as in the manual process we run a short query retrieval this time retaining NUM top documents retrieved by each query
an initial experiment in this direction has been performed as part of nlp track genlp3 run and the results are encouraging
context constraints o are defined by the following abstract syntax
such equations can be reduced to context constraints via skolemisation
important features of the effort include a multidisciplinary team with technical expertise in various areas including nlp and clinical expertise with the target population
this information is primarily used to reconstruct an appropriate expansion if later input indicates inappropriate decisions were made in earlier states of the parse
in addition the system must be fast and runnable on relatively inexpensive and portable pc platforms so as to make it cost effective
for example it might be used to teach a user to use standard agent verb object sentences by highlighting only words that fit into that pattern
some typical parallelism phenomena are ellipsis corrections and variations
the syntax and semantics of context constraints are defined as follows
in using abbreviation expansion users are required to memorize a set of abbreviations that if typed will be expanded into full words
this map provides access to a basic vocabulary and has proven to be an effective inter null face for users in our target population
assume that the word consists of the letters i1
incidentally this setting varied considerably from corpus to corpus
since the standard deviation is the square root of the variance we have
this information indicates which cases the verb is likely to have and the semantic type of words that are likely to fill those cases
in this paper we proposed a method to define similarities between general nouns used in various domains
the scheme to look for similar words in the corpus has already taken the influence of data sparseness
in order to avoid these difilculties the approach to use of different resources from the col pus is promising
it is difficult to extract all knowledge from only a corpus because of ineoml lete analysis and data sparseness
first we evaluate the obtained similarities by comparing them with the similarities in bunruigoidlyou
the method avoids data sparseness by estimating undefined similarities from the similarity in the thesaurus and similarities defined by corpora
this can be regarded as data sparseness problem that few nouns appear in the corpus
in other words we can not construct the general thesaurus from only a corpus
brown and pereira provide the clustering algorithm assigning words to proper classes based on their own models
in applications of natural language processing it is necessary to appropriately measure the similarity between two nouns
a document which is retrieved at a high rank from such a stream is more likely to end up ranked high in the final result
these has been organized together into a stream model in which alternative methods of document indexing are strung together to perform in parallel
a more radical normalization would have also verb object noun rel clause etc convened into collections of such ordered pairs
while our stream architecture may be unique among ir systems the idea of combining evidence from multiple sources has been around for some time
a manual run means that some human processing was done to the queries and possibly multiple test runs were made to improve the queries
we presented in some detail our natural language information retrieval system consisting of an advanced nlp module and a pure statistical core engine
NUM proper names we identify proper names for indexing including people names and titles location names organization names etc
our aualysis was based mainly m i h i rot eri ies of lhe g n rat NUM li nglish so ix naturally luilx lose t o i he division f t arl i v
ijnit classifiers arc further divided inl o iiqnei lcb ai tyi icai i ii l si e iai whil meti lcb ic assitiers are divided iltl o measui i all containei lcb ilssifters
NUM NUM kg no kami ha jgbun da NUM kg of paper top enough is NUM kg of paper is enough NUM NUM hako no kami ha jubun da NUM box of paper top enough is NUM boxes of paper are enough in fact both NUM and NUM could be translated with singular or plural verb agreement
then we introduce ore bilingual mmlysis o1 classifiers a nd show how this a na ysis can lm used in a la pmms l lc nglish mn hin trm lal i m sysi em se l i m NUM
upon reception of a message the receiving actor processes the associated method a program composed of several grammatical predicates e.g. syntaxctieck which accounts for morphosyntactic or word order constraints or conceptciteck which refers to the terminological knowledge representatiou layer and accounts for type and further conceptual admissibility constraints number restrictions etc
systematic cross classification allows to capture this fine grained distinction easily and in a principle based way
this intuition is modeled in figure NUM with the additional artificial concept feducated human
additionally we do not introduce artificial antonyms as wordnet does pregnant unpregnant
null NUM a forward composition b x i argsx u lcb y rcb y argsy x i argsxu argsy b
bug in fatma kitap okuyacak r
NUM a bugiin kimi gsrecek fatma
this grammar which derives the predicate argument structure is then integrated with the information structure in section NUM in section NUM the formalism is extended to account for complex sentences and long distance scrambling
NUM nonverbal elements are associated with simpler ordering categories often just a variable which can unify with the topic focus or any other component in the is template during the derivation
subordinate verbs in turkish resemble gerunds in english they take a genitive marked subject and are case marked like nps but they assign structural case to the rest of their arguments like verbs
germanet includes a new treatment of regular polysemy artificial concepts and of particle verbs
xl argsuw rcb y xlargs b
this will be discussed further in section NUM
however in our linguistic applications we only need to quantify over finite sets so the weaker theory is enough and the techniques correspondingly simpler3 the decidability proof works by showing a correspondence between formulas in the language of ws2s and tree automata developed in such a way that the formula is satisfiable iff the set of trees accepted by the corresponding automaton is nonempty
the constraint base is an automaton which represents the incremental accumulation of knowledge about the possible valuations of variables
the recognition problem is just the problem of determining whether or not the resulting automaton recognizes a nonempty language
what doner and thatcher and wright and for strong 2s rabin show is that each formula in the language of ws2s corresponds to a tree automaton which recognizes just the satisfying assignment labelings and we can thereby define a notion of recognizable relation
it is immediately clear that the compilation of such an automaton is extremely unattractive if at all feasible
the direct use of a succinct and flexible description language together with an environment to test the formalizations with the resulting finite deterministic tree automata offers a way of combining the needs of both formalization and processing
in particular where the definition of a property of parse trees involves negation or quantification including quantification over sets of nodes it may be easier to express this in an mso tree logic compile the resulting formula and use the resulting automaton as a filter on parse trees originally generated by other means e.g. by a covering phrase structure grammar
a reduction by a factor of four to five in the unweighted average case can be observed applying the parsetalk strategy
the parser s forwarding mechanism for search messages was further restricted to circumvent the above mentioned problem of erroneous discontinuous over analyses
alier updating inlormation at relevant word actors in the resultant tree successful termination of the scratch message is signalled to the receipthandler
we therefore compare at a more abstract computation level the number of method executions given exactly the same dependency grammar NUM
the reasons why we diverge from conventional parsing methodologies e.g. chart parsing based on earley or tomita style algorithms are two fold
if it fails a modifier search process is triggered if it succeeds a new dependency structure is constructed combining the partial analyses
a depth first yet incomplete parsing algorithm for a dependency grammar is specified and several restrictions on the degree of its parallelization are discussed
the specification of binary constraints already provides inherent means for robust analysis as grammatical functions describe relations between words rather than welltormed constituents
as our work progressed however we felt the growing need for restricting its scope as a continuous domestication process
due to this close coupling of grammatical and conceptual constraints syntactically possible though otherwise disallowed structures are filtered out as early as possible
the transition function can be thought of as a homomorphism on trees inductively defined as h a0 and h a tl t2 a h tl ha t2 a
then the induction is based on the closure properties of the recognizable sets so that logical operators correspond to automaton constructions in the following way conjunction and negation just use the obvious corresponding automaton operations and existential quantification is implemented w th the projection construction
from the initial state on both the left and the right subtree we can either go to the state denoting found xl al if we read symbol NUM or to the state denoting found x2 a2 if we read symbol NUM
the connection allows the linguist to specify ideas about natural language in a concise manner in logic while at the same time providing a way of compiling those constraints into a form which can be efficiently used in natural language processing applications
the xbar predicate on the other hand is a disjunctive specification of licensing conditions depending on different features and configurations e.g. whether we are faced with a binary unary or non branching structure which is better expressed as several separate rules
the reason is that it gives a clear cut if not simple minded definition of topic and lends itself easily to a statistical treatment
here we simply assume that a title term or a noun that appears in the title counts as a topic for the text
consequently the descriptive phrase an audio journal which is new information in the first paragraph is omitted from the second
the algorithm which assigns contrastive focus in both thematic and thematic constituents begins by isolating the discourse entities in the given constituent
first each property or discourse entity in the semantic and information structural representations is marked as either previously mentioned or new to the discourse
the output of this module is easily translated into a form suitable for a speech synthesizer which produces spoken output with the desired intonation
for example the conjunction predicate specifies that propositions sharing the same theme or theme be realized together in order to avoid excessive topic shifting
moreover the belief that the hearer is interested in buying an expensive powerful amplifier justifies including information about its cost and power rating
the theme foci and the rheme foci mark the information that differentiates properties or entities in the current utterance from properties or entities established in prior utterances
in addition the input provides a communicative intention for the goal which may affect its ultimate realization as shown in 1o
for example in describing the rabbit it may be important to distinguish it from the very similar hare in order to avoid confusion
this work develops an approach to multilingual name recognition that allows a system optimized for one language to be ported to another with little additional effort and resources
we use edr concept dictionary to acquire the concel tual relationship between two concet ts
we found that two thresholds are very hell ful in iml roving the result
if a rule succeeds use it to decide the attachment and exit
prepositional phrase attachment is a major cause of stru tural alnbiguity in natural language
we extract eoncel t lass from concept classification in edr concept dictionary rcb
we use clues that are general and reliable 1pfte stands for parser for free text of english
unfortunately all these smoothing methods are not efficient enough to make a significant improvement on perforlnancc
the tokenizer separates punctuation from words
a subtree relation c r a holds if cr is a subtree of cr i i.e. if there exists a context function NUM such that a NUM a
in order to account for such distinctions of focus we employ a two tiered information structure representation as a framework for maintaining local coherence in the generation of natural language
the equations in NUM determine that the semantics of the source clause and the semantics of the target clause are obtained by embedding the representations of the respective contrasting elements into the same context
apart from the lack of disambiguation we achieved an error free lemmatisation of all occurring word forms for a trial message database of about NUM words
for example the word ruel might count as a non word error for the source text word rut if a small dictionary is used for reference but count as a real word error if an unabridged dictionary is used
we assume that an ocr string is generated from the original word by one or more of the following operations a delete a character b insert a character or c substitute one character for another
the system running unoptimized code on a 128mhz decalpha processor processed the test corpus at a rate of about NUM words strings per second for experiments NUM and NUM and NUM words strings per second for experiment NUM
in general non word errors will never correspond to any dictionary entries and will include wildly incorrect strings such as as well as misrecognized alpha numeric sequences such as bn234 for 8n234
in particular because the process is based on the structural definition of a word viz a character sequence between white space not a morphological one any errors that obscure word boundaries will defy correction
we conducted three experiments NUM isolated word error correction the system used only channel probabilities without considering context information i.e. it always selected the candidate with the highest rank in the candidates list to correct a given ocr string
for example if the source text john found the man is rendered as john fornd he man by an ocr device then fornd is a non word error and he is a real word error
the probability pr slw the conditional probability that given a word w it is recognized by the ocr software as the string s can be estimated by the confusion probabilities of the characters in s if we assume that character recognition in ocr is an independent process
since pos based methods are not effective in distinguishing among candidates with the same pos tags and since methods based on word trigram models involve extensive training data and require that huge word trigram tables be available at run time we used a word bigram slm as the first step in our investigation
prepositional frames are not considered since according to the author it is not clear how a machine learning system could do this determine which pps are arguments and which are adjuncts
mary works since two weeks on it mary has been working for two weeks on morphology
proud is the student on it that the student is proud of the fact that pcs
for instance the precision and recall for the prepositions aus out was NUM and NUM that of t on off NUM and NUM while that of geeger against NUM and NUM respectively
in the experiment described in section NUM the 6n l number of iterations was set empirically
any clause in wt ch a pc immediately precedes the prronomlna adverb is mapped as in the appropriate rule with the additional element pp p n n in the set where n is the head of the nc within the prepositional constituent
in figures NUM two errors are due to the disambiguation component nehmen amo three errors stem from mistaking reflexive verbs for verbs t ki g any accusative object sich treffen mit to meet with sich bekennen zu declare oneself for sich halten an to comply with
the second component is the end tag tree confidence
figure NUM part of a robotag begin tag classifier
they will be discussed in the next section
the robotag server performs the tag learning functions
in using a decision tree for classification each node indicates a feature test to be performed
the first case is to clarify the ut terance NUM
at the same time if the technologist simply lets the user dictate how the job should be done as often as not the user will make inappropriate use of the technology either expecting too little or too much of its capabilities
it is in the nature of a lessons learned to support these future efforts that i have recorded here at the close of phase ii so many of the difficulties that we have encountered and in many cases also overcome
it will allow the government user to upgrade applications by the insertion of key new capabilities for example the replacement of one entity tagging module with a new one without a complete change of application and without necessarily having to stay with the same vendor
if only one or two of these projects can succeed in making a large positive contribution to a user s task the returns should be substantial in making it clear to people that the technology is ready to be deployed and for what kinds of tasks
