n the probability of a derivation of this type is 13m n pi the second type of derivation corresponds to substituting the elementary trees rooted with ck in s c1 c and subsequently substituting in the open trees that correspond to literals
a problem is np hard if it is at least as hard as any problem that has been proved to be np complete i.e. a problem that is known to be decidable on a non deterministic taring machine in polynomial time but not known to be decidable on a deterministic turing machine in polynomial time
now we derive both the threshold q and the parameter NUM any parse or sentence that fulfills both the consistency of assignment requirements and the requirement that each conjunct has at least one literal with child t must be generated by n derivations of the first type and at least one derivation of the second type
this type of derivation corresponds to assigning values to all literals of some variable ui in a consistent manner
deriving the probabilities the parses generated by the constructed stsg differ only in the sentences on their frontiers
the probability of an elementary tree with root s that was constructed in step NUM of this reduction is a value pi NUM i n where ui is the only variable of which the literals in the elementary tree at hand are lexical null ized i.e. have terminal children
this is not the case for example when computing the mpd under stsgs for sentence or even a word graph or when computing the mpp under scfgs for a sentence or a word graph
two places to the left of the phrase test one place resp
phrase finding rules a phrase finding rule in our framework is made up of several clauses
the converse is also true as short forms of person names e.g. mr
table NUM performance on the muc NUM named entities blind tcst
what is most encouraging about this approach is how well it performs on so many dimensions
the unary contextual tests in the table may also bc combincd to form binary or ternary tests
olatunji can help identify fitll nanm forms e.g. babatunde olatunji
of these phrases the second successfully triggers the example rule yielding the following relabeled string
documents shall be managed in such a way that ordered and unordered groups can be created
the group of dtlas however was originally designed to handle plain data whereas nouns are structured under a thesaurus
reuse of such criteria can facilitate the building and modification of requests for retrieval and routing
reference resolution other than having the obvious job of identifying the referents of noun phrases also may reinterpret the parser s assignment of illocutionary force if it has additional information to draw upon
while the response does not address the user s intention to go through cincinnati due to the speech recognition errors it is a reasonable response to the problem the user is trying to solve
while this accuracy rate is significantly lower than often reported in the literature remember that most speech recognition results are reported for read speech or for constrained dialogue applications such as atis
note that while the parser is not able to reconstruct the complete intentions of the user it has extracted enough to continue the dialogue in a reasonable fashion by invoking a clarification subdialogue
one way we attain robustness is by having overlapping realms of responsibility one module may be able to do a better job resolving a problem because it has an alternative view of it
however the problems we expected have not arisen
template based generation is clearly inadequate for many generation tasks
the second and third points come from using approx
the rest of the system was built at rochester
these results show that lasa NUM was able to satisfy our primary objectives to solve the two problems mentioned in section NUM weak prediction power and low legibility
these algorithms are now getting keen attention from the natural language processing nlp research community since the huge text corpus is becoming widely available
but the case frame tree will produce two translation errors hakobu for elephant when we classify the original table NUM
this facilitates the translation of japanese time expressions into english
tiffs is efficient because type NUM is the most specific type
this complicates the accurate wanslation of this modifier into english
example NUM june adverbial rain case marking ninth
the valency smlctnre is determined by binding all predicate modifiers to valency elements
see the example in the right bottom of figure NUM
see the example in the right bottom of fignre NUM
illico has been designed from the following two principles NUM modularity in the representation of knowledge defined at the different levels of language processing lexical syntactic semantic conceptual contextual levels NUM sentence composition using partial synthesis and guided composition
in this mode the user is guided while he produces text at each step of the composition of a sentence the system synthesises and displays the words and expressions that can be used to continue the sentence and that will lead to a well formed sentence
the software has been designed as a multi level and multi user system a system flexible enough to be adapted and to respond to specific needs according to the user s skills which depend on his level of language and cognitive development and his degree of autonomy
such sentence stntcturc is called the valency structure
such sentence structure is called tile double subject constrnction
concerning composition within the guided mode we have defined two levels a first one where the whole syntagms are not decomposed and are considered as final expressions of the grammar and a second one where the decomposition is made at the level of the words
in the first case the child has to choose first of all a verb put then if necessary a whole noun phrase the black triangle and then if necessary another whole noun phrase in the stock
we then discussed the maximum entropy principle
another reason is that some character sets are designed to cover multiple languages e.g. iso NUM NUM for several western european languages
it extracts eastern asian character strings presupposing that the given code string has been encoded with the given coding system csys
this implies that chinese zho is the most likely language if we presuppose the original string is encoded with euc gb
since this score exceeds the threshold NUM the eastern asian part is confirmed to be japanese string encoded with euc jis
for example if a document consists of characters in the j is character set the document must be written in japanese
if the potential candidates for the code system are limited the correct coding system may be inferred by using simple pattern matching
for every coding system that can handle eastern asian characters the first loop tries to extract eastern asian characters by using the coding system
if the given code string does not contain such escape sequences the eastern asian part is identified by the procedure shown in figure NUM
the idea comes from the observation that a human can distinguish whether or not a text is written in the language s he can read
which suggests the document is in english may be a german document in ascii format e.g. NUM is written as oe
figure NUM brill s rule sequence architecture as applied to partmf speech tagging
to the theater to go allowed the mother her daughter not the mother did not allow her daughter to go to the theater
this solution also works for verb raising in p cm constructions although the verbal head of the infinitival clause is deeper in the structure
for a rigid collocation such as this one champollion will print for all words in the selected translation except the first one their distance from the first word
scoring of thousands of words of sample data over time revealed that three of the five treebankers had parsing error rates percentage of sentences parsed incorrectly of NUM NUM and NUM respectively while the other two treebankers error rates were NUM and NUM respectively
the result is an extremely specific and informative syntactic and semantic diagram of every sentence in the treebank
crucially for experienced lancaster treebankers the number of such iterations is by now normally none or one
this displays a text segment and tbr each word contained therein a ranked list of suggested tags
to convey domain information one or more dewey decimal system three digit classifiers are associated with each document
this exact l arse or each s mt m e and add it to tlp l recl ar k
with regard both to accuracy and consistency of output analyses individual treebanker abilities clustered in a fortunate manner
will there remain any significant amount of writing say a century from now which is not at some step machinesupported
exhaustive relevant indepth real time analyses will be practically and economically justifiable on as large text flows as the population could possibly type or pronounce
now as computational linguistics will provide the intellectual and operational tools it will be possible to specify and enforce style guides worth the name
typically such regulation refers to low linguistic levels spelling terminology name forms certain phrases and headings document layout c
of course human machine interaction creates new genres we adapt to whoever or whatever is our interlocutor that is a well known feature of our linguistic competence
many private companies publishing houses newspapers and other organizations have an elaborate house policy on style of writing and speaking
automatic language identification has been discussed in the field of document processing
most text processing systems have languagedependent components such a s rules or dictionaries
first we present a few simple categories of contextual predicates that capture any information in b that is useful for predicting a next the predicates are used to extract a set of features from a corpus of manually parsed sentences
for the translation phase we developed an algorithm that avoids computing the dice coefficient for french words when the result must necessarily fall below the threshold
results for the cbl algorithm using all NUM features are shown in the column labeled w o feature selection
the baseline instance representation for the relative pronoun task is similar to the one used for the lexical tagging tasks
in addition we have tested the linguistic bias approach to feature selection on just one natural language learning task
thus far we have implemented just three linguistic biases all of which represent broadly applicable cognitive processing limitations
note that no attachment decisions have been made by the parser these will be made by learning algorithm as needed
w i NUM NUM if ff is missing from the unnormalized problem case w NUM otherwise
to get out of the chicken egg problem we propose an iterative procedure that alternates two operations segmenting text into words and building an lm
this approach is concerned among other things with the cross sentential anaphora
five formal tracks were run in trec NUM a multilingual track an interactive track a database merging track a confusion track and a filtering track
groups could choose to do the routing task the adhoc task or both and were asked to submit the top NUM documents retrieved for each topic for evaluation