before such a business could be established a market survey would be required to determine if such a center would pay for itself in addition to providing at a comparable or lower price better service than the already existing less formally organized system of distribution
style will vary widely among different user organizations from those who feel most comfortable with detailed project plans and schedule charts to those who hate view graphs and only trust round table discussions with an occasional hand drawn diagram on the back of a sheet of paper
the magnitude of the difficulty of achieving that goal was perhaps not well understood in the early days of the program but since then we have all learned that this transfer does not happen automatically but requires even more effort planning and creativity than the research
demonstrations with the user s data mocked up user interfaces examples of similar projects and descriptions of the results of testing the technology against tasks similar to the user s can all be used to support the contention that the technology could be made helpful to the user
this translates into carefully planned communication of information to those who need it when they need it well focused meetings and the presentation to the user only of quality products even interim products like prototype guis which have been thoroughly reviewed and are free of obvious mistakes
it is important to have input from users who may also experiment with the use of the technology if they are inclined to but it is also vitally important to have someone familiar with the technology and its potential look at new ways of tackling the user s issues
in subsequent sections we describe the details for each probability
the semantic formalism of such a system should thus allow for the underspecification of these unresolved ambiguities but still allow for them to be resolved in a monotonic way of course
the project verbmobil funded by the german federal ministry of research and technology bmbf combines speech technology with machine translation techniques in order to develop a system for translation in face to face dialogues
the binding of variables between functor and argument takes place via the subcat list through which a functor can access the main instance and thc main label of its arguments and state relations between them
the main difference between both types of mrss and lui is that the interpretation of lui in an object language other than ordinary predicate logic is well delined as described in section NUM NUM
the system has been tested on three simplified dialogues from a corpus of spoken language appointment scheduling dialogues collected for the project and processes about NUM of the turns the syntax can deal with
the qlf formalism incorporates a davidsonian approach to semantics containing underspecified quantifiers and operators as well as anaphoric terms which stand for entities and relations to be determined by reference resolution
the representations produced are then assigned dialogue acts and used to update the model of the discourse which in turn may be used by the speech recognizer to choose the current language model
contemporary syntactic theories are normally unification based and commonly aim at specifying as much as possible of the peculiarities of specific language constructions in the lexicon rather than in the traditional grammar rules
in order to evaluate the acquired dictionary m nn ng compares the frames obtained for NUM random verbs to those in a published dictionary yielding for these verbs an overall precision and recall rates of NUM and NUM respectively
this means that they are inconsistent with none and thus the coherence rule can not be applied
thus the only explanation for a structure is achieved by applying both these two rules and results in fail
note that the explanation of default rules at one substructure might affect the explanation of rules at other substructures
in default logic there is often a restriction to normal default theories since non normal default theories are not even semi decidable
the decidability of the nonmonotonic rules defined here is however highly dependant on the given subsumption order
it is also possible to define some constraints called requirements which must hold for a class
class value nonmon c x posterior x fail
in our original definition we said that the nonmonotonic rule above should be applied if y x
this makes the procedure for application of defaults more efficient and will be further discussed in section NUM
where p is precision r is recall and fl is the relative importance given to recall over precision
sampling ratios for our best japanese results were NUM NUM and NUM for person place and entity
to motivate our termination criterion consider the adverb tree and the asterisked node whose slash value is shared with slash at the root
adjunction involves the identification of the foot node with the bottom of the domination link and identification of the root with top of the domination link
finally we assume that rule schemata and principles have been compiled together automatically or manually to yield more specific subtypes of the schemata
the nod feature is reduced because it is a head feature whose value is inherited only from the head dtr and not from the adjunct dtr
normally we expect a sf with a specified value to be reduced fully to an empty list by a series of applications of rule schemata
in the second phase we derive from the entry for give another initial tree ts into which the auxiliary tree t1 for want can be adjoined at the topmost domination link
it should be noted that the results of our compilation will not always conform to conventional linguistic assumptions often adopted in tags as exemplified by the auxiliary trees produced for equi verbs
the derivations for the trees for the matrix verb want and for the infinitival marker to equivalent to a raising verb were given above in the examples t1 and t3
returning to the basic algorithm we will now consider the issue of termination i.e. how much do we need to reduce as we project a tree from a lexical item
furthermore suboptimal choices of this parameter tended to degrade performance rather than improve it
we will first factor out the dependence on the context size
for example tag trigram probabilities carl be estimated as follows
the experiments section discusses the settings that produced the best results for each task
robotag can use whatever lexical and token type features that the preprocessor pro null vides
the result of the test indicates which branch of the tree to take next
using words as features is related to the idea of automatic word list modification
too many negative examples can hurt learning accuracy by making the system too conservative
we can now explain why certain word orders are appropriate or inappropriate in a certain context in this case database queries
multiset ccg is a combinatory categorial formalism that can capture the syntax and interpretation of free word order in languages such as turkish
the different positions of the sentential adjunct yesterday in the following sentences result in different discourse meanings much as in english
such as a verbal category to combine with one of its arguments to its right or left
the ordering category in NUM is a function that specifies the components which must be found to complete a possible is
the arguments and adjuncts within most embedded clause can occur in any word order also seen in NUM a and b
in addition elements from the embedded clause can occur in matrix clause positions i.e. long distance scrambling 5c
in the next sections i will present a combinatory categorial formalism which can handle these characteristics of free word order languages
word order variation in turkish and other free word order languages is used to express the information structure of a sentence
consequently we have to generalize the second condition on c substitutions for all color annotations d of symbols in a xc d c in propositional logic
other phenomena for which the hocu approach seems particularly promising are phenomena in which the semantic interpretation process is obviously constrained by the other sources of linguistic information
for instance discourse la above is assigned the semantic representation 3a and the equation 3b with unique solution 3c
in particular they have n t measured the regularity of the purely lexical stream
past attempts to quantify prose rhythm may be classified as perception oriented or signal oriented
b the corpus frequencies of the top six lexical stress patterns
figure NUM n gram stress entropy rates for z weak secondary stress
prose rhythm is a widely observed but scarcely quantified phenomenon
the results for these experiments are shown in figures NUM and NUM
but even this constrained randomization is weaker than what we d like
the chosen sentence avoids consecutive primary stresses
NUM NUM the meaning of stress entropy rate
the rhythm of lexical stress in prose
when a query is submitted to the infinder database for expansion concepts which are contextually related to the query terms will be retrieved
the query must be transcoded for retrieval from each database and the documents retrieved must then be transcoded into the input set for display
occasionally as in sjis jis and euc for japanese there is a comparatively simple algorithmic mapping from one encoding to another
the queries were submitted in three different experimental sets raw characters and two sets of word based queries hand segmented and automatically segmented
neither was available in chinese where the error rate has been much worse than in any previous language we have worked on
in our experience with japanese both a grammar and a list of roughly NUM NUM words with their parts of speech were available
stemming word boundary identification punctuation and stopword identification must all be modified appropriate input and presentation methods must be provided
large scale text collections were tackled by the detection contractors while domain and language portability were the challenge for the extraction contractors
there will be information loss due to incomplete conversion tables or due to local expressions that have no equivalent in the another writing
in agglutinative languages a single printed word can express the lexical semantics of a complex noun phrase or even a whole sentence
the procedure uses a very simple heuristics to identify verbs the synt tic types of nearby phrases are identified by relying on local morphosyntactic cues
sparse data is a perennial problem when applying statistical techniques to natural language processing
our methodology in using the data has been to partition it into several sets
all other relations except for the pertains to relation are conceptual relations
furthermore linguistic resources are becoming increasingly important as training and evaluation material for statistical methods
a familiar type of regular polysemy is the organization building it occupies polysemy
an example for antonymy are the adjectives kalt cold and warm warm
if no such entry exists the verb is morphologically analyzed and its semantics is compositionally determined
sadjectives pertaining to a noun from which they derive their meaning financial finances
the cause relation in wordnet is restricted to hold between verbs
this list is further tuned by using other available sources such as the celex german database
the third experiment proceeds like the second except that minor adjustments and additions are made to the t eature set with the goal of improving performance s s s
the tree once built typically contains NUM nodes each one inquiring about one feature in the text within the locality of the current proper name of interest
figure NUM and table NUM show performance results p r when the three most powerful features are removed one at a time for companies persons and locations respectively
various types o1 features indicate the type el name parts of speech designators morphology syntax semantics and more
since a tree is built for each nmne class el interest the trees arc all applied individually and then the results are mergcd
a lexical unit may be a word e.g. started or a phrase e.g. the washington post
furthermore numerical constraints like path length restrictions am posited without motivating their origin whereas we state fomml well formedness and empirical criteria the evidence for which is derived li om psycholinguistic studies
from those general troth patterns and by virtue of the hierarchical organizatkm of conceptual relations concrete conceptual role chains cau atttomatically be derived in a terminological r tsoning system
in the process of path finding an extensive unidirectional search is performed in the domain knowledge base and formal well formedness conditions holding for paths between two concepts are considerexl viz
the main dillerence to our work lies in the fact that these path patterns to not take the compositional properties of relations into accotmt e.g. transitive relations
for proper selection we define a ranking on those path labels according to their intrinsic conceptual strength in terms of the relation t
the elliptical phrase which occurs in the n th utterance is restricted to be a definite np and the antecedent must be one of the forward looking centers of the preceding utterance
tim particular advantage of our approach lies in the integrated treatment of textual ellipsis within a single coherent grmnmar lormat that integrates linguistic and conceptual criteria in terms of general constraints
complete connectivity compatibility of domains and ranges of the included relations non cyclicity exclusion of inverses of relations and non redundancy exclusion of including paths
the theme of u is represented by the preferred center cp u the most highly ranked element of c un
in linguistic applications the outcomes are usually not real numbers but pieces of linguistic structure such as words part of speech tags grammar rules bits of semantic tissue etc
interpolation requires that the training corpus is divided into one part used to estimate the relative frequencies and a separate held back part used to cope with sparse data through back off smoothing
tile performance of the tagger was compared with that of an tlmm based trigram tagger that uses linear interpolation for n gram smoothing but where the back off weights do not depend on the eonditionings
we see that the trigram tagger is better than the bigram tagger in all three cases and significantly better at significance level NUM percent but not at NUM percent in case c
here r0 does not depend on the number of observations in cofftext ck only on the underlying probability distribution conditional on context ck
note that the absolute performance of the trigram tagger is around NUM accuracy in two cases and distinctly above NUM accuracy in all cases which is clearly state of the art results
tiros we will repeatedly strip off the tag furthest from the current word and use the estimate of the probability distribution in this generalized context to improve the estimate in the current context
assume further that there is abundant data in a more general context c t d c that we want to use to get a better estimate of p x i c
misaligned sections give rise to candidate context free correlation rules e.g. from frontal there were made the remainder etc validation against a text corpus leads to context sensitive correction rules such as from view frontal view
for the experiments described in this paper we considered the speech recognition system as a black box that is our efforts were directed at correcting the transcription errors in a post processing mode rather than to improve the initial transcription
this preprocessor performs tokenization word segmentation morphological analysis and lexical lookup as necessary for each language
instead of attempting a definition the acquired dictionary was compared to a broad coverage published dictionary cont iniug explicit information on prepositional subcategorization
for instance NUM is mapped with this rule to lcb pp auf nin is rcb
mary thlnk on john s arrival mary thinks about john s arrival d mary denkt damn da6 john bald aukommt
the automatic extraction of german prepositional sfs is based on the observation that certain constructs involving so called pronominal adverbs are high accuracy cues for prepositional subcategorization
he has on it thought considered that he thought of passive vc v nc n
pronominal adverbs are compounds in german consisting of the adverbs da r and wo r and certain prepositions
in the next section a learning procedure is described which makes use of pronomln NUM adverb correlative constructs to infer prepositional subcategorization
as an example the shallow parse structure for the sentence fragment in NUM is shown in NUM below
for instance NUM is mapped to lcb pp an npa v erinnern rcb
this default rule can be used when defining verbs
the constraints on classes are inherited through the hierarchy
immediate means that the nonmonotonic rule is going to be applied each time a full unification task has been solved or whenever all information about an object in the defined inheritance hierarchy has been retrieved
however as seen in the next section for many uses of nonmonotonic extensions within unification based formalisms the aim is to derive failure if the resulting structure does not fulfill some particular conditions
however this generalization is outside default logic
since it is the user who defines the actual nonmonotonic theory multiple extensions must be allowed and it must be considered a task for the user to define his theory in the way he prefers
note that as soon as a value has been further instantiated into a real value it will no longer be consistent with any no value and the nonmonotonic rule can not apply
if we assume that fl is defined as a default in young and rounds work then it is interpreted according to the rule if it is consistent to believe then believe
currently the lexicon of the implemented system contains about NUM NUM entries full forms and the grammar consists of about NUM syntactic rules of which about NUM constitute a subgrammar for temporal expressions
these two restrictions the use of only a ludrepresentation s context in composition and the monotonic percolation of semantic predicates up the tree make the system completely compositional in the sense defined in section NUM
even with very low power tools however we can construct automata for interesting grammatical constraints
condition b splits into a case where NUM serves as a secondary argument inside both cr and a and a case where it is a primary argument in c or a
but that construction merely ensures a right branching structure for forward constituent chains such as h b b c c or h b c c d d e f g g h and a left branching structure for backward constituent chains
if type raising is lexical then the definitions of this paper do not recognize NUM as a spurious ambiguity because the two parses are now technically speaking analyses of different sentences
since the new parser must be able to generate a non nf parse when no equivalent nf parse is available its method of controlling spurious ambiguity can not be to enforce the constraints NUM
in addition their method is less efficient than the present one it considers parses in pairs not singly and does not remove any parse until the entire parse forest has been built
a result worth remembering is that although tag equivalent ccg allows free interaction among forward backward and crossed composition rules of any degree two simple constraints serve to eliminate all spurious ambiguity
b there is a tree NUM that appears as a subtree of both c and cd but combines to the left in one case and to the right in the other
for instance to rule out NUM the crossing back ward rule n n i n i n must be omitted from english grammar
frequency NUM if the word is not included in the frequency list
new links could be added based on knowledge of the user s message selections
the grammatical description of single words is organized in a hierarchy of so called word actors which not only inherit the declarative portions of grammatieal knowledge but are also supplied with lexicalized procedural knowledge that specifies their parsing beo havior in terms of a message protocol
given the complex control structure requirements of a realistic text understanding system integrated incremental robust processing we argued for a unifying approach in which declarative grammar constraints are lexically encoded and procedural knowledge can be specified by distinguished lexicalized communication primitives viz
preserving the strengths of this approach lexicalized control but at the sane time reconciling it with current standards of lexicalized grammar specification the parsetalk system can be considered a unifying approach which combines procedural and declarative specifications at the grammar level in a formally disciplined way
the efficiency gain that results from the parser design introduced in the previous sections can empirically be demonstrated by a comparison of the parsetalk system abbreviated as pt below with a standard active chart parser NUM abbreviated as cp
as shown by experiment NUM when the system uses context based non and real word error correction it achieves a total error reduction rate of NUM NUM
for ocr to be truly useful in a wide range of applications such as office automation and information retrieval systems ocr reliability must be improved
of course such problems in processing unknown words are not unique to ocr error correction they represent a general problem for all natural language processing tasks
makes NUM ossible a uniform an l st raigh forwa rd
in a word bigram language model we assume that the probability that a word w will appear is affected only by the immediately preceding word
the analysis inl rotlut ed in this tin per
th main t rt l rly
the differentiation into measure and container provides a graceful default
silicrs mm english lmr itivo nouns
alth lugh the analysis is bas d
exalnples of spe ms classifier s are given in table NUM
our calegory if classitier includes both a im nesc
however virtually any entity attribute pair might be described using an illustrative comparison and so we need some way of generalising the processing here
in effect this means interleaving the intersection of the grammar and the input description such that only the minimal amount of information to determine the parse is incrementally stored in the constraint base
consider the following naive specification of a lexicon NUM lexicon x x e sees a x e v a
therefore it seems unlikely that a formalization of a realistic principle based grammar could be compiled into a tree automaton before the heat death of the universe
we achieve this by using the intertranslatability of formulae of mso logic and tree automata and the embedding of mso logic into a constraint logic programming scheme
we show that this allows an efficient representation of knowledge about the content of constraints which can be used as a practical tool for grammatical theory verification
tree automata represent properties of trees and there are many such properties less complex than global well formedness which are nonetheless important to establish for parse trees
because we choose the monadic second order language over whichever of these two signatures is preferred we can quantify over sets of nodes in n2
so further work will definitly involve the optimization of the prototype implementation while we await the development of more sophisticated tools like mona
the metaplan for this repair repeated below is similar to that for acceptance but involves the reconstruction of the discourse model
the long term goal of our work is to construct a model of communicative interaction that will be able to support the negotiation of meaning
our model unifies the fundamental tasks of interpreting speech acts producing speech acts and repairing speech act interpretations within a nonmonotonic framework
the defaults that characterize misunderstandings have a lower priority than the metaplans because speakers consider misunderstandings only when no coherent interpretation is possible
the most common type occurs within the turn itself or immediately after it before the other participant has had a chance to reply
at any given moment speakers may differ in their beliefs about the dialogue and hence can only assume that they understand each other
in fact similar phenomena can also be found in english NUM therefore the precision will surely be improved when syntactic information is used to further filter candidates
in addition to make sure only a small portion of terminology words had been missed NUM NUM words were randomly selected from the rest NUM NUM words and only NUM were found to be terminologies
it can help to decide texts category
from table NUM and table NUM we can see about one fourth of new words are terminology words another one fourth are proper names the rest are other domain specific words
network or have meanings outside of science areas such as f tl agent and procedure
so g l and g are only parts of longer candidates computer and NUM afghanistan
it significantly reduces most of the hard work which should be done manually and reduce the effort and time which are needed to transport a natural language processing work from one domain to another
figure NUM comparison between original and augemented dictionary
american airlines co aud anw rican
thus it becomes extremely important that the system present appropriate expansions and that these expansions be ordered using a heuristic that accurately predicts the most likely expansion
in this section we describe how these strategies are implemented within peba ii
an example www page generated using this strategy is shown in figure NUM
as we noted earlier direct comparisons are thus user initiated
the baboon has about the same shoulder height as a domestic dog
it is hoped in this manner to control the cost of using these technologies
however some general comments are possible from my observations of other systems developments
the goal of technology transfer is subscribed to by both government and contractor participants
this person can then experiment in the use of the technology to do these tasks
so some setbacks at least in transferring this technology to the user are necessary
technology transfer is a complex process and the number of issues that must be addressed is large
templates are structured into a hierarchy h temp lates corresponding therefore to a taxonomy of events
in nkrl the set of legal basic templates can be considered at least in a first approach as fixed
the arguments and file templates occurrences as a whole may be characterised by the presence of pro titular codes the determiners
qhe financial daily NUM sole NUM ore reported mediobanca had called a special lx ud meeting concerning plums for capital increkse
this is depicted in figure NUM
it makes no difference for our treatment of parallelism
context unification is the satisfiability problem of context constraints
the second elliptic one is too
parallelism guides the interpretation process for the above discourses
the decidability of context unification is an open problem
editing the forward translation causes an automatic reworking of the backtranslation
figure NUM structure of memt architecture
interactive speech translation in the diplomat project
different mt technologies exhibit different strengths and weaknesses
we work with an informant to map out the pronunciation system for the target language and make use of supporting published information though we have found such to be misleading on occasion
the diplomat rapid deployment speech translation system is intended to allow naive users to communicate across a language barrier without strong domain restrictions despite the error prone nature of current speech and translation technologies
as for the mt component the emphasis is on rapidly acquiring an initial capability in a novel language then being able to incrementally improve performance as more data and time are available
a typical exchange consists of recognizing the interviewer s spoken utterance translating it to the target language backtranslating it to english NUM then displaying and synthesizing the possibly corrected translation
once the action nodes of the graph have been created or perhaps while they are being created the author has the ability to link them together using a set of predefined procedural relations goal precondition sub action side effect warning and cancellation
as proof for it that he praised the reaction of the public opinion in russia as proof of the fact that
the system comes figure NUM sample communication protocol
in this case the antecedent precedes the anaphor
figure NUM dependency tree for examples NUM and NUM
the rows labeled initial choose give the state after hand crafted choose rules are applied while the rows labeled initial delete give the state after the hand crafted choose and delete rules are applied
note that relations defined in tc ws2s are written lowercase
the reason for the choice of comparator entity here lies in the fact that both animals possess sharp spines this is the only salient property the animals share
for example consider the following text extracted from the animal corpus sheep are hollow horned ruminants belonging to the genus ovis suborder ruminata family bovidae
given an entity e we want to describe and some attribute a of the entity we want to communicate we use the algorithm in figure NUM
we apply several operators to text knowledge bases to detenmne which concepts properties and relationships play a dominant role m the corresponding texts and thus should become part of their topic description all of these operators are grounded m the semantics of the underlying terminological logic some of the operators make addltmnal use of cut off values which are heurmtlcally motwated and have been evaluated emptrically
NUM normally i do n t like swimming but this sunday it was so hot that i spent the whole day on the beach and in the water
the analysis of a word form is carried out in two steps lemmatization determination of the derivational root only for semantically transparent derivation affixes
the state of the art and the named special requirement for a retrieval module in an aac device suggest the use of enhanced full text retrieval using semantic expansion of queries
for message selection systems the low communication rate is partially caused by the fact that many systems rely on retrieval methods that put a high cognitive load on the user
the morphological module is indispensable to be able to enhance the system with a query expansion algorithm which is needed to satisfy the minimal input requirement for communication aids
frequently in fact the actual choice of structure assigned by the treebank annotators seemed largely dependent on semantic indications unavailable to the transformational learner
we tested both approaches and the baseline heuristic using part of speech tags turned out to do better so it was the one used in our experiments
another test is the uschtlness of the representation for fllrther processing
we begin by associating a particular set pointer sl with the root vertex
athc prolog symbol leq represents the udrs subordination relation
so in this area another applicalion for semantic underspecitieation is lurking
isu thermore a wide range of existing parsing systems e.g.
several approaches to underspecifica ion axe conceival le
s2 endotracheal tubes are unchanged in
this could potentially have an impact at srs performance
the misalignment gives rise to the context free correction rule from frontal
the process is started by collecting a sufficient amount of training text data
the training was performed on NUM reports and tested on NUM reports
we plan to study these issues in the near future
the system is applied to medical dictation in the area of clinical radiology
this is accomplished by looking up the misaligned words in a broad coverage medical terminology lexicon NUM
this is substantially higher than the advertised NUM error rate
how then can we turn this notion of minimal missing links into a computationall y effective scoring procedure that works in the general case
note that these results agree with the original scoring proposal for the first three cases but agree with intuition for the last two
for example the replacement rules mere were made the remainder and this or the suppon are obtained by aligning the following two sentences sl there were made of this or unes and tubes are unchanged in position
that is there were NUM other correct occurrences of fusion within the corpus of all valid reports i.e. NUM NUM s4 and at best all of these would translate correctly when read into the srs
it should be noted that a typical chest x ray dictation report is quite short from a few lines to a few paragraphs and is dictated quite rapidly in anywhere from NUM seconds to a few minutes
this optimization is necessary to achieve a respectable level of recognition accuracy however it may not guarantee consistently high accuracy performance due to the limited capabilities of the underlying language model usually a NUM or NUM gram hmms
its development can be considered a landmark in the model theoretic semantic analysis of various forms of quantified sentences conditionals and anaphorically linked multi sentential discourse
hence for all the examples given above any of those sentences becomes ungrammatical if the reflexives are replaced by non reflexive anaphoric expressions of
if an intermediate node occurs between reflexive and antecedent which denotes a noun with a possessive modifier the reflexive is d bound by this noun cf
this decision might nevertheless cause trouble if more conceptually rooted text cohesion and coherence structures have to be accounted for e.g. textual ellipses
both the relative clause and the genitive attribute are modifiers of the subject which usually occurs at the first position in the german main clause
on any other occasion e.g. the head of the pronoun is a preposition the message is simply passed on to the receiver s head
to do so we collect all occurrences of string l in the asr output e.g. there were made and determine how many times the rule z r is supported in the parallel sample
for instance a preposition may occur between reflexive and verb since the notion of d binding does not discriminate between nps and pps of
if the finite verb is a modal or auxiliary verb one or more non finite verbs may occur between the reflexive and the finite verb cf
formally choose si which maximizes the following probability max p f is p s isj sk
this distinction becomes crucial for already established relations like has property subsuming chargetime etc or has physical part subsuming has accumulator etc
a small scale evaluation experiment was conducted on a test set of NUM occurrences of textual ellipses in NUM different texts taken from our corpus
the computation of paths between an antecedent z and an elliptic expression NUM however may yield several types of well lormed paths viz
word actors communicate via asynchronous message passing an actor can only send messages to other actors it knows about its so called acquaintances
consequently a searchtextellipsisantecedent message is created by the word actor for ladezeit message passing consists of two phases NUM
the heuristics we propose include language independent conceptual criteria and language dependent information structure constraints reflecting the context boundedness or unboundedness of discourse elements within the considered utterances
nqine dictionaries wlfich contain rich semantic or concel tual information ml w be of help in improving the perforlnan e
tilne n2 and a two atom NUM redicate describes the colwei t relation between two at nns e.g.