where symbolizes the immediate dominance relation between the mother and the list of daughters
the coefficients were learned from the training data in a manner similar to that done in trec NUM but the specific set of measures used has been expanded and modified for trec NUM
the standard vector normalization was used and query expansion was done using the rocchio method to select up to NUM terms and NUM phrases from the top NUM documents retrieved
note however that there was no topic expansion done in the automatic brkly6 run so this improvement represents the results of a good manual topic expansion over no expansion at all
the common thread was that all groups used the same topics performed the same task s and recorded the same information about how the searches were done
the particular sampling method used in trec is to take the top NUM documents retrieved in each submitted run for a given topic and merge them into the pool for assessment
many sets of q1 queries might be built to help adjust systems to this task to create better weighting algorithms and in general to prepare the system for testing
the test collection consists of over NUM million documents from diverse full text sources NUM topics and the set of relevant documents or right answers to those topics
to get out of the chicken and egg problem we propose an iterative procedure that alternates two operations segmenting text into words and building an lm
it is often advantageous to produce the top n parses instead of just the top NUM since additional information can be used in a secondary model that reorders the top n and hopefully improves the quality of the top ranked parse
we calculated gp n p tile first term of formula NUM based on the word numt er coverage of p in the original thesaurus rather than in the partim thesaurus since the original thesaurus usually contains many more words than tile partial alf p has low generality it will have high gp p
expectations can arise from interruptions when the nature of the interruption makes it obvious that there will be a return to the interrupted segment
finally the cache model appears to handle the class of return pops which prima facie should be problematic for the model
the lasa NUM place all classes in input table under root maketree root table maketree node table lcb a class set in table find attribute which maximizes we have implemcnted this algorithm as a package that we called lasa NUM inductive learning algorithm with structured attributes
the cache model was proposed as a computational implemention of human working memory operations on attentional state are formulated as operations on a cache
the dtla takes an attribute value and class table for an input NUM although the table usually includes multiple attributes the algorithm evaluates an attribute s goodness as a classifier independently of the rest of the attributes
for example natural is deleted from a query already containing natural language because natural occurs in many unrelated contexts natural number natural logarithm natural approach etc
changing the weighting scheme for compound terms along with other minor improvements such as expanding the stopword list for topics has lead to the overall increase of precision of NUM to NUM over our baseline results in trec NUM
at the same time it is important to keep in mind that the nlp techniques that meet our performance requirements or at least are believed to be approaching these requirements are still fairly unsophisticated in their ability to handle natural language text
null a selection of NUM topics in this range previously used in trec NUM to trec NUM were run in the routing mode against the disk NUM database plus the new data including material from federal register ir digest and internet newsgroups
in order to cope with this the pair extractor looks at the distribution statistics of the compound terms to decide whether the association between any two words nouns and adjectives in a noun phrase is both syntactically valid and semantically significant
trec NUM new ad hoc queries are far shorter less focused and they have a flavor of information requests what is the prognosis of rather than search directives typical for earlier trecs the relevant document will contain
in our post trec NUM experiments we changed the weighting scheme so that the phrases but not the names which we did not distinguish in trec NUM were more heavily weighted by their idf scores while the in document frequency scores were replaced by logarithms multiplied by sufficiently large constants
schematically these new weights for phrasal and highly specific terms are obtained using the following formula while weights for most of the single word terms remain unchanged weight ti c1 log tf c NUM ix n i idf in the above tx n i is NUM for i n and is NUM otherwise
it has been first necessary to define formally the very notion of ambiguity relative to a representation system as well as associated concepts such as ambiguity kernel ambiguity scope ambiguity occurrence
these however are minor problems with technical solutions
many thanks go to the following people who helped us organize and conduct the testing on summary extraction hideaki takahashi sachiko yoshida jun haga takehito utsuro and takashi miyata
figure NUM types of entities referred
but as soon as we consider languages that allow null pronominmization like italian new extensions to the original model have to be designed in order to deal with pronouns with no phonetic content
the vocabulary of a language is full of accidental phonological gaps
the transducer keeps track of the input symbols seen so far
therefore the entire pruning process is o n3k3
flapping transducer induced with alignment trained on NUM NUM samples
labels with no colon indicate identical input and output symbols
all states of a subsequential transducer are valid final states
cient in size to learn an efficient or correct transducer
we have not attempted to create an ostia like induction algorithm for nondeterministic transducers
the final transducer produced with the alignment algorithm is shown in figure NUM
the exact process used to build the initial tree transducer is described below
this can happen when the words in the confusion set have more than one tag in common e.g. for affect effect rcb the words can both be norms or verbs
as an indicator of the difficulty of the task we compared each of the methods to the method which ignores the context in which the word occurred and just guesses based on the priors
where t tl tn and p ti tl 2ti NUM is the prob ability of seeing a part of speech tag tl given the two preceding part of speech tags ti NUM and ti NUM
a suggestion is allowed to go through iff the ratio of the probability of the word being suggested to the probability of the word that appeared originally in the sentence is above a threshold
context word features test for the presence of a particular word within k words of the target word collocations test for a pattern of up to contiguous words and or part of speech tags around the target word
tag sets are used rather than actual tags because it is in general impossible to tag the sentence uniquely at spelling correction time as the identity of the target word has not yet been established
combining trigram based and feature based methods for context sensitive spelling correction
where t is a tag sequence for sentence w
we then briefly survey the computational formalism underlying the implementation of surgg as well as the syntactic theories that it integrates
but for demonstration purposes figures NUM NUM trace one possible derivation for the sentence i saw the man with the telescope using the part of speech pos tag set and constituent label set of the penn treebank
in the current state of linguistic research such an heterogeneous approach is the best practical strategy to provide broad coverage
the dm the heart of adept is composed of a set of library routines providing a standard interface between adept and the collections of documents in persistent storage
an entry exists for each problem document it contains the document identifier source problem class status mapping template identifier date time stamp etc
the dm is tipster compliant and utilizes open database connective odbc to store document and document relevant information in the sybase system NUM database
at the rose data administrator s discretion documents in the problem queue can be sorted and limited by either source date time stamp problem class mapping template and status
adept provides a friendly user interface enabling data administrators to easily extend the system to tag new document formats and resolve problems with existing document formats
the gui enables a data administrator to see the original document the output sgml template and the fields from which the sgml tags were generated
if while processing dp is unable to identify a required sgml tag validate or normalize its contents the document is identified as a problem document
problem detection and diagnosis adept recognizes problems in the input documents and offers deep diagnostics and suggestions to the data administrator for fixing those problems
from the diagnostics the data administrator can easily determine whether the problem is due to an error anomaly in the data or a change in format
data processing and extraction adept processes both well formed and ill formed data accepting raw documents and parsing them to identify source dependent fields that delineate specific important information
we would also like semantically related words to cluster so that although boys may be near sandwiches because both are nouns girls should be even closer to boys because both are human types
it aetermiues if the character is kept or deleted
almost all type NUM personal names are identified correctly
NUM NUM NUM NUM clue NUM title the first is title
if both have high weights both are chosen
in the political section there are many titles
it has NUM NUM chinese personal names and NUM NUM characters
the reader s attentional state is recorded in two stacks the centers tlistory stack and the backwatq centers stack collecting respectively the cf and the cb of the already produced utterances
for instance in the sentence yesterday at home peter threw himself on the dessert like a lion the subject inherits the properties of speed and voracity of a lion attacking its victim
as a result the basic cbl algorithm for lexical tagging tasks was augmented with a decision tree algorithm whose job it was to discard irrelevant features from the case representation
in a typical document a single entity may be referred to by many name variants which differ in their degree of potential ambiguity
in addition as the heuristics depend on linguistic conventions they are language dependent and need updating when stylistic conventions change
once these boundaries have been established there is another type of well known structural ambiguity involving the internal structure of the proper name
in this minimal sense nominator uses the larger context of the document collection to learn more variants for a given name
proper nouns differ in their linguistic behavior from common nouns in that they mostly do not take determiners or have a plural form
since obtaining and maintaining a name database requires significant effort many applications need to operate in the absence of such a resource