pfte system is a versatile parsing system in development which overs a wide range of phenomena in lexical syntactic semantic dimensions
analyzing the strategies human beings employ in pp attachment disambiguation we f mnd that a wide variety of information supplies important clues for disambiguation
when the occurrence frequency on the corpora is low we use preference rules to determine pp attachment based on clues from conceptual information
it is designed as a linguistic tool for at plications in text understanding database generation fi om text and computer based language learning
a context constraint is called satisfiable if it has a solution
the actual threshold to decide between segment boundary and no segment boundary is the parameter NUM which we varied from NUM NUM to NUM NUM
figure NUM f scores as a function of the output unit threshold NUM on unseen data net with w NUM
the pre and post boundary trigger words were then merged and the top NUM selected to be used as features for the neural network
NUM randomly selected errors were used to do the error analysis which consisted of NUM false positives and NUM false negatives
the combined method is only slightly better than the pos method but they both are clearly superior to the trigger word method
symmetrically at the end of each turn the last c x w NUM input units are also padded
all conceptual paths which meet the above linkage criteria for two concepts z and y are contained in the final list denoted by cp v
such a summary repre sentation is called underspecified if a procedure is given with it to terive a set of real semantic representations fl om it
this step yields for each edge j starting in v and for each vertex u at the end of j a set of tree readings b
it permits the program to display chinese text by including an opening and closing annotation which indicated which character encoding the text was using
for this purpose we used conversion programs provided freely on the network gb big5 or created at ciir stc
to create the ddos the discourse component processes each semantic form produced by the interpreter adding its information to the database
combining these three modules into a single integrated model might improve performance since similar information is used in all three decisions
the goal was to offer automatic or user guided query expansion by supplying terms which are related in meaning to the query terms
the topic navigation and keyword lists are very expensive to construct and fail in heterogeneous collections or in domains which change rapidly
the detection function compares two dags x and y by checking every constraint unification equation x in x with any inconsistent or more general constraint y in y
thus the propagation will terminate when enough features are detected or when restrictor includes all the finite number of features in the grammar
the detection function described in the next subsection is called on a and b and selected features are added to the restrictor
in this paper we describe an information theoretic model based on lexical stress that substantiates this common perception and relates stress regularity in written speech which we shall equate with the intuitive notion of rhythm to the probability of the text itself
for this reason we settled on a model size n NUM and performed a variety of experiments with both the original corpus and with a control set that contained exactly the same bins with exactly the same sentences but mixed up
in particular subtracting the stress entropy rates of the original sentences from the rates of the randomized sentences gives us a figure relative entropy that estimates how many bits we save by knowing the proper word order given the word choice
in contrast to postulated syllabletimed languages like french in which we find exactly the inverse effect speakers of english tend to expand and to contract syllable streams so that the duration between bounding primary stresses matches the other intervals in the utterance
by computing the stress entropy rate for both a set of wall street journal sentences and a version of the corpus with randomized intra sentential word order we also find that word order contributes significantly to rhythm particularly within highly probable sentences
the maximum entropy rate possible for this process is one bit per syllable and given the unigram distribution of stress values in the data NUM NUM are primary an upper bound of slightly over NUM NUM bits can be computed
each sentence in the corpus was assigned to one of one hundred bins according to its perplexity sentences with perplexity between NUM and NUM were assigned to the first bin between NUM and NUM the sec null in each perplexity bin
stress as a lexical property the primary concern of this paper is a function that maps a word to a sequence of discrete levels of physical stress approximating the relative emphasis given each syllable when the word is pronounced
we regard every syllable as having either strong or weak stress and we employ a purely lexical context independent mapping a pronunciation dictionary a to tell us which syllables in a word receive which level of stress
jnlike normal nouns fix lalm nes
the paper is organized as follows
the relation of hyponymy is a holds for all word classes and is implemented in germanet as in wordnet so for example rotkehlchen robin is a hyponym of vogel bird
it marks expletive subjects and reflexives explicitly encodes case information which is especially important in german distinguishes between different realizations of prepositional and adverbial phrases and marks to infinitival as well as pure infinitival complements explicitly
we encode two different sorts of artificial concepts i lexical gaps which are of a conceptual nature meaning that they can be expected to be expressed in other languages see figure NUM and ii proper artificial concepts see figure NUM NUM advantages of artificial concepts are the avoidance of unmotivated co hyponyms and a systematic structuring of the data
for example in figure NUM the concept of a cat is shown to biologically be a vertebrate and a pet in the folk hierarchy whereas a whale is only the concept of cross classification is of great importance in the verbal domain as well where most concepts have several meaning components according to which they could be classified
the relation pertains to relates denominal adjectives with their nominal base finanzzell financial with finanzen finances deverbal nominalizations with their verbal base entdeckung discovery with entdecken discover and deadjectival nominalizations with their respective adjectival base mi digkeit tiredness with miide tired
ll it captures semantic intuitions every speaker of german has and it groups verbs according to their semantic relatedness
clustering methods which in principle can apply to large corpora without requiring any further information in order to give similar words as output proved to be interesting but not helpful for the construction of the core net
if on that path the message encounters a head which d binds the sender mediating messages from the initiator that head may possibly govern an antecedent in its subtree
this update of the concept identifier is the final result of anaphora resolution a change which accounts for the coreference between concepts denoted by different lexical items at the text level
the use of constraints as filters becomes evident through the further restriction of this set by the predicates adapted to particular grammatical relations thus taking the notion of satisfiability into account
only if the message reaches a finite verb or a noun which has a possessive modifier is a new message with phase NUM sent and the message in phase i terminates
c s is the minimal number of correct links necessary to generate the equivalenc e class s it is clear that c s is one less than the cardinality of s i e c s isi NUM m s is the number of missing links in the response relative to the key set s
the variance is the quadratic moment w r t the mean and is thus such a measure
by practicing birth control for each bottom up generation of constituents in this way we avoid a population explosion of parsing options
uncountable nouns such as furniture have no plural form and can be used with much
group classifiers combine with plural or uncountable noun phrases to make a countable noun phrase representing a group or set
they have both singular and plural forms and can also be used with much
i has exponentially many semantically distinct parses n NUM yields NUM NUM NUM parses 2deg NUM NUM equivalence classes
alt j e chooses the interpret tion shown in example NUM as its defmflt
the last type of classifier is sp cn s classifiers
when a list of modulators is present as in the occurrence c7 of fig NUM they apply successively to the template occurrence in a polish notation style to avoid any possibility of scope ambiguity
a move consuuctiou like that of occurrence el completive construction is necessarily used to translate any event concerning the transmission of an information i1 sole NUM ore reported
iii a binding occmtence c9 which links together the previous predicative occurrences and which is labeled by means of goal another operator included in tile taxonomy of causality of nkrl
for example the location determiners represented as lists are associated with the m guments role fillers by using the colon operator see cl
we think of binary trees over e as just the set of terms tr constructed from this alphabet
a tree domain is a subset of strings over a linearly ordered set which is closed under prefix and left sister
rcb lexicon x e lcb xemaryaxena rcb this shifts the burden of handling disjunctions to the interpreter
we choose our labeling alphabet to be the set of length n bit strings NUM NUM rcb
this is due to the limitation of the constraints which appear in the definite clauses to pure mso constraints
in section NUM we conduct a set of experiments to determine whether the use of text structure has a positive effect on the performance
it is shown that a text structure thus identified gives a good clue to finding out parts of the text most relevant to its content
the raw keystroke data is being used in two ways
figure NUM potential and actual topics of actual and potential topics plus a set of indices that are used to represent the text
for example it is likely that users may produce unusual sentence input
we anticipate beginning this testing during the summer and fall of NUM
therefore if we assume that ask if and request confirm are possible from the syntactic pattern of the next utterance then the following table can be expected for the next utterance from the dialogue transition networks
the communication strategy can be based on enhanced message composition or the user can rely on a set of pre stored messages together with a selection procedure
thus techniques from information retrieval have to be modified considerably to be applicable to the messages communicated by aac users which typically contain not more than NUM words
we then present the overall design of the retrieval based communication aid and describe the morphological analysis module used for indexing and the ranking algorithm in more detail
note l hal t ll definition of loeally t redicatel etter onlpm to is similar to the definition of the orde r over logical interpretation in the t ri oritized cir umscription
this representation can t e regar led as all assignment of qualitative strength for NUM ref l ell rules all reduces a ur letl of tel resenting a ttriority over preference rules gready
language in order to use hcli l nguage h r iml lemen ation of prioritizcd ireunmcripdon we need lo change t ornnflas in NUM rioritized circumscription into onsl raints in tlclp
is it feasible and beneficial to provide some kind of pared down version of compansion on an aac device
we have also been working with experimental measures which attach higher significance to the collocation frequency measures which in essence trust the recogniser more often
the metrics d2 and d3 can and should be normmised perhaps to the NUM NUM range in order to facilitate integration with other metrics such as the recogniser s confidence value
influence we used a section of the wall street journal wsj corpus containing 102k sentences over two million words as the training corpus for the partial results described here
in these definitions wlw NUM denotes the frequency of co occurrence of the words wl and w2 NUM while 1note that the exact word order of wl and w2 is irrelevant here
their effect is apparent in table NUM especially in the bottom third of the table where the low frequency of the primer pushes d3 down to insignificant levels
for example it seems quite unlikely that any symmetric information measure can accurately capture the co occurrence relationship between the two words paleolithic and age in the phrase paleolithic age
it is our contention that symmetric measures constrain the re ranking proposing process significantly since they are essentially blind to a significant fraction perhaps more than ha f of all co occurrence phenomena in natural language
we anticipate the addition of some statistical reasoning particularly as a heuristic for ordering possible expansions that the system deems appropriate
setting NUM two stage parsing is added to NUM setting NUM coreference resolution for proper names is added to NUM
1o t e rmil us NUM o atl ain high recall whilst maint aining high precision
in these texts there are NUM organization names NUM person names and NUM location names and NUM time expressions in total
NUM pieces of article of 72k bytes were randomly selected to test the coverage of this terminology dictionary
in this paper a semi automatic approach is developed to extract technical words and phrases from on line corpora
human intervention is still inevitable since statistical methods not only generate useful but also noisy words
at the next step most of candidates are also filtered out if their weights are too small
hence the first step of chinese information processing is necessarily to segment the character sequences into word sequences
in addition some shorter candidates were actually parts of longer ones and could n t exist independently
this phenomenon can be explained by the fact that some highly associated candidates still could n t compose terminology phrases
only NUM NUM candidates have frequencies greater than ti ti NUM including NUM NUM available words
this is usually true since NUM favors standard constituents and prefers application to composition most grammars will not block the nf derivation while allowing a non nf one
but imagine removing only the rule b a c b this leaves the string a b b c c with a left branching parse that has no legal nf equivalent
this paper addresses the problem for a fairly general form of combinatory categorial grammar by means of an efficient correct and easy to implement normal form parsing technique
although differences between ccg and l mean that the details are quite different each system works by marking the output of certain rules to prevent such output from serving as input to certain other rules
that is given a suitably free choice of meanings for the words the two parses can be made to pick out two different vp type functions in the model
theorem NUM is proved by a constructive induction on the order of a given below and illustrated in NUM for c a leaf put nf c a
the parser is proved to find exactly one parse in each semantic equivalence class of allowable parses that is spurious ambiguity as carefully defined is shown to be both safely and completely eliminated
ccg was chosen as the grammatical formalism because it licenses non traditional syntactic constituents that are congruent with the bracketings imposed by information structure and intonational phrasing as illustrated in NUM
discourse entities are determined to be contrastive if they belong to the same set of alternatives in the knowledge base where such sets are inferred from the isa links that define class hierarchies
marks intra utterance boundaries with very little pausing marks intra utterance boundaries associated with clauses demarcated by commas and marks utterance final boundaries
this algorithm determines the minimal set of properties of an entity that must be focused in order to distinguish it from other salient entities
the focus assignment algorithm employed by the sentence planner which has access to both the discourse model and the knowledge base works as follows
figure NUM an architecture for monologue genera tion ii bel hl holds rating x powerful
the result is a set of properties roughly ordered by the degree to which they support the given intention as shown in NUM
the architecture for the monologue generation program is shown in figure NUM in which arrows represent the computational flow and lines represent dependencies among modules
while this approach has proven effective in the present implementation further research is required to determine its usefulness for a broader range of discourse types
after these processing stages the results generator produces a version of the original text in which all the proper names which have been detected are marked up with pre defined sgml tags specifying their classes
rules for coml siti ntally onstrut ting s mmnti representations were assigned t y han l t lcb tm grammar rules
there arc mso a good nmnb r of rules tilt NUM wson names sin c care must be taken with given names family nmnes titles e.g.
similarly beativc artists agency might lm al bre viated to at lat n it in th same lexl
for cx mq le ford motor co might lm used in a text whim th ompally is first mentioned but subscquc nl
our approach could straightforwardly be extended to account for additional classes of proper nalnes and the points we wish to make about tile approach can be adequately presented using only this restricted set
association in assoeiation of air flight attendants l he last words of organization names which do not contain of were examined to find trigge r
instance of the proper name being classilic l
our approach to proper name recognition is heterogeneous
the schanklan type of conceptual dependency cd representations e g culhngford NUM lehnert NUM dejong NUM dyer
mann thompson NUM hovy NUM while the sgml and the propositional representations are precoded the discourse level document structures are reconstructed during summarizing the cognitive agents install the respective rst relations only a few of the most necessary and most simple rst relations have been implemented elaboration
the text condensation process examines the text knowledge base generated by the parser to determine certmn chstnbutlons of activation weights patterns of property and relatlonslnp assignments to con
cept descriptions and particular connectwlty patterns of active concepts m the concept hierarchy these constitute the basra for the construction of thematic descriptions as the result of text condensation only the
table NUM the operator u for combining topic de
NUM NUM estimate of channel probabilities and learning of character confusion table
on average the system generates several hundred candidates for a given string
sr1 a relationship or property rp of a salient concept c is considered salient in the context of c lff rpaetzve c rp NUM and it holds that
letter n grams are used to index the words in the lexicon
tlonslnp thus the rcount statement would make no sense since none of the count constructs and the flags make an assertion about the meaning of the concepts revolved they have no influence on the concepts extension cf fig NUM fig NUM illustrates the apphcatlon of multiple knowledge base operatlons resulting in the text knowledge representation for the newly learned concept notebooster as a speclahzatlon of notebook
so how do we find the standard deviation of a non numerical random variable
then follow two quantitative studies without subjects
a single case design was used
the input method used was linear scanning
a they prefer the american amplifier
the implementation which is presented in the following section produces descriptions of objects from a knowledge base with context appropriate intonation that makes proper distinctions of contrast between alternative salient discourse entities
the data structures and notational conventions are given below
l h or the american amplifier
an information structural approach to spoken language generation
the result is a procedural hierarchy such as the one shown in outline form in figure NUM
unlimited vocabulary compansion relies on having a large amount of semantic information associated with each word for the processing within the semantic parser
clearly rules with validity weights of NUM or less are useless but even those below NUM NUM may be of little value
as described the c box method is clearly speaker dependent that is the correction rules need to be learned for each new speaker
context free rules are derived from perceived differences in alignment of a specific pair of sentences but they would not necessarily apply elsewhere
it remains to be seen if the present solution is acceptable and if a degree of speaker independence can be introduced through rule generalization
given this colored framework the por is directly modelled as follows primary occurrences are pe coloured whilst free variables are pe coloured
note that NUM reduction in the colored a calculus is just the classical notion since the bound variables do not carry color information
this class intuitively allowing only nesting functions as arguments up to depth two covers all of our examples in this paper
the work reported in this paper was funded by the deutsche forschungsgemeinschaft dfg in sonderforschungsbereich sfb NUM project c2 lisa
while this is sufficient for a theoretical analysis for actual computation it would be preferable never to produce these solutions in the first place
note that in NUM unification yields a linguistically valid solution ld but also an invalid one le
the second condition formalizes the fact that free variables with constant colors stand for monochrome subformulae whereas color variables do not constrain the substitutions
furthermore we assume that pronouns that are preceded and c commanded by a quantified wh focused antecedent are variable colored whereas other pronouns are pf coloured
for the imitation solution z ad we imitate the right hand side so the color on a must be d
later we will discuss how to ac tuire above information and use it in disambiguation
and statistic models ha ve a ground m themlttical NUM asis
this will not always suffice however a major challenge for handling haitian creole is that NUM of the haitian population is illiterate
in japanese the met corpus of press conference related texts from kyodo news agency was used in the experiment
the screen shot goes on to show that if the previous word is not a person title the system consults the 2nd word prior to the candidate begin tag to see if it is an organization noun prefix such as bank board or court
from this we can infer that a subj sf should not always be raised across domination links in the trees compiled from this grammar
for the head complement and head subject schema these conditions follow from the valence principle and the sfs are comps and subj respectively
we note that the trees produced have a trunk leading from the lexical anchor node for the given lexical type to the root
we therefore only add the entry for the ditransitive verb give which we take to subcategorize for a subject and two object complements
in the following we provide a sample derivation for the sentence i know what kim wants to give to sandy
having an empty subj list at the top of the domination link would then allow for adjunction by trees such as t1
while hpsg has a more elaborated principle based theory of possible phrase structures tag provides the means to represent lexicalized structures more explicitly
each partial projection could be produced by raising some subset of sfs across each domination link instead of raising all sfs
the approach will account for extraction of complements out of complements i.e. along paths corresponding to chains of government relations
returning to the task of scoring coreference for this problematic case we note that a response of a b c d induces two equivalence classes thus partitioning the set of ke y entities into subsets lcb a b rcb and lcb c di
specifically the recall respectively precision error term s are found by calculating the least number of links that need to be added to the respons e respectively the key in order to have the classes align
the main observation to make is that natural language processing is not as effective as we have once hoped to obtain better indexing and better term representations of queries
further not all prepositions contributed equally to the precision and recall rates
unlike previous approaches it is based on a probabilistic context free grammar
in the following p denotes the preposition within the pronomlnal adverb
the cues produced by the system are not perfect predictors of subcategorization
a word pair s weight is mainly decided by its correlation coefficient
the framework appears to extend however to all kinds of cases where structural underspecification and discourse semantic parallelism interact
the interpretation t of a term t under a is the finite tree defined as follows
we believe that the ambiguity can be integrated into the framework of context unification without making such a problematic assumption
the usefulness of other links such as meronymy has yet to be confirmed
for example given the following question we may answer in a word order which indicates that today is the topic of the sentence and little ahmet is the focus
however verbs can be focused in turkish by placing the primary stress of the sentence on the verb instead of immediately preverbal position and by lexical cues such as the placement of the question morpheme
restrictions such that elements to the right of the verb have to be discourse old information in turkish are expressed as features on the arguments of the verbal ordering functions
the arguments of a verb in turkish as well as many other free word order languages do not have to occur in a fixed word order
the advantage of using a combinatory categorial formalism is that it provides a compositional and flexible surface structure which allows syntactic constituents to easily correspond with information structure units
however given a different wh question in NUM the subject and their vowel harmony variants which attach to the noun nominative case and subject verb agreement for third person singular are unmarked
parallel lines indicate the application of rules to combine two constituents together the first line is for combining the syntactic categories and the second line is foe combining the ordering categories of the two constituents
a novel characteristic of this approach is that the context appropriate use of word order is captured by compositionally building the predicate argument structure as and the information structure is of a sentence in parallel
word order variation in relatively free word order languages such as czech finnish german japanese korean turkish is used to convey distinctions in meaning that go beyond traditional propositional semantics
consider the following example grammar NUM
tree readings are treated as objects
tile only semantic composition operation is concatenation
remember that mrs does not use
NUM NUM stuttgart mikec adler ims
this also involved defining weights for the links in order to rank retrieved messages
for many word forms in the messages however the category remains ambiguous
on the other hand we need to make sure that variants of the same name are indeed recognized as such e.g. u s president bill clinton and president clinton with a degree of confidence
this approach gives opportunity to process the regular words not listed in dictionaries new derivatives loan words etc
procedure lnit sets up tile initial class descriptions taking into account only the characters corresponding to the changing sound
domain theory says that in estonian can only one stem end and or stem grade change appear between the pair of the stem variants
in other words il between two stem wtriants no stem grade changes is observed the stem end change holds between them
in evaluation of the similarity in bunrui goi hyou we give a c if the answer is unique and right a a if the answers contain the right answer and x if the answers do n t contain the right answer
we found that robotag s best english results were obtained with a sampling ratio of NUM a radius of NUM and certainty factors of NUM NUM for pruning for all the tag types
robotag performance for the proper noun tagging task in english and japanese is compared against human tagged keys and to the best hand coded pattern performance as reported in the muc and met evaluation results
we also thank dr kristiina aokinen naist and the anonymous reviewers for valuable conlinetll s
itowevcr the idea of using delwed lexical choice in reverse makes it possible widlout modifying the parsing and generation algorithms to parse certain types of ill formed inputs and to generate corresponding well formed outpul s using the same shared linguistic descril tions
the profit system gives an efticient encoding of typed feature structures
null wc have addressed the issue of bidirectional use of shared linguistic descril tions rather than robust parsing
our fralnework seeks to combille the elegance of a typed feature fornlalisln and iipsg syntactic theory with efficient processing
the results are shown ill table NUM
table NUM restflts with processing head words
oil low occltrr ulces ill corpora
graalllnlar book and japan time s
there have been many proposals to attack this problem
we then describe how this information is used in disambiguating pp attachment
prepositional phrase attachment through a hybrid disambiguation model
this population requires limited output structures comprised primarily of fairly simple sentence structures
finally we note that extending this measure from a single key equivalence class to a n entire test set t simply requires summing over the key equivalence classes
this score appeals to the intuitive notion that of the three links necessary to make the key entities fully coreferential the response only provides two
c s m s c s isi NUM ip s i NUM
the partition is of size NUM and so the minimal number of links that need to be added to reunite the partition is just NUM
in the case of recall we conceptually needed to add links to the response building up the response s equivalence classes so as to end up with the key
NUM in phase NUM the message is forwarded to the subject of the finite verb or if a noun d binds the reflexive to the possessive modifier of the noun
priorities are determined based on the independent perlormance of each tree
phrasal template rules are applied in order to delimit proper names
companlos persons locations dates figure NUM english performance results
also designators have a large impact on resolving conflicts
all features necessary to apply rules and trees are attached
the generated tree is applied to test data and scored
the rest can be generated automatically through machine learning techniques
the particular lexical units of interest here are proper names
features strengths were measured for companies persons and locations
the second leads to success by another projection step
in addition note that the restriction that NUM in all nonmonotonic rules and the special cases for s fail ensures that the application of one nonmonotonic rule never destroys the applicability of a previously applied rule
the 316ltuses sparingly energy b c
this condition gives an overall accuracy score of NUM NUM
the grammar formalism we use for a survey cf
head denotes a stxuctural relation within dependency trees viz
conceptual criteria are of tremendous importance but they are not sufficient for proper ellipsis resolution
as only a single well formed role chain from clock miiz pair to 316lt can be determined viz
class NUM the two words are two highly associate words but have no direct syntactic relations
this assumption does not hold for users with cognitive impairments
no chinese grammar is yet available for plum
track incremental progress during the course of system development
automatic segmentation gives a similar increase in performance
gray elements are not yet available for chinese
as a second example consider foreign names
we relied on user hand segmentation to identify words
figure NUM factors determining part of speech error rate
developing either from scratch is quite time consuming
how do we manage expectations in an unknown situation
aliases for person names are also fairly straight forward
thus it is not seen for purposes of system development
this problem is accentuated by the fact that in the search for more accurate probabilistic language models more and more contextual information is added resulting in more and more complex conditionings of the corresponding conditional probabilities
we want the weight NUM to depend on the context ck and in particular be proportional to some measure of how spread out the relative frequencies of the various outcomes in context ck are from the statistical mean
the statistical language model usually consists of lexical probabilities which determine the probability of a particular tag conditional on the particular word and contextual probabilities which determine the probability of a particular tag conditional on the surrounding tags
the second occurrence of NUM q3 in the same sentence does not agree with readers expectation thus potentially confusing
this includes combining atomic messages into aggregated messages choosing cue words and determining paraphrases that maintain focus and ensure coherence
when two attributes have the same rank the system breaks the tie based on a priority hierarchy determined by the domain experts
extending the current aggregation algorithm to be more general is currently being investigated such as combining related messages with different actions
three main criteria were used to design the combining strategy NUM domain independence the algorithm should be applicable in other domains
because of the parallel structure created between the first NUM messages readers are expecting a different date when reading the third clause
using the same example e2 s1 NUM d1 and el NUM d2 have three distinct attributes
by tracking which attribute is compound a third message can be merged into the aggregate message if it also has the same distinct attribute
in most systems the user must remember an access route or in some cases a code in order to speak a message
a semantic path is a series of semantic relations which can be used to reach a lemmatised message word from a lemmatised input key word
semantic relations between a lemma and some word form on the one hand differs considerably from the semantic relations between derived words and their root
the load placed on the user means that he or she is only able to select from a small number of different things to say
several ideas for improving the text retrieval algorithms and wordkeys and their inclusion in other communication aids are still waiting to be realized
selectional restrictions of verbs for nouns will however be automatically extracted by clustering methods
the threshold of t2 was set to NUM only NUM NUM words frequencies in cw corpus were three times higher than in xn and then satisfied this threshold limitation
for example even if local area and network always appear as the substrings of local area network in a corpus this method generates redundant strings such as a local a local area and area network
in their approach the bigrams are valid only when there are fewer than five words between them
it is based on the idea that there is a string which is used to induce a collocation
it is interesting to note that the strings which do not belong to the grammatical units also take high entropy value
NUM in table NUM evaluation is done by human check and NUM collocations are regarded as meaningful
through the method various range of collocations which are frequently used in a specific domain are retrieved automatically
this method is useful for the languages without word delimiters and for the other languages as well
the relationship encoded in many dictionaries and thesauri is synonymy and often some hypernyms are also included
for verbs wordnet makes the assumption that the relation of entailment holds in two different situations
the system relies on a large lexicon for the automatic indexing of messages and for semantic query expansion
when in doubt germanet refers to the degree of polysemy given in standard monolingual print dictionaries
there is strong emphasis of re usability of the software especially the lexicon modules for other aac systems
the presence of a catalogue of standard basic templates which can be considered as part and parcel of the definition of the language
this led us to the description of a concurrent parsing algorithm which is characterized by a depth first robust yet incomplete analysis of textual input
in this section we introduce the parsetalk system whose specification and implementation is based on an object oriented inherently concurrent approach to natural language analysis
in order to establish e.g. a dependency relation the syntactic and semantic constraints relating to the head and its prospective modifier are checked in tandem
these restrictions nmst be net in order to establish a dependency relation between the receiving word actor and the sender of the n essage
3as an alternative to the immediate establishment of a dependency relation a hoadfound message can be returned to enable the subsequent selection of preferred attachments cf
especially the latter consumes large computational resources since for each interpretation variant a knowledge base context has to be built and conceptual consistency must be checked
based on a conceptual parsing model this approach took a radical position on full lexic dization and communication based on a strict message protocol
thus under realistic conditions these techniques loose a lot of their theoretical appeal and compete with other approaches merely on the basis of performance measurements
note that in contrast to the searchhead message the phraseactor lbrwards the copy message to its root actor from where it is distributed in the tree
the dependency relation connecting the copied phrases indicated by the bold edge in the newly built phraseaetor is created by the establish message
benner b bussi re m debellis j silver and s sparks for their comments and suggestions and t caldwell r kittredge t korelsky d
in a data model it is important that names be used consistently for naming objects and relations since otherwise the model is difficult to understand whether or not modex is used
the two statements are presumably incompatible but the correct one can only be chosen on the basis of knowledge about the fictitious gulfinkel worrow domain which modex lacks