efforts to resolve it have traditionally focussed on the development of full coverage parsers extensive lexicons and vast repositories of world knowledge
it leaves capitalized sequences like minimum alternative taz annual report and chairman undetermined as to whether or not they are names
next splitting heuristics are applied to all candidate names for the purpose of breaking up complex names into smaller ones
before the text is processed by nominator it is analyzed into tokens sentences words tags and punctuation elements
null finally the viability of both the linguistic bias approach to feature set selection and the general cbl approach to natural language learning must be tested using much larger corpora
this paper describes a prototype disambiguation module kankei which was tested on two corpora of the trains project
one is to use the same kinds of full and partial pattern matching in training as are used in disambiguation
thus experiments included trials where the NUM NUM dialogs were used to predict the NUM dialogs NUM and vice versa
weighting the n grams in a nonuniform manner should improve accuracy on the trains corpora as well as in more general domains
one hope of this project is to make generalizations across corpora of different domains
the ability to adjust probabilities based on evidence seen is an advantage over rule based approaches
however the intuition is that one source of evidence is insufficient for proper disambiguation
the only requirement is that patterns have at least two items a preposition or adverb and a verb or nphead
the frequency with which np and vp attachment occurs for these patterns is totaled to see if one attachment is preferred
contextual predicates are functions that check for the presence or absence of useful information in a context b and return true or false accordingly
NUM r run r past r argl r j name j john furthermore it is a complete sentence
we translate this recency bias into representational changes for the training and problem cases in two ways
e.g. the lamps near the paintings of the house that was damaged in the flood
next we present a technique for automating feature set selection for case based learning of linguistic knowledge
this paper addresses the issue of algorithm vs representation for case based learning of linguistic knowledge
we believe however that it offers a generm approach for case based learning of natural language
hence the baseline case representation is parser dependent i.e. nlp system dependent rather than task dependent
first incorporate any bias that relabels attributes e.g. r to NUM labeling
nineteen of the NUM cases have antecedents that include the often distant subject of the preceding clause
for example on the classic proper names task in mixed case text we havc achieved good results starting from runs of lexemes tagged with nm or m ps the penn treebank proper noun tags
indeed short name forms e.g. detroit diesel can sometimes only be identified correctly once their component terms have been found as part of the complete naxne e.g. detroit diesel corp
here we will restrict ourselves to the description of the system networks reflecting the choices in tone
can be either interpreted as a question tone 2a or a statement tone la
section NUM concludes the paper with a summary and a number of questions that have been left untouched
intonation as realization of interactional features thus draws on discourse and user model as the source of constraints
cor is a task independent model based on searle s speech act theory NUM
together the information from these input sources controls the traversal of the grammar see section NUM NUM
tonicity the placing of the tonic syllable i.e. its position within the tone group
specification of a clause see for instance NUM NUM NUM
NUM the relation between mood and tone is potentially many to many with one exception imperative is always realized by tone NUM
the same is true for the classification of intonation which was developed by pheby in the late sixties
at any given locus a test may either search for a particular lcxcmc match a lexeme against a closed word list match a part of speech or match a phrase of a given type
brill s rule learning algorithm he search for a rule sequence in a given training corpus begins hy first applying the initial labeling function just as would be the case in running a complete sequence
later top down clustering would operate on these word groups as if they were words
the evaluation is performed on the NUM most frequent words of the lob corpus
the third option is to derive a fully automatic word classification system from untagged corpora
the smoothed structural tag model successfully predicts the original utterance as the most likely
the best of these two level systems is the NUM plus NUM model which scores NUM
a probabilistic language model should assign a relatively low probability to the third sentence
nonprobabilistic models while theoretically well grounded so far tend to have poor coverage
one main engineering application that can use word classes is the statistical language model
null for comparison we calculated some test set perplexities of other language models
for example the legend e1 NUM means that text of the domain e1 office environment was used with a first order hmm for the experiment
when we arrive at the end we only need to keep the e globally highest probabilities and trace back the states that resulted in these
moreover it must be noted that the success rate of the system although already good enough can be further improved by better training on a larger corpus of selected texts
spelling in dutch is rather straightforward for etymologically dutch words but words of foreign origin are usually spelled as in the lauguage of their origin
with greek the model behaved quite well reaching more than NUM success for the second order hmm experiments with up to four output candidates
the model behaved best in experiments that used the newspaper corpora which are more casual in style and richer in vocabulary than the other domains
this way it is guaranteed that there will be no case where a sequence of graphemes produces a sequence of phonemes of a different length
initially a system based on a first order hmm was implemented and the results of its evaluation detailed in section NUM were promising
these include the basic hidden markov model theory the viterbi algorithm the n best algorithm and the solutions used to make the ptgc system fast and efficient adequate for real time applications
every grapheme is usually pronounced in the same way i.e. corresponds to one phoneme and every phoneme usually has more than one possible spellings regardless of its neighboring phonemes
to answer these questions we present a variety of kinds of analysis from vocabulary distributions to perplexities on language models
the bilingual glossary is essentially a phrasal dictionary a glossary entry contains a source phrase pattern a set of corresponding target phrase patterns and correspondences between variables in the source and in the target patterns figure NUM
moreover it is fairly easy to enhance the glossary when new texts are being processed these new texts can be added to the corpus and the corpus can be processed again to provide a new list of potential glossary entries
the translations produced answer the need for fast multilingual machine translation capabilities as required in information processing environments because the linguistic components of the system are derived from the very texts undergoing translation and analysis in the system
reuse of mrds bilingual dictionaries that are used for the wordfor word fall back translation are processed versions of various mrds NUM e.g. the spanish english collins dictionary figure NUM or of other mt dictionaries that have been restructured according to temple own dictionary structure
the term contraction is taken from the graph theoretic notion of edge contraction
lcb NUM rcb or o cooked d NUM
first it uses only the traditional operations of substitution and adjnnction
3we sh ll assume there are no adjunction constraints in this paper
each node in the derivation tree is the name of an elementary tree
we add a new rewriting operation to the ltag formalism called conjoin NUM
we assume hat the anchor can not be involved in a build contraction
figure NUM tree for a ditransitive verb in ltag
figure NUM moving the dot while recognizing a conjoin operation
flammia has proposed a method for generalizing the use of the g coefficient for hierarchical segmentation that gives an upper bound estimate on inter labeler agreement
the head and the modifiers included in the noun phrase have to allow the identification of the entity among all the ones active in the reader s attention potential distractors
the task of selecting the correct modifiers for a non anaphoric expression is not an easy task since in the knowledge base attributive and distinguishing restrictive properties are mixed
e is in a question use a singular indefinite noun phrase e e is used in procedural descriptions null italian german use a definite phlrm description
NUM as an hpsg theory is assumed to be a set of constraints that describe well formed descriptions of linguistic objects this is clearly not wanted
c ein m irchen erz ihlen wird er seiner d seiner tochter erzghlen wird er das mgrchen
a minor change in the feature geometry of signs was sufficient to cope with the spurious ambiguity problem of pollard s to appear account
hinrichs and nakazawa have to block this case by stating type constraints 4in the original grammar i use a binary branching schema for head complement and verb cluster struclures
the licensing daughter has licensing function only and is not inserted into the domain of the resulting sign at this point of combination
if a vert al omi lex is build two verbs are combined and the resulting sign inher its all arguments from both verbs
NUM well er ihm ein m irchen because he him a fairy talc erz hlen lassen hat
the existential quantifier is qualified as an externally dynamic quantifier
this will be done on the basis of an information passing framework
usage most people eat cereal with milk in a bowl
NUM he knowledge acquisition is performed on a children s first dictionary
contains a limited nnmber of highly connected words giving usefid information about a particular domain or situation
those temporary graphs express similar or related ideas in different ways and with different levels of detail
ideas cart be expressed in many ways and we therefore need a more relaxed matching schema
NUM john makes a nice drawing on a piece of paper with the pen
cross sentential anaphora NUM if a farmer owns a donkey he beats it
771e merchandise was recorded with the number NUM NUM
the direct generator has two functions NUM planning the text in direct mode top down anti NUM generating more or less fixed expressions or non null linguistic texts i.e.
although there are more research and industrial projects in analysis than in natural language generation generation has great potential since the gains in terms of quality and productivity largely justify the investment
the other criteria were marked out of NUM
NUM helle that you will forgive us or
the choices made depend on certain attributes e.g.