modex certainly belongs in the tradition of these specification paraphrasers but the combination of features that we will describe in the next section is to our knowledge unique
in fact while such conventions appear to be limiting at first they serve a second purpose namely that of imposing discipline in naming
for example it would be illegal for the section NUM to belong to two his institution each section belongs to exactly one course
it is widely believed that graphical representations are easy to learn and use both for modeling and for communication among the engineers and domain experts who together develop the oo data model
for example we are extending the system to allow the user to enter free text associated with particular objects in the model such as classes attributes
note that any language whose formulas can be converted to automata in this way is therefore guaranteed to be decidable though whether it is as strong as the language of ws2s must still be shown
we plan to iteratively refine the interface by doing usability studies of our prototype with current users of the communic ease map tm
this edge is marked as usual but with tile syntactic feature structure and the a drs built from the idiomatic information of ptiraseo li x
first one can abstract over a complete drs partial drs or one can abstract only over a single discourse referent predicative drs
they are either frequently used words such as i t computer and
candidate terminology phrases are those word pairs which are composed of one terminology word and another word inside this terminology s border window
since chinese name recognition is also an complex problem in chinese real text processing this method can also be utilized to recognize names
in fact this approach will soon be embedded into an integrated chinese information processing system fdasct NUM
even in more universal applications such as semantic analysis and translation terminologies also play important roles and therefore require special treatment
therefore before further processing those domain specific words which are unavailable in the dictionary should be extracted and added to it
due to the availability of large scale on line real text corpus based natural language research has become one of the focuses of computational linguistics
this table contains more than NUM chinese function words such as of and be
among the NUM most frequently used words far below NUM of them are longer than NUM characters NUM
for example computer science dictionaries coverage of computer science terminology ranged from NUM to NUM NUM
furthermore the language of NUM ws2s is sufficiently close to standard prolog like programming languages to allow the transfer of techniques and approaches developed in the realm of p p based parsing
a direct comparison with nymble on particular tag types is not possible because only the overall f measure is reported for the muc NUM task
in this paper we elaborate on the example of scope and ellipsis interaction
figure NUM solving the context constraints of example ll i
therapy is often geared toward developing strategies that overcome or compensate for these production problems
the first icon in the sequence the category icon establishes the word category
one can imagine this particular aspect of the system being expanded for therapy sessions
fig l the architecture of the system NUM grammar checking extended variant of syntactic parsing this is the main part of the system
besides that operations on hundreds or thousands partial trees are very ineffective and they can also slow down substantially the processing of the given sentence
in order to demonstrate this problem we will take this sentence and modify it trying to find out what the main source of ineffectiveness of its parsing is
among the suitable candidates there is for example the prepositional phrase v telefonickem rozhovoru in the telephone discussion
another change we would like to demonstrate is the deletion of all other free modifiers the result of which is a certain backbone of the sentence
our data structure is a set of attribute value pairs with the data about valency frames of particular words as the only complex values embedded attribute value pairs
the same approach may not be used for error localization and identification although the cases when parsing in layers fails on a correct sentence are quite rare
conclusion the main purpose of the demo of our system is to demonstrate a method of grammar based grammar checking of a free word order language
in other words each symbol used by rfodg belongs to exactly one set from each pair of sets under a b and c
while the compansion prototype is viewed as a promising and successful application of nlp to aac it raises some questions when viewed as a practical aac system
of course there is a tremendous amount of cognitive load associated with memorizing abbreviations and with developing memorable abbreviations for a large number of words
this procedure is performed for each string obtained in the previous stage
one possible solution is to eliminate unnecessary strings at the second stage
owing to this the method have good applicability to many languages
table NUM result of the second step
retrieving collocations by co occurrences and word order constraints
consider the example of figure NUM
the majority of invalid collocations are of this type
they are quite representative of the given domain
we performed an experiment for evaluating the algorithm
in some instances the fault may be that the system has not yet been programmed to deal with certain constructions e.g. certain kinds of verb compliments
table NUM spanish specific features for s s s
the weighted averages of the p r measures across all languages for companies persons locations and dates are shown in figure NUM table NUM shows comparisons to other work
the development of natural language proccssing nlp systems that perform machine translation mt and information retrieval ir has highlighted the need for the automatic recognition of proper names
a nmltitrce approach was chosen over learning a single tree for all name classes because it allows for the straightforward association of features within the tree with specific natllc classes and facilitates troubleshooting
the test set consisted of NUM new messages
delimitation occurs through the application of phrasal templates
sophisticated grammatical output the sentence generator used by compansion relies on a grammar which necessarily encodes some limited set of grammatical constructions
we have analyzed tile results in terms of how much each module of the system contributes to the proper nmne task
l erson or location name s as well as normal nomls att l their coml inations
co plc based on the company designator list provided in the muc6 reference resources
we take advantage of graphological syntactic semantic world knowledge and discourse level information to perform the task
these constituents are then treated as unanalysable units during the second pass which employs a more general sentence grammar
a a discourse interl retation the discourse inl rt r t w
the non terminal nnp is the tag for proper name assigned to a single token by the brill tagger
digger words are detected by matching against lists of such words and are then specially tagged
however two of the issues i have discussed are of paramount importance
government off the shelf software gots may perhaps provide a better answer
the research fields sponsored by tipster are application oriented in any case
training for endusers managers and support personnel should accompany installation
for tipster demonstration projects undertook this step individually in phase ii
this paper summarizes what i have learned from the phase ii effort
language exists for the communication of meaning
the second is collaboration with the user
tipster participants have been encouraged to commercialize their ideas when feasible
the contributions of both groups are necessary
the first alternative leads to failure via further projection or imitation as s c does not contain j as a subtree
in this case there exists a context function NUM such that al NUM al and a2 NUM a
imitation presents two possibilities for locating the argument j of the context variable c as a subtree of the two arguments of the rigid head symbol
an implementation of a semi decision procedure for context unification has been carried out by jordi l6vy and we applied it successfully to some simple ellipsis examples
note that context functions can be described by linear second order lambda terms of the form ax t where x occurs exactly once in the second order term t
the parallelism imposed by ellipsis requires the scope of the quantifiers in the elliptical clause to be analogous to the scope of the quantifiers in the antecedent clause
from the trees in NUM one can not construct a context function to be assigned to c which solves the parallelism constraints in NUM
during the last few years the phenomenon of semantic underspecification i.e. the incomplete availability of semantic information in processing has received increasing attention
a solution of a context constraint is a variable assignment a such that a t a t for all equations t t in NUM
first we discuss the overall architecture for the l obotag system
first we describe a general client server architecture used in learning from observation
c4 NUM represents training examples as fixed length feature vectors with class labels
the decision trees are trained from texts which have already been tagged manually
the scores on the test set are shown in the table NUM
there are a number of ways in which robotag performance could be improved
then we give a detailed description of our novel decision tree tagging approach
to resolve these cases robotag uses a static tag priority scheme
next we focus on the machine learning algorithm employed for tag learning
robotag design was motivated by our goal of developing an interactive learning system
the interface works with multiple languages and includes support for both single and double byte coding schemes
it uses the new confusion table for the next pass correction of the ocr text
candidates must share at least some features with the input string query
i v argmaxw pr wi s NUM
pr xly is the probability that letter y is replaced by letter x
the corrected ocr text therefore has NUM fewer real word errors than the original ocr text
in such cases the system replaces the presumed non word error with a word from its lexicon
table i gives the number of real word and non word errors for literal words in the ocr data
we degraded the print quality of the documents by photocopying them on a light setting
the improvement in performance is gained principally from the reduction of the real word errors
this is NUM NUM higher than the rate achieved in the context based non word experiment
each proper noun is then classified into an appropriate type e.g. person entity place using decision trees id3 an easier task than also learning to place tags
in addition we supply access to conceptual knowledge via a kl one style classification based knowledge representation language
of course the government relation between antecedent and reflexive need not be an immediate one
an actor can send messages only to other actors it knows about its so called acquaintances
the parsetalk specification language in addition incorporates topological primitives for relations within dependency trees
the company compaq which develops the lte lite equips it with a pci motherboard
in particular they treat pronominal coreference and anaphora i.e. reflexives and reciprocals
as a general rule the anaphor must not occupy the position of the reflexive pronoun
another special case arises if the antecedent is a modifier of the subject of a sentence
the search of an antecedent is triggered by the attachment of the definite article to the noun
an antecedent bund message is created which changes the concept identifier of rechher appropriately
the second most common class of errors involved conjunctions which combined with the former class make up half of all the errors in the sample
first and foremost english count nouns admit a morphological contrast between singular and plural mass nouns do not being almost always singular
comprising the denotation of the count noun where what constitutes a part is relative to the kind of object denoted by the count noun
and of course many virtues are one of a kind honesty bravery courage chastity sincerity gratitude and fidelity
at the same time it is equally clear that productive conversions of this sort can give rise to lexicalizations bauer NUM ch
here i have in mind such common nouns as kleenex band aid hoover and xerox which clearly derive from proper names
the class any no value should not be used when defining linguistic knowledge
therefore its computational properties are unclear and needs more investigation
for simplicity multiple inheritance between classes will not be allowed
there is however no hinder in excluding the use of other values here
however this possibility is mostly limited and does not include nonmonotonic operations
when the rule is applied the new information obtained would be NUM iq NUM
one of the texts produced for the save a file procedure is shown in figure NUM
it can also infer some of the actions involved in using these objects
this is done in the workspace with a graphical outlining mechanism
NUM introduire un titer dans la zone de texte filenarnestring
we are also grateful to jon barber who assisted in the implementation of this system
you can quit the save as dialog box by clicking on the cancel button
these changes are conditioned by the certain context arising after either the stem end or the stem grade change e.g.
system has to create class descriptions from the available data characters and their belongness to the sound classes
the endotracheal tube is in size factor position there is and re expansion of the fight upper lobe
we have developed a method of improving and controlling the accuracy of automated continuous speech recognition through linguistic postprocessing
given this equation lc becomes 2a with solutions 2b and 2c primary occurrences are underlined
consequently we require that the variable representing the fsv be pf colored that is its value may not contain any pf term
as required no other solution is possible given the color constralnts in particular ax l jpe spf is not a valid solution
given definition NUM NUM 4a is then assigned two fsvs namely NUM a gd lcb l j x l x e wife rcb b
the features of a syntactic pattern with possible entries are shown in figure NUM
the contrast predicate specifies that pairs of themes or rhemes that explicitly contrast with one another be realized together
previous approaches to the problem of determining where to place accents have utilized heuristics based on givenness
to correct for this we modified the randomization algorithm to permute only open class words and to fix in place determiners particles pronouns and other closed class words
a lexical trigram backoff smoothed language model was trained on separate data to estimate the language perplexity of each sentence in the corpus
we find that the stream of lexical stress in text from the wall street journal has an entropy rate of less than NUM NUM bits per syllable for common sentences
the consistently positive difference function demonstrates that there is some extra stress regularity to be had with proper word order about a hundredth of a bit on average
phonologists distinguish between three levels of lexical stress in english primary secondary and what we shall call weak for lack of a better substitute for unstressed
the phrase recently were filed can be syntactically permuted as were filed recently but this clashes filed with the first syllable of recently
in the wall street jour null nal corpus we find such sentences as the fol lowing is sues re cent ly were filed with the se curi ties and ex change com mis sion
we test two models in order to verify the efficiency of the proposed model
to produce our decision trees an information theoretic criteria called information 1robotag uses decision tree classifiers as part of the learned tagging procedure
in our case this means characterizing text positions where a tag should begin or end from text positions in which it should not
now we can define a discourse structure analysis model with the statistical speech act processing
compact automata can be easily constructed to represent dominance and precedence relations
they tackled data sparseness by generalizing the word to the class which contains the word
and the coetlicient of variation for all similarities measured by the corpus is NUM NUM
the proposed method uses the handmade thesaurus as the different resource from the corpus
to adhere to this tradition ill current work the stem changing rules are represented in tile following way r a c this says that tile string a is to be replaced by rewritten as the string b whenever it is preceded by c the left context and rcb bllowcd by c the right comext
note that the subdag under the lc arc is the rule used to rewrite the constituent on the left corner path and the paths from the top node represent which top down constraints are propagated to the lower level
NUM in the example when the detection function is called on d NUM and d NUM after the first recursive application the feature owner is selected and added to restrictor
this means no top down constraints are propagated to the lower level therefore the parsing becomes pure bottom up
the dag d NUM in figure NUM represents the initial application of rl to the category np
therefore if a newly created dag is incompatible or more general than existing dags rule application continues
the subsumption relation is defined as dag d subsumes dag d if d is more general than d
NUM then d NUM is replaced by NUM NUM and the propagation is re done once again
we also indend to do more exhaustive case analysis and investigate the selection ordering of the detection function
a left corner parsing algorithm with top down filtering has been reported to show very efficient performance for unification based systems
a histogram showing the amount of training data in syllables per perplexity bin is given in figure NUM
but both measures overestimate the entropy rate by ignoring longer range dependencies that become evident when we use larger values of n
we regard this as a first step in quantifying the extent to which metrical properties influence syntactic choice in writing
prose rhythm analysts so far have not considered the syllable stream independent from syllabic phonemic or interstress duration
expectedly adding the symbol increases the confusion and hence the entropy but the rates remain less than a bit
it is clear that the perplexity bins are well trained but not yet that they are comparable with each other
this measure gauges the average surprise after revealing each word in the sentence as judged by the trigram model
we discarded sentences not completely covered by the pronunciation dictionary leaving NUM NUM million words and NUM NUM million syllables for experimentation
one source of error in our method is the ambiguity for words with multiple phonetic transcriptions that differ in stress assignment
in speech production syllables are emitted as pulses of sound synchronized with movements of the musculature in the rib cage
most of quadruples in test data are not in the training data however
section NUM compares our approach to that of others for simi1 more formally a semantic representation is monotonic iff the interpretation of a category on the right side of a rule subsumes the interpretation of the left side of the nile
the proposed method avoids data sparseness by estimating undefined similarities from the similarity in the thesaurus and similarities defined by the corpus
however it will be important in future research to investigate how much weight should be given to each bit of data
the aim of this paper is to automatically define the similarity i etween two nouns which are generally used in various domains
in this paper we propose a method distinct from the above methods which use a handmade thesaurus to find similar words
in this method relatively few bits of cooccurrence data are used because nouns in the cooecurrence data are not on bunrui goi hyou
llowever the thesaurus constructed by such ways does not contain so many nouns and these nouns are specified by the used corpus
the obtained similarities are obviously the same in number as the original similarities and are more appropriate than the original similarities in the thesaurus
certainty factor this parameter affects de null cision tree pruning a process used to simplify learned decision trees
each potential begin and end tag produced by the decision tree also has a confidence rating a number between NUM and NUM estimating the chance of correct classification
these scores were achieved using NUM NUM words of tagged text NUM times the size of the NUM NUM word training set used for the robotag experimental results reported here
since 2c contains a primary occurrence it is ruled out by the por and is thus excluded from the set of linguistically valid solutions
nevertheless such a structuring is important because note that these are not notationally distinguished up to now this still needs to be added
certain wordnet relations are modified to cross word classes verbs are allowed to cause adjectives and new cross class relations are introduced e.g.