cc ddlai sera un peu plus long que prdvu
the hlst section analyses tile results of tile assessment
recall that grammar specialization in general trades coverage for speed
finally the specialized grammar is used to search for full parses
we have found this to be an effective way of evaluating system performance
this is enforced by imposing the following dominance hierarchy between the possible categories
first the lexicon and morphology rules are used to hypothesize word analyses
the right bigram score as above but considering right neighbors
similarly the maximum constituents parse is also derived from the sum of many different derivations
crossing brackets zero crossing brackets and the paired differences are presented in table NUM
in method continued a single new non terminal is introduced for each original non terminal
we can create a subtree by choosing any possible left subtree and any possible right subtree
we can compute this probability using elements of the inside outside algorithm
figure NUM shows pseudocode for a simplified form of this algorithm
null now assume that the theorem is true for trees of depth n or less
for every stsg subderivation there would be an isomorphic pcfg subderivation with equal probability
the algorithm randomly samples possible derivations then finds the tree with the most sampled derivations
algorithm for parsing using the model however he did discover and implement monte carlo approximations
the mcpi is thus employed to remove the less plausible attachments proposed by the grammar with a consequent reduction in size of the related collision sets
we assign every node in every tree a unique number which we will call its address
this paper aims to analyze word dependency structure in compound nouns appearing in japanese newspaper articles
near correctness NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
NUM the approved translation pairs are registered in the translation dictionary until no new pair is obtained then the threshold value fmin is lowered and the steps NUM through NUM are repeated until fmin reaches a predetermined value
the most plausible correspondences are then identified using the similarity values so calculated null a for an english word sequence we let wj lcb wjl wj2 wjn rcb be the set of all japanese word sequences such that sim wji we log2 f i
it is noticeable that the pairs with high frequencies give very accurate translation in the cases of the computer manual and the business letters whereas the scientific journal does not necessarily gives high accuracy to highly frequent pairs
in the current setting all words and word sequences of two or more occurrences are taken into account
we is calculated where wj and we are japanese and english word sequences and fj fe and fie are the total frequency of wj in the japanese corpus that of we in the english corpus and the total co occurrence frequency of wj and we appearing in corresponding sentences
the fact that no word sequence occurring less than frnin times can not yield greater similarity value than log NUM frnin assures that all pairs of word sequences with the occurrence more than fmin times are surely taken into consideration
NUM word selquenc e extraction i NUM setting of minu um occurence condition ranslation i NUM extraction of translation canditates re er o NUM similarity calculation threshold decrement
p e l f mi ej f log p ej probabilities p ej i f and p ej are calculated from parallel corpus by counting the occurrences
we propose a method of finding corresponding translation pairs of arbitrary length word sequences appearing in parallel corpora and an algorithm that gradually produces good correspondences earlier so as to reduce noises when extracting less plausible correspondences
the second set c2 is a set of NUM collocations similarly selected from the set of approximately NUM NUM collocations identified by xtaact on all data from NUM
these source collocations contain both flexible word pairs which can be separated by an arbitrary number of words and fixed constituents such as compound noun phrases
we describe the measure that we use and then provide a detailed description of the algorithm following this with a theoretical analysis of the performance of our algorithm
other languages could be easily imlflemented in the overall skeleton of i ss i iui lcb uc
in other words if we find ourselves tagging the frame elements of inheritance in that same sentence the phrase george s cousin would be tagged as an heir in that frame
categorization as a function of the size of test docnments
these ill formed words were ignored in our sinfilarity measurement
on the other hand in the word shape token based approach with the first and the third generation copies n NUM NUM the test documents were similarly large enough
as shown in table NUM the accuracy of the word shape token based approach for higher quality images n NUM NUM was nearly equal to that of the ocr based approach
in section NUM with the use of a topic tagged document image database we show the word shape token based approach is quite adequate for content oriented categorization in comparison with a conventional ocr based system
too figure NUM text line parameter positions above and comlected components below fable NUM character shape code membership character menlbers shape code a a zbdfhklt0 NUM
table NUM accuracy of the word shape token based
the inaccuracy of ocr can be largely mitigated
in the ocr based approach it removes stop words
other attributes such as original language or code set may be used to control the internal tipster processing
see appendix a for a list of the most common attributes that might be used by an application
these groups may be comprised of sets with various characteristics such as source publisher or discourse
the interface control document is the defining document identifying specific inputs and outputs for tipster components and modules
depending upon the application as soon as a document has been routed it must be available for retrospective search
the statistical linguistic and semantic analysis performed by detection and extraction may be considered the key work of tipster processing
template objects and patterns may be selected modified and re combined or attached to different templates to establish new extraction criteria
templates in the library shall be composed of template objects fill rules and patterns associated with them
the purpose of this library is to provide patterns strings for use in building new template filling capability
it seems obvious to us that progress in dialogue systems is intimately tied to finding suitable evaluation measures
thus changing only the closed class forms of the example sentence could yield a new sentence like will the lassoers rustle a steer
for example the closed class preposition across prototypically designates a spatial schema consisting of two parallel lines and a perpendicular path from one to the other
this system is currently being extended to handle multiple internet and for fee database protocols resulting in a useful system for information retrieval from heterogeneous information sources
a good example is the use of first and rest attributes to represent list structured features such as syntactic arguments and subcategorized complements
the paper is organized as follows section NUM uses an analysis of english verbal morphology to provide an informal introduction to datr
the sequence of atoms on the right hand side of this equation is just a sequence of atoms as far as datr is concerned
sens l s knowledge ba se ki3 is then described l ogel her with the fimcdon which projects mnbiguous soas onto kb in the search for the best ca ndidate a mdogue pwo liffere nl
we observed above that when inheritance through a global descriptor occurs the global context is altered to reflect the new node path pair
thus after love mor present has inherited through verb mor root the global path will be mor root rather than mor present
if p2 is the only path defined at n which p1 extends then p1 takes its definition from the definition of p2
however in practice this has no useful consequences due to interactions with the default mechanism see section NUM NUM below
that is to local inheritance values and global descriptors are one and the same and are inherited through the network
descriptors nested within definitional expressions are treated independently as though each was the entire value definition rather than just an item in a sequence
at the foc control gate the following are expected to be put in the tacad and under tipster cm control NUM tipster application as built documentation
proper names are a case in point since their syntactic distribution partially overlaps that of noun phra ses in general as this overlap is only partial name analysis within a full context free grammar is cumbersome and some approaches have taken to include finite state name parsers as a front end to a principal context free parsing stage jacobs et al
initially we made use of a simple arithmetic difference metric y s wimrc y for yield is the number of additional correct phrase labelings that would be introduced if a rule were to be added to the rule sequence and s for sacrifice is the number of new mistaken labelings that would bc introduced by the addition of the rule
also measured is the identification of some numeric expressions money and percentiles dates and times
our lowest score is on organizational names but note that the system lacks any extensive organization name list
the learner had access to the same predefined word lists including the less than perfect tu s tmr gazetteer
as a result we had to rely ahnost entirely on the learning procedure to acquire any rule sequences
we have only reported here on nature finding tasks but early invcstigations in other areas arc encouraging as well
to the left of the phrase is the word og tothe left of that is an unlabelled phrase merge the entire left contextinto the oizg phrase and all the first two clauses of the rule are antecedents that look for phrases such as america inc
ionsidcring only lexical rules those that look for particular words this means that there are as many as i8 NUM possibh unary lexical rules NUM x NUM rule schemata mad iz NUM NUM binat t lexical rules o77 z x NUM simple bigram rule schemata in the search space
the system consists of four steps step NUM this step provides all possible word groupings with all possible tags
the final solution for the above example is t ll l qn lcb he pron
thanks are also due to patcharee varasai supapas kumtanode mukda suktarajarn for their linguistic helps
example of the syntactic coarse rules for a set of two consecutive words wi wi l in thai grammar is given as follows if wi is noun then wi z might be noun verb modifier ifwi is verb then wi s might be noun postverb rood
the former will be processed at this stage by looking up the preferred words see table NUM
the problem solver realizes that enginel is n t currently in detroit so this ca n t be a route extension
each token of a sentence in the sentence bank is connected back to the spl plan template associated with it i.e. the templates for a particular sentence and for its components are directly accessible from the sentence bank
however early in the construction of splat it was noted that processes should be divided into two categories those which modify the ideational content of a sentence and those which dictate the textual structure of a sentence
from a lower initial figure coverage of sec unseen corpus increased by a larger factor
one particular problem with the crossing bracket measure is that a single attachment mistake embedded n levels deep and perhaps completely innocuous such as an aside delimited by dashes can lead to n crossings being assigned whereas incorrect identification of arguments and adjuncts can go unpunished in some cases
precision is less than NUM due to crossings minor mismatches and inconsistencies due to the manual nature of the markup process in tree annotations and the fact that susanne often favors a flat treatment of vp constituents whereas our grammar always makes an explicit choice between argument and adjunct hood
integrating the text and the pos sequence grammars is straightforward and the result remains modular in that the text grammar is folded into the pos sequence grammar by treating text and syntactic categories as overlapping and dealing with the properties of each using disjoint sets of features principles of feature propagation and so forth
since our evaluations indicate that our system achieves a good level of accuracy with little treebank data and that NUM NUM coverage was achieved for english quite early in the grammar refinement effort porting the current system to other languages should be possible with small to medium sized treebanks around 20k words and feasible manual effort of the order of NUM person months for grammarwriting and treebanking
on this set the system achieves a zero crossings rate of NUM NUM mean crossings NUM NUM and recall and precision of NUM NUM and NUM NUM respectively with respect to the original susanne bracketings
detecting a metaphor is meaningless here and conventional metaphoric meanings can be viewed as polysemies
for example the linguistic category of reality status can involve inflections that represent a proposition as factual conditional potential or counterfactual
to give a single illustration the two imaging systems of perspective point and distribution of attention can both be exemplified together with a contrasting pair of sentences
the fact that the closed class component is limited to only a select set of concepts and conceptual categories accords it a specific and critical functional role in language
the closed class component utilizes its relatively small conceptual inventory to structure the remainder of conceptual content such as that which can be expressed by the open class forms
thus the structuring of a visual scene appears to rely greatly on the perception of bilateral symmetry of rotation and of dilation expansion contraction
but there is little evidence that the visual perception of a scene involves a structuring or classifying of its contents in terms of such ascriptions of reality status
for a given vocabulary v the mapping t initially translates words into their corresponding unique structural tags
linguistic topology here resembles mathematical topology in that the preposition in is magnitude neutral it occurs equally well e.g. in in the thimble and in the volcano
but the category of affect is found to be quite low in the inventory and the specific notion of hate seems to be absent
to illustrate in present day enghsh the verbs keep and hate as in i keep skiing and i hate skiing are both regular open class forms
a corpus based analysis we made showed the existence of textual clues in relation with the metaphors
the user interface of the temple workstation includes a collection document browser the tipster editor for documents a genetic translation function access to lexical resources and contextsensitive help figure NUM
the authors wish to thank adina miller and wiley harris of ord and ron dolan of the library of congress for their help in the organization and presentation of the data provided in this paper
the result is a meaningfifl translation but can have a telegraphic feel
the mt module is composed of two separate translation sub modules which operate independently
when more than one grader is used the results are averaged together
english yes what do you think NUM could meet tuesday
another active research topic is the automatic detection of out of domain segments and utterances
acceptable is the sum of perfect and ok sentences
figure NUM september NUM evaluation of ilr combined with phoenix
pected this result strengthens our belief in the potential of this approach
the second is the phoenix module designed to be more robust
the first is the glr module designed to be more accurate
also people are not very good in explaining the contexts in which a word may be used or in explaining the difference between two words
hence lexical information is pre sented in a hypertext lashion i.e. the user can jump from one information to another b the system has a mechanism for example retrieval
morphological output from qjp an example of segmented morphemes with morpheme tags are shown in figure NUM where NUM nouns NUM NUM etc and NUM stems of word t maxked by zj in kanji character are recognized using allocation rules and connection table
in section NUM we discuss feature selection and present an automatic method for discovering facts about a process from a sample of output from the process
it can be shown that p is always well defined that is there is always a unique model p with maximum entropy in any constrained set c
safe is a vague term one might for instance reasonably define a safe segmentation as one which results in coherent blocks of words
on the left are phrases the model strongly prefers not to interchange such as somme d argent abus de privil ge and chambre de commerce
these words are then ordered to give a french sentence f we denote the ith word of e by ei and the jth word of f by yj
it is not hard to incorporate the maximum entropy word translation models into a translation model p fie for a french sentence given an english sentence
together the five constraint templates allow the model to condition its assignment of probabilities on a window of six words around e0 the word in question
in the routing task it is assumed that the same questions are always being asked but that new data is being searched
the ready availability of more federal register documents suggested the use of topics that tended to find relevant documents in the federal register
trec NUM presented a continuation of many of these complex experiments and also included a set of five focussed tasks called tracks
the conferences included evaluation not only of the tipster contractors but also of many information retrieval groups outside of the tipster project
the difference was that the results submitted for the filtering runs were unranked sets of documents satisfying three utility function criteria
both the main tasks were more difficult the test topics were much shorter and the test documents were harder to retrieve
in this method a pool of possible relevant documents is created by taking a sample of documents selected by the various participating systems
however participants in trec NUM felt that the topics were still too long compared with what users normally submit to operational retrieval systems
these guidelines deal with the methods of indexing and knowledgebase construction and with the methods of generating the queries from the supplied topics
in trec NUM four out of the five tracks were run as preliminary investigations into the feasibility of running formal tracks in trec NUM
our objective in developing functionality 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
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
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
trw has developed a text search tool that allows users to enter a query in foreign languages and retrieve documents that match the query
a single query can contain words and phrases in a mix of different languages with the foreign language terms entered using the native script
display of search results in native scripts including japanese chinese korean arabic cyrillic thai and vietnamese
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 a number of languages and retrieve documents that match the query
some of our key innovations include search and retrieval of multi lingual data using queries specifying search terms in different languages and encoding sets
the fifth important difference between the ltig and gnf procedures is that the schabes and waters tree insertion grammar output of the ltig procedure can be represented compactly
each instance u of one of the new trees introduced is replaced by one or more instances of t substituted into the appropriate tree u e i u a
second tig prohibits adjunction on the roots of auxiliary trees and allows simultaneous adjunction while tag allows adjunction on the roots of auxiliary trees and prohibits simultaneous adjunction
step NUM convert every auxiliary tree t in a into an initial tree as follows let a i be the label of the root of t
otherwise add a new root labeled zi with two children on the left and on the right a node labeled zi and marked for substitution
note that there are eight ways of substituting an a1 or a2 rule into the first position of a z2 rule but they yield only four distinct rules
to see this note that the elimination of empty rules required when converting an ltig into a gnf can cause an exponential increase in the number of rules
the problem is does the scfg generate a sentence with probability q for the word graph w g lcb t f rcb 3m
thus there is no sentence parse that has a probability that can result in a yes answer the answer of mppwg and mps is no
yes if ins s answer is yes then there is an assignment to the variables that is consistent and where each conjunct has at least one literal assigned true
for the resulting stsg to be a probabilistic model the probabilities of parses and sentences must be in the interval NUM NUM
if the left most open tree n of tree t is equal to the root of tree tl then t otl denotes the tree obtained by substituting tl for n in t
implicit in this but crucial the different occurrences of the literals of the same variable must be assigned values consistently
and finally the assignment of true false to ui is modeled by creating a child terminal t resp
but when the grammar size is large and the comparison is between NUM seconds s and NUM seconds for a sentence of length NUM then things become different
no ifins s answer is no then all possible assignmcnts are either not consistent or result in at least one conjunct with three false disjuncts or both
bustly integrates any kind of information obviating the need to screen it first
thc default uke selection is the nearest possible uke bunsctsu and if nccessazsr q rcb p substitutes the selcetion NUM ascd on rules comparing the two pairs the currcnt selected ukc bunsetsu and a more distant possible uke bunsetsu for thc subject kakaribunsctsu
in figure NUM the root forms of inflected words have been derived and are shown in the parentheses such us which is the root form shuushi kei of
with our deterministic transducers the transducers are joined via composition
one simple method is coordinate wise ascent in which is computed by iteratively maximizing q one coordinate at a time
from the perspective of numerical optimization the function NUM is well behaved since it is smooth and convex in
over the past few years we have used candide as a test bed for exploring the efficacy of various techniques in modeling problems arising in machine translation
in this paper documents are always assigned to a single category
alternative sentence specificati0ns result in different expressions of the same information for instance including more or less detail changing the speech act or changing the textual status of various entities
thus two different philosophical approaches maximum entropy and maximum likelihood yield the same result the model with the greatest entropy consistent with the constraints is the same as the exponential model which best predicts the sample of data
candide estimates p e the probability that a string e of english words is a wellformed english sentence using a parametric model of the english language commonly referred to as a language model
our model p of the expert s decisions assigns to each french word or phrase f an estimate p f of the probability that the expert would choose f as a translation of in
this method computes the solution a of an equation g a NUM iteratively by the recurrence NUM with an appropriate choice for a0 and suitable attention paid to the domain of g
we constructed a maximum entropy model pin ylx by the iterative model growing method described in section NUM the automatic feature selection algorithm first selected a template NUM constraint for each of the translations of in seen in the sample NUM in berger della pietra and della pietra a maximum entropy approach table NUM maximum entropy model to predict french translation of in
with this information in hand we can impose our first constraint on our model p p dans p en p h p au cours de p pendant NUM this equation represents our first statistic of the process we can now proceed to search for a suitable model that obeys this equation
the first column gives the identity of the feature whose expected value is constrained the second column gives al f the approximate increase in the model s log likelihood on the data as a result of imposing this constraint the third column gives l p the log likelihood after adjoining the feature and recomputing the model
for example noun de noun phrases ending in intdrot are sometimes translated word for word as in conflit d intdrot conflict of interest and are sometimes interchanged as in taux d intdrot interest rate
they usually denote reading matters but not organizations
the pos matrix pm given below is used to implement the finite state automaton model of the syntactic coarse rules where syntactic category of wi is cati and wi l is cath
one of the important requirements for developing practical natural language processing system is a morphological analyzer that can automatically assign the correct pos part of speech tagging to the correct word with time and space efficiency