the sf mapping component maps a shallow parse structure of a main clause in a pronominal adverb correlative construct to a set of putative subcategorization frames reflecting structural as wen as morphological ambiguities in the original sentence
the architecture of the system the design of the whole system is shown in the fig i
we do not use any partial results for the evaluation of the possible source of an error
in order to speed up the processing even more we have to use another type of simplification
dejmal to function of minister of environment
the union of terminals and nonterminals is exactly the set of all symbols used by rfodg
the composition of a grammar checking module tries to stick to this idea as much as possible
the limited space of this paper does not allow to present the full description of rfodg here
the processing time is NUM NUM s i NUM structures were created and NUM items were derived
if he appears in a sellteltce as the subject and the subject in the previous sentence is male then it is NUM referal le that he refers to the previous subject
the difference is that locally NUM redicate better omparator onsiders assignment s for variabh s in constraint s in iiclp whereas t he order over h gical interpret alton
ill this patter we use our hclp language based on a l oolean constraint solver to get tlj most t rcferal lc models from t ref er mce rules rot resented as bo h mt constraints in tlt hc l1 language
this effort focuses on a particular user population which enables us to constrain the system processing sufficiently to make the nlp application feasible
if desired it would be fairly easy to add priorities to the nonmonotonic rules and thus induce a preference order on explanations
we then divide this into none which represents that a structure can not have any value and any value which contains all actual values
the second constraint placed on the formalism the possibility of defining an inheritance hierarchy is not essential for the definition of nonmonotonic operations
we also noted that the method can be applied to all domains of structures where we have a defined subsumption order and unification operation
in this paper i want to allow the user to use other forms of nonmonotonic inferences and not only the normal default rule given above
there are two main properties that will be assumed of a unification based formalism in order to extend it with the possibility of defining nonmonotonic constructions
in this section i will show how some of the most common nonmonotonic extensions to unification based grammar can be expressed by defining rules as above
this approach can significantly reduce the manual effort in the generation of terminology dictionary
then compound words are extracted from the combination of terminology words and other words
then compound words which are combined by terminology words and other words are generated
a small window is put over each terminology word appearing in the text sequence
first there are scarcely any available machine readable chinese dictionaries for specialized domains
for example haas introduced a method for automatic identification of snblanguage vocabulary words
the remaining NUM NUM were potential new words and then further examined by computer professionals
the basic idea is the one given in section NUM namely that natural language expressions are not directly translated into discourse representation structures drss but into a representation that describes several drss
integration with other aac software should be investigated such as software designed for unique text entry word prediction systems and for the rapid use of quick conversational fillers
only that analysis and not the other is allowed to continue on and be built into the final parse of NUM
the results of the present paper apply to such restricted grammars and also more generally to any ccg style grammar with a decidable rule set
ccg may be regarded as a generalization of context free grammar cfg one where a grammar has infinitely many nonterminals and phrase structure rules
for most linguistically reasonable choices the proof of theorem NUM will go through NUM so that the normal form parser of ss4 remains safe
preferableto may be defined at whim to choose the parse discovered first the more left branching parse or the parse with fewer non standard constituents
the motivation for producing only nf parses as defined by NUM lies in the following existence and uniqueness theorems for ccg
the present proofs for ccg establish a result that has long been suspected the spurious ambiguity problem is not actually very widespread in ccg
however their method relies on the grammaticality of certain intermediate forms and so can fail if the ccg rules can be arbitrarily restricted
the main contribution of this work has been formal to establish a normal form for parses of pure combinatory categorial grammar
a series of evaluations has been done on tile system using a blind test set consisting of NUM wall street journal texts
nearly half of the t rol x name rules ard for n ganisation names be ausc they may contain fmther proller name s e.g.
before parsing an attempt is made to identi y proper naine phrases sequences of proper names and to classify them
in what tbllows we describe how proper names are recognized and classified in lasie by considering the contribution of each system component
given fort lauderdale fla if we know fla is a location name fort lauderdale is also classified as a location name
the non terminals list locjnp list 0rgan np and cdg ip are tags assigned to one or lnor input tokens in the name phrase tagging stage of lexical preproeessing
there are NUM such rules ill total of which NUM are for organization NUM for person NUM for location and NUM for time exl ressions
we have described an ie system in which four classes of naming expressions organization person and location names and time expressions are recognized and classified
the results of evaluating the system on a blind test set of NUM articles are presented and discussed in section NUM section NUM concludes the paper
in this model each object actor constitutes a process on its own
we started out with a rather liberal conception which allowed for almost unconstrained parallelism
this leads us to a parsing algorithm with restricted parallelism whose experimental evaluation is briefly summarized
the parser operates as the nlp component of a text knowledge acquisition and knowledge base synthesis system
messages were forwarded strictly from right to left wandering through the preceding context rather than being broadcasted
the figures depict the main steps of word actor initialization head search and phrasal attachment
these requirements obviously put a massive burden on the control mechanisms of a text understanding system
furthermore the chart parser performs an extremely resource intensive subsumption checking method unnecessary in the parsetalk system
next the new containeractor will be sent an analyzewithcontext message to continue the parsing process
provided that these constraints are fnltilled an attach message is sent to the encapsulating phraseactor
table NUM results from context dependent real and non word error correction
fully countable nouns such as knife have both singular and plural forms and can not be used with determiners such as much
once an appropriate translation of the classifier has been found knowledge of its type allows the system to decide the appropriate form of the final translation
the second type of classifer typical consists of those classifiers which are descriptive in their own right such as teki drop
lal mw se does noi ta ve contrasting singular and l hlr fl forms of nouns
c could be used as a elassiiier with the amount n in its scope NUM or c could have anaphoric reference NUM
if there is no nonpluralia tanta translation equivalent then the translation will default to x c of n as above but with n headed by a bare plural noun
in alt j e it is done by examining the semantic category of the embedaif n s countability preference is pluralia tanta then n will never be individuatcd
the az mysis has rcb e n imt lemen x d in ntt s at a nes lx english
finally the system s interface must be carefully designed so as to make it easy for users to learn and use
in addition to the theoretical grammar testing described above we also plan an informal evaluation of the usability of the system
syntactic frames and particle verbs deserve special attention in the verbal domain
cinch biiren aufbinden it is necessary to suppose a three argument relation rxyb with two open variables x represents the subject np and y the indirect object np
in order to extract learning features the robotag server employs a preprocessor plug in for each language it operates with
in our case this means learning to label tokens as begin or end tags from the token s lexical features
notice that in the above cases the relation between the idiom components bock biir and its paraphrased referents fchler liigcngcschichte is not a metaphorical one but a conventional one
these two relations are implemented and interpreted in germanet as in wordnet
this technique allows us to pm se sentences like NUM NUM where one part of the idiom is modified ail NUM ilot the idiom as whole
a single token usually does not contain enough information to decide whether it makes a good tag begin or end
how many neighboring tokens to use is determined by a radius parameter as will be discussed in section NUM NUM
the rest of bi gram tri gram NUM gram candidates constitute NUM separate tables
and NUM NUM of all the program output was judged to be correct
and those nonnoun phrases would also be discarded since terminologies are always nouns
new words are extracted from the top NUM of this table
he concludes that complex stemming algorithms can be slightly more effective than simple ones and that the removal of derivational affixes is not always desirable
in this state some cases can be expected for the dialogue
we construct a statistical dialogue model based on speech acts as follows
these NUM dialogues consist of about NUM NUM utterances NUM NUM words total
the second case is to return to the ut terance NUM
rather dialogues have a hierarchical structure
initially the set of properties to be contrasted cse is empty
null 5the implementation assigns slightly higher pitch to accents bearing the subscript c e.g.
these rhetorical predicates employ information structure to assist in maintaining the coherence of the output
this paper presents an architecture for the generation of spoken monologues with contextually appropriate intonation
top level properties such as those in NUM are realized by the matrix verb
the theme of each utterance is considered to be represented by the material repeated from the question
different sets of propositions would be generated for other perhaps thriftier hearers
the set of alternatives is determined by the hierarchical structure of the knowledge base
in fact if the intonational patterns of the two examples are interchanged the resulting speech sounds highly unnatural
that is the content planner ensures that the theme of an utterance links it to material in prior utterances
while various name recognizers have been developed they suffer from being too limited some only recognize one name class and all are language specific
in the first experiment the english trees generated from the feature set optimized for english are applied to the spanish text e e s
the words build make manufacture and produce can be associated with the subject category make type verbs
the numbers directly beneath a node of the tree represent the number of negative and positive examples present from the training set
some features such as morphological keyword and key phrase features are determined by hantl analysis tff the text
for exampie of all the company nmnes in the english training text NUM are associated with a corporate designator
the problem being considered is that of segmenting natural language text into lexical units and of tagging those units with various syntactic and semantic features
in the second experiment new spanish specific trees are generated from the feature set optimized for english and applied to the spanish text s e s
above we identified three particular types of comparisons that are present in our corpus
in peba ii each corresponds to a particular discourse strategy for generating a hypertext page
each becomes an option when peba i has been asked to describe some specified entity
this is a set of entity types that can be compared against for illustrative purposes
domesticated sheep are also more timid and prefer to flock and follow a leader
peba ii generates the text shown in figure NUM in response to such a query
how do we decide which comparator to choose when there are multiple candidates
there are a number of interesting research issues here how is a comparator entity selected
knowledgebases for lexical transfer mt can be developed in a matter of days or weeks those for structuraltransfer mt may take months or years
the speech recognition system continuously listens thus the participants do not need to physically indicate their intention of speaking
the specification of lexiealized communication primitives allows heterogeneous and local lorms of interaction among groups of lexical items
a tag postprocessing step resolves overlapping tags of different types using a prioritization scheme
constraint must be enforced a term occurrence appearing in an equation must appear unchanged in the term assigned by substitution to the free variable occurring in this equation
given the discussion above it is immediate to see that h NUM has to be instantiated with pf the projection binding kzw w
fortunately the hocu framework is just this different colors can be used for different types of primary occurrences and likewise for different types of free variables
second their method is a generate and test method all logically valid solutions are generated before those solutions that violate the por and are linguistically invalid are eliminated
more specifically given part of an utterance u with semantic representation sere and foci f1 f n we require that the following equa
again the por will rule out the incorrect solutions whereby contrary to the vp ellipsis case all occurrences that are directly associated with parallel elements i.e.