at this stage the syntactic coarse rules are used for pruning the remaining erroneous word chains caused by the word boundary ambiguities tagging ambiguities and or implicit spelling errors
frdm the result of the experiment the proposed model can work with time efficiency and increase the accuracy of word boundary segmentations pos tagging as well as implicit spelling error correction
our preliminary experiment shows that the proposed model can work with a time efficiency and increase the accuracy of word boundary and tagging disambiguation as well as the implicit spelling error correction
from the result of our experiment NUM NUM words can generate NUM NUM implicit spelling error words where NUM NUM of those errors have new syntactic categories
however classifier will be pruned since it violates the syntactic coarse rule that classifier could not be an initial word
from the experimentation results while a corpus based approach has proven to be efficient the method seems to be computationally costly and requires a large amount of training data and validation data
instead of using a corpus based approach which requires a large amount of training data and validation data a new simple hybrid technique which incorporates heuristic syntactic and semantic knowledge is proposed
similar two dummies are added to the target sentence
similarly the right dummies align with each other
organic producls o1 lmman origin for medical use
table NUM rules acquired from bilingual definitions for NUM
lc zhuotian wuo budao yitiao yu
NUM NUM mutual information and frequency gale and
translation process tends to preserve contiguous syntactical structures
e similarity between connection target and dictionary translations
i e i caught a fish yesterday
various factors used to evaluate connections are also given
conclusion we investigated model merging a technique to induce markov models from corpora
for example if one relation and one object can be said to describe the features of object we can define one feature of human as agent of walking
in contrast the level NUM groupings contains natural language weapotf head chief and point in time which seems to be a reasonable basis for feature description
this means that sim c0 c1 located in higher part of the hierarchy is expected to be less than sim c2 c3 located in lower part of the hierarchy
for example even if a sentence a man bit a dog exists in the corpus we can not declare that biting dogs is a general property of man
more than NUM million words of news articles have been parsed using a multi scale parser and stored in the corpus with mutual references to news article sources parsed sentence structures words and wordnet synsets
one of the advantages of this method is that it is expected to give a grouping based on the quantity of information which will be suitable for the target task i.e. semantic abstraction of triples
the degree of abstraction i.e. the number of groups is one of the principal factors in deciding the size of the feature space and the preciseness of the features power of description
in our implementation we introduce a new grouping method called the flat probability grouping method in which synset groups are specified such that every group has the same class probabilities
similarity methods can be broadly divided into relation based methods which use relations in an ontology to determine similarity and distribution based methods which use statistical analysis as the basis of similarity judgements
but this method posed problems that so large volumes of fractional and unnecessary expressions are extracted that it was impossible to extract interrupted collocations combining the results
if the sentence number is given to every combination list of nes the sentences corresi onding to the extracted interrupted collocation can easily be identified
as a result it has been made possible to easily calculate interrupted collocations and together with phrase templates and other basic data regarding sentence structure
in contrast the method proposed in this paper extracted NUM NUM millions types of substrings and a total frequency of them has reduced to NUM NUM millions
in this experiment the turnaround time was NUM or NUM hours where components of collocations to be extracted was limited to the substrings with the frequency of NUM or more times
to simplify matters we first assume that the substrings which have any kinds of punctuation mark as a part of them are not extracted in the procedure of uninterrupted collocation extraction
and under the condition that fractional substrings are restrained to be extract a new method of automatically extracting and tabulating all of the uninterrupted collocational substrings has been proposed
since character recognition systems are under the control of applications engineers the objective of this work is to provide well tbrmed data and evaluation criteria for those recognition systems
the experiments revealed NUM accuracy for the test set of NUM NUM word phrases out of NUM NUM training data and NUM NUM tbr NUM NUM untrained test data
in linguistic viewpohlt these include language ornialisms text corpora and statistical int ormation of a language
first text corpora voice and handwritten scripts dbs dictionaries and a set of terminological dbs constitute the information base
major sources of the corpus inchlde books magazines and newspapers up to date three million word phrases are gathered
because the output of morphological analysis is rather complex due to the characteristics of korean the use of a tagger to reduce ambiguities seems important for further processing
from the cognitive engineering point of view the research focuses on the structure of korean alphabets fonts command structures and interdisciplinary works of cognitive science
side the technology covers information interchange and compression techniques basic techniques of artificial intelligence such as knowledge representation searching and tools for manipulating korean alphabets
categorized corpus korean verbs and adjectives are classified into over seventy categories and a set of sentence styles are investigated for NUM basic verbs of those categories
these probabilities were derived from a corpus of NUM sentences of read speech from the wall street journal and are shown to be a reasonably close match to probabilities from phonetically hand transcribed data timit
finally we hope in future work to be able to combine our rule based approach with more bottom up methods like the decision tree or phonological parsing algorithms to induce rules as well as merely training their probabilities null
to produce the initial rule probabilities we need to count the number of times each rule applies out of the number of times it had the potential to apply
for expository simplicity we have made the incorrect assumption that consonants have a duration of i frame and vowel a duration of NUM or NUM frames
a text to speech system was used to gen2although it was not relevant to the experiments de null scribed here our lexicon also included two sources which directly supply surface forms
posrules p r d p r p dlp allrules p r pepron aederivs p
the mlp is trained on phonetically hand labeled speech timit and then further trained by an iterative viterbi procedure forced viterbi providing the labels with wall street journal corpora
allrules p r be the count of all derivations of p in which rule r could have applied i.e. in which d has either a r or r tag
NUM initialize i NUM a initialize j NUM 3except for features that look only at the current word i.e. features of the form wl word and tl tag
to recapitulate the transducers induced by ostia suffered from undergeneralization in a number of ways
the architecture shall provide a design that maximizes platform transportability
the use of any particular type of information is dependent upon the application
error statistics shall include counts of the number of times particular errors occurred
the architecture shall allow an application to collect document processing and error statistics
NUM this method is similar to aggregation methods used in nl generation
NUM NUM NUM NUM verification method demonstration
NUM NUM NUM verification method demonstration and inspection
we need to consider the predicate argument relations instead of simple word collocations
NUM NUM NUM NUM verification method inspection
user annotations may have access controlled by the mechanism specified in access control
they applied both approaches to a french english corpus of about one thousand sentence pairs
NUM the second point which will be relevant in the discussion of personal names in section NUM NUM relates to the internal structure of hanzi
here p is just the probability of any construction in as estimated from the frequency of such constructions in the corpus
for a language like english this problem is generally regarded as trivial since words are delimited in english text by whitespace or marks of punctuation
a morpheme on the other hand usually corresponds to a unique hanzi though there are a few cases where variant forms are found
thus rather than give a single evaluative score we prefer to compare the performance of our method with the judgments of several human subjects
we asked six native speakers three from taiwan t1 t3 and three from the mainland m1 m3 to segment the corpus
fortunately there are only a few hundred hanzi that are particularly common in transliterations indeed the commonest ones such as f
as with personal names we also derive an estimate from text of the probability of finding a transliterated name of any kind ptn
in these examples the names identified by the two systems if any are underlined the sentence with the correct segmentation is boxed
what both of these approaches presume is that there is a single correct segmentation for a sentence against which an automatic algorithm can be compared
the user interface consists of a small set of classes that play various roles in the overall architecture
the two major objects of the user interface interaction model are the listtree and the document store objects
the document window provides a view of the content of the targeted document see figure NUM
the user can copy relevant key terms to a holding area by selecting edit from the menubar
the user is presented with a popup dialog for importing the selected key terms see figure NUM
since the system is in place we have conducted a series of usability testing within our company
figure NUM the primary interaction with the key term hierarchy is accomplished by direct manipulation of the tree visualization
for example in the following continuation of this dialogue user3 show me return flights from denver to boston it is intuitively much less likely that the user means the on tuesday constraint to continue to apply
each element in x can have one of five values specifying the relationship between the filler of the corresponding slot in me and ms output vector y is constructed by directly copying all fields from input vector x except those labeled tacit
in this test the system was trained on approximately NUM atis NUM and atis NUM sentences and then evaluated on the december NUM test material which was held aside as a blind test set
to compute p s i ft t we make the independence assumption that slot filling operations depend only on the frame type the slot operations already performed and on the local parse structure around the operation
recall that the semantic interpreter is required to compute p ms t p wit
this phase is the same as for classifier models and the distributions at the leaves are often extremely sharp sometimes consisting of one outcome with probability i and all others with probability NUM in the second phase these distributions are smoothed by mixing together distributions of various nodes in the decision tree
figure NUM compares the tagging error rate on unknown words for the unigram method left and the loglinear method with nine features labeled statistical classifier at right
the maximum entropy parser is a statistical shift reduce style parser that can not always access head modifier pairs
after each cycle the estimates satisfy the constraints specified in the model and the estimated expected marginal totals come closer to matching the observed totals
the pattern is usually further simplified by considering only the heads of the possible attachment sites corresponding to the sequence verb noun1 preposition noun2
thus the term uzii denotes the deviation of the mean of the expected cell counts with value i of the first variable from the grand mean u
prepositional phrases exhibit a tendency to attach to the most recent possible attachment site this is referred to ms the principle of right association
multiple encoding sets in a single query
the fundamental solution is to have everyone use a unique coding system that can handle all the characters in the world
however it will require several years before most of the documents are encoded into a unique well defined coding system
if the score is larger than a predetermined threshold the loop terminates and returns the language and the score
for east asian languages we use a character unigram instead of a word unigram to model a language
sometimes a character set is used in a language that is not the primary candidate suggested by the character set
one re on for this is that the character set of a text is sometimes ambiguous due to the
iiowever in international domain it is difficult or impossible to specify the coding system with simple pattern matching
node labeled with the start category of g2 namely s a node x is expanded by choosing a rule that rewrites the category of x
however they provide a rough lower bound measure of performance
figure NUM illustrates how four example boundary sites in figure NUM would be coded
for example segment initial phrases have been correlated with longer preceding pause durations
pause np has the best additive algorithm performance as measured by the summed deviation
this confirms that the tuned algorithm is over calibrated to the training set
our training set of NUM narratives provides NUM examples of potential boundary sites
figure NUM shows one of the highest performing learned decision trees from our experiments
the problem of unbalanced data is not unique to the boundary classification task
output was evaluated against what the human narrator actually said
combined feature cue prosody complex true false
thus the problem can not be addressed as long as the method relies only on statistics
consider the contingency matrix shown table NUM between japanese word wjp n and english word weng
first we decide a set of anchors using article boundaries section boundaries and so on
from the result we may safely say that our method can be applied to voluminous corpora
mutual information represents the similarity on the occurrence distribution and t score represents the confidence of the similarity
the initial state of the algorithm is a set of already known anchors sentence pairs
first the systems of functional closed words are quite different from language to language
our method gradually determines sentence pairs anchors that correspond to each other by relaxing parameters
these technical terms are the subjects of the text and are essential for alignment
figure NUM overview of the alignment system figure NUM overviews our alignment system
table NUM language models and segmentation accuracies NUM test sentences
the third sentence of figure NUM is an example of this type
some transliterated western origin words exceed the predefined maximum length for unknown word
in table NUM we set fl NUM NUM to compute f measure
recall is defined as m std and precision is defined as m sys
we discarded word types that appeared only once in the training texts
note that the test sets were not part of the training set
here special symbol indicates the word boundary marker
we use word bigram as the segmentation model in the following example
our word model can be thought of a generalization of their statistical model
there is much debate about what to define discourse segments in terms of and what kinds of relations to assign among segments
we report highly significant results of segmentations performed by naive subjects where a commonsense notion of speaker intention is the segmentation criterion
as reported in their talk not in the paper reliability on segment structure and core identification was well over the NUM
to make this evaluation we first use a significance test of the null hypothesis that the distributions could have arisen by chance
the need to model the relation between discourse structure and linguistic features of utterances is almost universally acknowledged in the literature on discourse
in terms of the segmentation shown here the referents introduced in segment x are more relevant for interpreting the pronoun in segment z
here we present the results of our study of naive subjects performing a relatively unstructured segmentation task on a corpus of similar discourses
for example a discourse cue is more likely to occur when the contributor precedes the core utterance p NUM
thus our system of the lexicon could guarantee that these subcategorization frame s of intuitively the same word sense share the same c block
the set of original postpositions is described in the left term in the source direction of an arc in the permutation commands
any two s blocks can share the same word sense c block as long as the deep case thematic role frame is consistent
this factor alone can be used by the analyzer to restrain the application of the generated subcategorization frame for the indirect passive interpretation
here is to show an empirical result of the development and analysis of the lexicon from the point of view of space complexity order cf
x dat y nom eat passive x eats y x can eat y xor x is eaten by y as is observed in e.g.
x was made to eat y by z a correct process would generate the subcategorization frames represented in the example sentences from e.g.
NUM NUM lead to the same event structure interpretation which is shared by the english translation x gives y to z NUM e.g.
the benefits include more manageable repeatable lexicon realized by reducing the underlying redundancy of information in some distributed architecture of the computational lexicon
the sum of these figures are also much smaller than that of simple combinations 16c7 NUM NUM
we have NUM open data not NUM for lasa1 because the data is expanded due to the semantic ambiguity
this process continues until the longest common prefix of the outputs of all arcs leaving each state is the null string the definition of an onward transducer
while subsequential relations are formally a subset of regular relations any relation over a finite input language is subsequential if each input has only one possible output
the number of nodes in each decision tree is bounded by o k since there are at most k arcs out of a given state
these biases are so fundamental to generative phonology that although they are present in some respect in every phonological theory they are left implicit in most
in other words the traditional formalism of context sensitive rewrite rules contains implicit biases about how phonological rules usually work that are not present in the transducer system
systematic phonological constraints such as syllable structure may make it impossible to obtain the set of examples that would be necessary for ostia to learn the target rule
first all output symbols beyond the longest common prefix of the outputs of the two arcs are pushed back to arcs further down the tree
NUM NUM indicates that the machine outputs a string consisting of the previous input segment followed by the current segment
the higher number of states reduces the number of training examples that pass through each state making incorrect state mergers possible and introducing errors on test data
we measure a generalization s goodness by tile total sum of the penalty scores of the nodes used for the generalization
in this paper we introduce an extended dtla lasa NUM inductive earning algorithm with structured attributes which can handle structured attributes in an optimmn way
these attempts however are not always satisfactory in that the handling of the thesaurus is not flexible enough
with these preparations we now formally address the problem of tlm optimum genermization of the singh attribute tattle
in other words a single attribute table as shown in table NUM is the flmdamental unit for the dtla
the value y is rift if a rift belongs at position j and is no rift otherwise
unfortunately we often do not know how to compute terms weights
the results are presented in the following chart figure NUM shows the average cpu time to get NUM of the probability mass for each estimate and each sentence length
inside probability is defined as the probability of the words or tags in the constituent given that the constituent is dominated by a particular nonterminal symbol
figure NUM shows a graph of non NUM e that is the percent of nonzero length edges needed to get NUM of the probability mass for each sentence length
we also measured the total cpu time in seconds needed to get NUM of the probability mass for each of the NUM sentences
this seems to be a reasonable basis for comparing constituent probabilities and has the additional advantage that it is easy to compute during chart parsing
null previous work the literature shows many implementations of best first parsing but none of the previous work shares our goal of explicitly comparing figures of merit
we parse exhaustively to determine the total probability of a sentence that is the sum of the probabilities of all parses found for that sentence
a simple extension to the normalized fl model allows us to estimate the per word probability of all tags in the sentence through the end of the constituent under consideration
NUM popped the percentage of constituents in the exhaustive parse that were used by the best first parse to get NUM of the probability mass
the first task is one of feature selection the second is one of model selection
this makes building of good search queries a more sensitive task than before
unfortunately when a new constraint is imposed the optimal values of all parameters change
the head word constituent or pos label of the nth tree and the label of proposed constituent begin and last are first and last child resp of proposed
the bigram parser uses a backed off estimation scheme that is customized for a particular task whereas the maximum entropy parser uses a general purpose modelling technique
in the experiments reported here the trigram method was run using the tag inventory derived from the brown corpus except that a handful of common function words were tagged as themselves namely except than then to too and whether
typically the procedures postulate many different values for a l which cause the parser to explore many different derivations when parsing an input sentence
for lcb their there they re rcb for instance we enabled word usage errors which include substitutions of their for there etc but we disabled contractions which include replacing they re with they are
we conducted a case frame tree acquisition experiment on lasa NUM and the dtla NUM using part of our bilingual corpus for the verb take
the library shall contain common objects composed of slot definitions with fill rules but without patterns
NUM NUM NUM verification method demonstration
NUM NUM NUM verification method demonstration
NUM NUM verification method demonstration and inspection
the boundaries shall ensure that persons only have access to the appropriate level of classified information
as a minimum the format and access method of each persistent knowledge item shall be established
multi lingual is considered to be multiple languages in one document or multiple documents in different languages
the proper interpretation should be clear from the context
we will call NUM the set of active features
we employ the following notation to represent these features
let us consider the fifth row in the table
the final model contained NUM constraints
computational linguistics volume NUM number NUM
for example including the template NUM feature
the experiment uses the different input media to manipulate the word error rate and the degree of spontaneity
does the word carry one of the english inflectional suffixes
then we build an lm based on the second set and use the resulting lm to segment again the first corpus
we got a perplexity NUM for a vocabulary of 80k words and the alternating procedure has little impact on the perplexity
in addition to the NUM million characters of segmented text we had unsegmented data from various sources reaching about NUM million characters
our preliminary experiment shows that the alternating procedure not only improves the accuracy of our segmentation but discovers unseen words surprisingly well
the first author would like to thank various members of the human language technologies department at the ibm t j watson center for their encouragement and helpful advice
the underlined entries are the success transistions
edu salim roukos ibm t j watson research center yorktown heights ny NUM usa roukos c wat son
except for the successfid transition from j
after each segmentation an interpolated trigram model is built and an independent test set with NUM NUM million characters is segmented and then used to measure the quality of the model
each character in p is scanned from left ro right
even worst for some internal code e.g.
the null character z represents all the characters in but not in zr lcb x rcb z 02r lcb
second the time complexity of mapping o ip d can be done in linear time with respect to ipj and in constant time with respect to NUM
the confusion sets were selected on the basis of being frequently occurring in brown and representing a variety of types of errors including homophone confusions e.g. lcb peace piece rcb and grammatical mistakes e.g. lcb among between rcb
o elxlqcd where qc is the set of states of me which is large this approach is not attractive
note that there will be no removaj of disagreeing decisions if the text has the kappa coefficient greater than or equal to t at the start
nn j n wa 3aq t translated as the department of chinese chinese university of hong kong
big NUM it is not possible to directly convert the byte sequc ce into the corresponding character sequence in the backward direction
for example the third unseen NUM byte character is mapped to a one byte character the value of which is char NUM
at this position t NUM a4 does not match with p NUM
second we can design and implement robust explanation systems that employ a representation of discourse knowledge that is easily manipulable by discourse knowledge engineers
given these difficulties how can one evaluate the architectures algorithms and knowledge structures that form the basis for an explanation generator
to construct an explanation plan mckeown s text system traverses the schemata and sequentially instantiates rhetorical predicates with propositions from a knowledge base
the second line contains the keyword pro which denotes that everything in its scope will describe the structure of the entire verbal phrase
it addresses a multiplicity of issues in explanation generation ranging from knowledge base access and discourse planning to a new methodology for empirical evaluation
after approximately twenty passes through the critiquing and revision phases edps were devised that produced clear explanations meeting with the domain expert s approval
rather than merely returning a flat list of views the explain algorithm examines the paragraph specifications in the nodes of the edp it applied
we refer to these relations as slots or attributes and to the units that fill these slots e.g. ovule as values
there are two benefits of interposing a knowledge base accessing system between an explanation planner which performs global content determination and a knowledge base
it is also important that the two corpora though very different in style behave in the same way as far as systematic ambiguity is concerned
complexity and recurrence of ambiguous patterns in corpora in the previous section we pointed out that unsupervised lexical learning methods must cope with complex and repetitive ambiguities
at first we need to study the system classification parameters r and r see tables NUM and NUM
it is important to observe that the complexity does not arise simply from the number of colliding tuples but also from the structure of ambiguous patterns e.g.
prob esli eslj mr est esl log2 prob esl prob esl NUM
the first stage is noise compression in which we adopt an incremental syntactic learning method to create a more suitable framework for subsequent steps of learning
cfg rules for compound noun construction use these categories as non terminals
therefore this example captures the typical problem laced in our task
we admit that the definition of a word might be controversial
figure NUM exam pie o f evi deuce c olle ct ion
for simplicity the values of the head attribute are indicated instead of the non terminal symbols
this phenomenon occurs very early in our domains and this could be easily foreseen according to the high correlation between esl s that we measured
patterns in NUM NUM collect the evidence of a predicateargument relation between a sino verb and a noun
table NUM human performance table NUM shows the group average f measures for the
the examples below clearly illustrate this
we discuss them in ss2 NUM
in particular we ask two questions
the test set was acoustically segmented
corpora and test perplexity both within and across corpora
the best perplexity numbers are obtained under matched conditions
written this down honesty in government
table NUM dialog divided into subparts
NUM NUM mismatch in training and test data segmentations
laughs and coughs and turn taking
when looking for a particular word the user can input in english or japanese any word that is semantically related with the one he is looking for
e mail for example can be used not only for interaction between teachers and students but also for interaction among students collaborative learning
the dictionary consists of a lexicon and a database of examples the latter being a collection of collocations extracted from text corpora such as newspaper articles
the examples are displayed in hyptertext format in terms of similarity that is the examples are grouped or ordered in terms of member ship or proximity
there is another reason to plead for this kind of feedback loop software users are generally the ones who know best what their needs are that is what is useful
first of all network facilities e mail news www home pages minimize not only the boundaries of time and space but they also help to break communication bar tiers
this allows japanese kids to practice let s say english by exchanging messages with students from abroad chatting about their favorite topics like music sport or any other hobby
however before doing so we have to consider several basic issues what information is useful that is what in formation should be provided to the learner when and how
this situation is even worse when single word terms are intermixed with phrasal terms and the term independence becomes harder to justify
whether or not they claim to be nlp machines typically behave conspicuously differently than humans in communication employing either some explicitly declared machinese or some unspecified superset of some unspecified subset of some human language
ever since the first coling was arranged and the term computational linguistics was coined our undertaking has had two faces research where computation is used for better understanding of language and application where understanding of language is used for better computation
one reason for the preoccupation with editorial detail is the legislators lack of linguistic sophistication they do not possess the intellectual tools to describe the desired text properties nor have linguists if consulted so far had much of substance to offer on higher levels of structure
additionally adept captures similar statistics on problem types and problems associated with each document
the set of elementary trees and probability function and the word graph are constructed as follows NUM
the mppwg mps instance is does the stsg generate a parse resp
the partial function o is called left null most substitution
NUM the reduction constructs three elementary trees for each conjunct ck
the proof in this paper is not a mere theoretical issue
this concludes the proof that mppwg and mps are both np complete
the input sentence which the reduction constructs is simply v11 v3
moreover the answers to the mppwg instance must correspond exactly to the answers to ins
for our example some suitable probabilities are shown in figure NUM
finally it is worth noting that the solutions that we suggested above are merely algorithmic
the models of interest are known as random fields
conversely speech synthesis being predominantly concerned with rendering text to speech rarely considers actual full scale generation
in this case the user would be expected to react i.e. either confirm or rebuke this statement
as a basis for discussion we introduce our dialogue model and the relevant parts of the grammar in detail
the two pillars of our system a state of the art computational dialogue model and a state of the art nl generator are presented
tone the choice of a tone for each tone group the tone is associated with the tonic
the parameters indicate which participant can perform a given move s information seeker k information knower
hence we conclude that requests that are not in response to a user utterance should be realized as a wh question
in terms of speech function we realize this request as a question demanding in ormation
hence the intonational control required for speech generation in a dialogue system has been built into the existing komet grammar
we start from the stratum of grammar and move to the other linguistic and pragmatic resources relevant to the present problem
lev NUM to and tte o c functioning in a similar way as quotation marks however requires a careful observation
the two horizontal lines can be interpreted as mles for the probability of an en form being an infinitive the solid line or overall mle clearly provides an estimate based on the whole population whereas the dashed line or hapax based mle provides an estimate for the hapaxes
primarily at present it is the method for recording and passing forward the information developed by modules of the extraction component
the architecture will help the developer get an application to the end user more quickly and will provide a more flexible application design
for example an application may be needed for document detection only even though the architecture also provides specifications for extraction related modules
NUM NUM conformance to the architecture the tipster architecture has been completed to the extent that thc basic functionality of modules has bcen determined
as long as the inputs and outputs of a module conform to icd specifications it is considered to be a tipster module
for example it may require the parsing of aircraft tail numbers even though no icd specifications exist for such a module
because an architecturally compliant application will follow standard conventions for internal and external interfaces it will be possible to build standard test suites
details about the configuration management policy are given in section NUM NUM of this document as well as in the configuration management plan
to support the development of this tacad the vendor will demonstrate by inspection module by module compliance with the tipster architecture
the tacad may be used by vendors to facilitate teaming with other vendors or insertions of new capability into existing tipster systems
following this we observe a series of text slices in which he appears frequently
finally consider the diagnostic plots for max havelaar shown in figure NUM
the goal of this paper has been to explore in detail the consequences of intra
the words that have already been used have a raised probability of being used again
in fact there are NUM text slices where ahab is not mentioned at all
the probability mass of unseen types mp NUM is also tabulated
from the 14th observation onwards the hubert labbe model consistently underestimates the real vocabulary size
the nonrandomness at the level of sentence structure does not influence the expected vocabulary size