the application framework for the parsing device under consideration is set up by the analysis of realworld expository texts viz
generating a scopally resolved lud representation from an underspecified one is the process which we referred to as plugging in section NUM NUM
the macro states that a transitive verb introduces a basic predicate of a certain relation with an instance and a label
a lud representation also has a semantic subcategorization list under the feature subcat which performs the same function as a a prefix
a category symbol like np in the rule above also stands for the entry node of its associated feature structure
the arguments instances are accessed via the verb s subcat list and get bound during functor argument application cf
note that the only relevant piece of information contained in a lud representation for the purpose of composition is its context
for building lud reprcsentations we use a lambda operator and functional application in order to compositionally combine simple lud representations to complex ones
the notions of monotonicity and underspecification were discussed and lud a description language for underspecifled discourse representation structures was introduced
the transfer component takes the possibly resolved semantic analysis of the input and builds a target language representation
at any moment in the dialogue a user may activate the verbmobil device and start speaking his her native language
while the method allows them to spell any word in actuality spelling is rather limited and the spelled vocabulary items may be easier to anticipate
thus the following equivalence is valid over finite trees x x c x c x context constraints are also more general than equality up to constraints over finite trees which allow to describe parallel tree structures
in many cases clear cut context free rules like the one given above axe hard to come by
l fusion count NUM r effusion r effusions r effusion or min weight NUM NUM
examination of the r n ed sf table shows that frames with a low NUM log value consist mostly of errors
any clause in wldch a possible locus of attachment is morphologicauy ambiguous is mapped with the appropriate rule applied to all morphology alternatives
these results were compared to the blind judgements of a single judge NUM were found to be correct NUM incorrect
however if only the prepositional frames listed for these verbs are considered the rates drop to appro mately NUM and NUM respectively
a lower bound for the precision of the system is given by the number of learned correct frames divided by the number of learned frames or NUM NUM
false cues stem from incorrect decisions in the disambiguation component as well as parsing and mapping errors spurious adjuncts or actual errors in the original text
rules for obtaining semantic constraints from binary syntax trees are NUM i for every s node p add xp cp x p for any other node add xp x p
thus the conjunction of both clauses has only two readings either the interpretation is the wide scope existential one in both cases two specific european languages as well as two specific asian languages are widely known among linguists or it is the narrow scope existential one many linguists speak two european languages and many linguists speak two asian languages
note that in example NUM the trees representing the semantics of the source and target clause must be equal up to the positions corresponding to the contrasting elements two european languages two asian languages
xs cl two language lamx c3 x s a xs c2 many linguist lamy c4 x s a x spoken by vary var
NUM cs x cl two language lamx c3 x a cs ax cz many linguist lamy c4 x a xs cs spoken by vary varx
the closure operation on NUM i and ii leads to the two possible scope readings of NUM given in NUM i and a constraint set specifying the scope neutral meaning information as in NUM can be obtained in a rather simple compositional fashion
let us take xs and xt to represent the semantics of the source and the target clause i.e. the first and the second clause of a parallel construction the terminology is taken over from the ellipsis literature and xcs and xct to refer to the semantic values of the contrast pair
we illustrate the algorithm in figure NUM there we consider the constraint x q s c j a x c xcs a xc j which is also discussed in example NUM i as part of an elliptical construction
the conjunction of the constraints in NUM and NUM correctly allows for the two solutions NUM and NUM with corresponding scopings in xs and xt after closure NUM mixed solutions where the two quantifiers take different relative scope in the source and target clause are not permitted by our constraints
another way to represent referential ambiguities is to retry argument slots using additional variable names l and x below not to be mistaken as discourse referents
these flmctions at total only tbr the root and the leaves for inner nodes v they are restricted to the union d1 of the
the semantic grammar must be correlated with the syntactic grammar so that there is a oneto one mapping between lexical entries and rules
this is so because on the implementational level commutativity and associativity would necessitate an abstract data type thus a costly overhead
NUM i lcb epresentational underspeeification the ambiguil ies are represented explicitly or implicitly in a forrealism
mrs produces a spurious reading in which the pp with a telescope adjoins to the np a man while the pp in the apartment modifies the hill sentence
nevertheless we have been successful in creating standard backoff trigram models from very small corpora
the interviewer receives a graphic indication of whether the backtranslation was accepted or not
the possibly corrected backtranslation is then shown to the interviewee for confirmation
such scripts run from several hundred to around a thousand utterances for the languages we have examined
step NUM misalignect sections give rise to preliminary context free string replacement rules l r where l is a section in the asr output and r is the corresponding section of the true man null NUM
at the same time while the gain in recall and precision has not been negligible we recorded NUM NUM increases in precision no dramatic breakthrough has occurred either l
a typical full text information retrieval ir task is to select documents from a database in response to a user s query and rank these documents according to relevance
we acknowledge the following members of our trec NUM team who participated in the query expansion experiments louise guthrie jussi karlgren jim leistensnider troy straszheim and jon wilding
the results obtained from different streams are list of documents ranked in order of relevance the higher the rank of a retrieved document the more relevant it is presumed to be
queries obtained through the full text manual expansion proved to be overwhelmingly better than the original search queries providing as much as NUM precision gain
thus terms occurring predominantly in relevant documents will have their weights increased while those occurring mostly in non relevant documents will have their weights decreased
it becomes easier to fine tune the system in order to obtain optimum performance it allows us to use any combination of ir engines without having to modify their code at all
it has been reported that the use of different sources of evidence increases the performance of a system see for example callan et al NUM fox et a1 NUM
therefore the c box approach consists of the following sub processes NUM collecting training data NUM aligning text samples NUM generating correction rules NUM validating correction rules and NUM applying correction rules
top num number NUM title topic combating alien smuggling desc description what steps are being taken by governmental or even private entities world wide to stop the smuggling of aliens
while many problems remain to be resolved including the question of adequacy of term based representation of document content we attempted to demonstrate that the architecture described here is nonetheless viable
if any of the anaphor predicates succeeds the determined ante6edent sends an antecedentfound message directly to the initiator of searchantecedent this message carries the concept identifier of the antecedent
these deficits are no wonder since drt is not committed to any particular syntactic theory and thus can not place strict enough syntactic constraints on the admissible constituent structures
NUM the incorporation of an ordering constraint is even more justified if one looks at sentences which have a similar structure but are different with respect to word order cf
on any other occasion e.g. the head of the initiator is a preposition or a non finite verb the message is simply passed on to the receiver s head
however we want to prevent occurrences such as NUM
those phrases to which punctuation is attached serve an adjunctive function
conversely punctuation could only occur adjacent to maximal level phrases e.g.
the robotag server receives commands from the client and returns learning results to it
from analysis of our results we have noted that trying to choose one correct parse for every token is rather ambitious at least for turkish
a given word may be interpreted in more than one way but with the same inflectional features or with features not inconsistent with the syntactic context
we have applied our approach to the morphological disambiguation of turkish a free constituent order language with agglutinative morphology exhibiting productive inflectional and derivational processes
in this process our main goal is to remove any seriously improbable parses which may somehow survive all the previous choose and delete constraints applied so far
in this paper we present a constraint based morphological disambiguation approach that uses unsupervised learning component to discover some of the constraints it uses in conjunction with hand crafted rules
we first reprocess the training corpus but this time use a second set of projection templates and apply initial rules learned choose rules and heuristic delete rules
derivation the procedure outlined in the previous section has to be modified slightly in the case when the unambiguous token in the rc position is a morphologically derived form
we have noted that while learning choose rules the system zeroes in rather quickly on these contexts and comes up with rather successful rules for conjunctions
although it is easy to formulate what things can go together in a context it is rather impossible to formulate what things can not go together
the scoring function takes into account tag length and decision tree confidence values only
for comparison we used the standard f measure formula with a fl of NUM as reported above
we assume that inflected forms are mapped to base forms by an external morphological analyzer which might be integrated into an interface to germanet
this is only possible if all synonyms of both senses and all their dependent nodes in the hierarchy share the same regular polysemy which is hardly ever the case
according to levin in press NUM some can be used as verbs of motion accompanied by sound a train rumbled across the loopline bridge
conforming to the results of the trials messages retrieved from the database are ranked according to the criterion of semantic distance between key word and index word
adding this capability would allow performance improvement especially in cases where lexicon data is sparse
bikel reports that moving from NUM NUM to NUM NUM training texts yielded a NUM NUM improvement
ultimately a leaf node of the tree is reached which specifies the classification result
simultaneously a searchpronantecedent message in phase NUM takes the path to the sentence delimiter of the previous sentence where it evaluates pronanaphortest with respect to its acquaintances focus and potfoci no effect
null next we evaluate the appropriateness of the first estimation
n k narusawa NUM NUM NUM hitachi ibaraki NUM japan shinnou lily dse
in this experiment to measure the similarity in bunrui goi hyou and the similarity obtained by our method
the similarity in bunrui goi hyou are defined by the level of the common parent node of two classes
it follows that the obtained similarity is roughly similar to the similarity in bunrui goihyou
sornlerl lamwmi h el m s vehbai lassiti rs ally lassifier whi lcb h is derived from a verb NUM kraa l haa muan five roils of pal er
noun phrases of the form n no c where c is a group classifier but not a jos shi will also be translated as c of n where n will be plural if it is headed by a fully or strongly countable noun or a pluralia tanta
we divide such nouns into two groups strongly countable those that are more often used to refer to discrete entities such as cake and weakly countable those that are more often used to refer to unbounded referents such as beer
the coml inatio ls of nouns and classifiers mentioned above can all be translated by the machine translation systerit alt j e using the analysis of classifiers presented in this paper ill combination with a semantic hierarchy of NUM NUM categories common to all nouns as described in ikeharaetal
furl h j work remains NUM be done in xmnining the listribul ion of classifiers in differ nt domains and possibly identit ying classifiers a ul omal i a lly
part translate as x c of n where the classifier is translated by its translation equivalent from the transfer dictionary and n is uncountable headed by a bare singular noun NUM tsubu no kome l grain of rice NUM grain of rice
system vocabulary is derived from the text materials assembled for acoustic modeling as well as scenarios from the target domain for example interviews focussed on mine field mapping or intelligence screening
the first message contains the key word itself message NUM contains another word form of the same lemma
the score then follows by simple arithmetic
rm be equivalent classes generated by the response
oping the formalism especially to handle complex sentences
integrating free word order syntax and information structure
what s fatma going to do today
definitions for the features in figure NUM and other abbreviations can be found in the appendix
to obtain the best initial split of the training set the feature cn alias is chosen
knowledge incorporated into the framework is based on a set of measurable linguistic characteristics or features
other features are predetermined obtained via on line lists or are selected automatically based on statistical measures
an initial core set of linguistic features useful for name recognition in most languages is identified
this paper presents an approach to proper name recognition that uses machine learning and a language independent fi amework
for non token languages no spaces between words it also separates contiguous characters into constituent words
to acquire the knowledge required for classilication each word is tagged with all of its associated features
then trees for each proper name type are applied individually to the proper names in the featurized text
feature translation occurs through the utilization of on line resources dictionaries atlases bilingual speakers etc
in the case of textual ellipsis the missing conceptual link between two discourse elements occurring in adjacent utterances must be inferred in order to establish the local coherence of the discourse for an early statemerit of that idea cf
as plausible paths are the strongest type of conceptual paths only an element which is more highly ranked in the centering list and is linked via a plausible path to the elliptical expression could be preferred as the elliptic antecedent of ladezeit charge time over akku accumulator according to the constraint from table NUM
at the conceptual level textual ellipsis also called functional anaphora relates a quasi anaphoric expression to its extrasentential antecedent by conceptual attributes or roles associated with that antecedent see e.g. the relation between ladezeit charge time and al u accumulator in lc and lb
rs lcb x y i fx and y represent the same type of is pattern then the relation p c applies to x and y else ifx and y represent different forms of bound elements then the relation isbo nd applies to x and y else the relation rsb applies to x and y rcb
these relations do no tt block the triggering of the resolution procedure for textual ellipsis e.g. accumulator charge time ciiarge time whereas instantiations of their inverses we here refer to as pof type relations e.g. property of subsuming charge time of etc and physical part of subsuming accumulator of etc do e.g. accumulator accumulator of 316lt
moreover the crucial problem still unsolved in this logically very principled framework concerns a proper choice methodology lor fixing appropriate costs for specific assmnptions on which among other factors textual ellipsis resolution is primarily based
text phenomena e.g. textual forms of ellipsis and anaphora are a challenging issue for the design of parsers for text understanding systems since lacking recognition lacilities either result in referentially incoherent or invalid text knowledge representations
considering the performance of the criteria we propose disregarding effects that come from deficient knowledge engineering i.e. restricting the evaluation to the NUM cases run by the ellipsis handler the precision rate amounts to NUM NUM
implausible paths will be excluded from a path list iff plausible paths already exist while implausible paths will be excluded iff plausible or metonymic paths already exist
finally message NUM is an example of retrieval through semantic query expansion
NUM if for some reason russ did not want to know the information he might decide not to produce an askref
it can also occur on its own with anaphoric or deictic reference
one purpose of the main lexicon in wordkeys is to serve as a lexical database for the indexing module when performing morphological analysis
this implies that the system is able to retrieve messages containing any word of the english language apart from extremely domain specific vocabulary
this partitive construction is similar to the japanese quantifying construction xc no n
additionally at any stage of a conversation the user can add a message to the database or modify an existing message
NUM nan hiki how many animals int
the remaining candidates were sorted by their correlation coefficient in descending order
in fact the recent revival of interest in statistical language processing is partly because of its comparative success in modelling context
the suggestion that age exerts as much influence on paleolithic as vice versa seems ridiculous to say the least
the recogniser output typically consists of an ordered set of candidate words word choices for each word position in the input stream
we demonstrate here that traditional measures such as mutual information score are likely to overlook a significant fraction of all co occurrence phenomena in natural language
null the results shown here serve to strengthen our hypothesis that non standard information measures are needed for the proper utilisation of linguistic context
a dim score of NUM or more implies a significant association whereas an mis below NUM is considered a chance association
this work has been supported by a small business research program phase i grant from the department of health and human services public health service and a rehabilitation engineering research center grant from the national institute on disability and rehabilitation research of the u s department of education h 133e30010
this paper describes some of the problems with bringing compansion to a standard communication device and introduces some work being done in conjunction with the prentke romich company prc a well known communication device manufacturer on developing a pared down version of compansion for people with cognitive impairments
assuming the user is selecting full words at a time so time of word selection is basically constant and is independent of the number of letters iri the word the technique shows the most gain when used by a linguistically sophisticated user who desires well formed english constructions
the system is in its prototype stage and currently consists of a prc liberator a standard piece of hardware which provides access to vocabulary items through the communic easetmmap attached to a pentium based desktop pc running the user interface with windows nt and a software based textto speech synthesizer
in such a case no error message is issued
the interpreter of our grammar solves these situations itself
after having carried out all deletions we arrive at the following structure kds nepfedpokl id i spolupr ici a neni pravdou e benda prosadil dejmala
during the processing there were NUM items derived
this makes the task of the evaluating part of our system a bit more difficult but nevertheless the gain on effectivity not accompanied by the loss of recall justifies the use of this technique
partial results are misleading because it is often the case that the error is buried somewhere inside the partial tree and tlo operations performed on partial trees can provide a correct error message
if yes the new item is deleted
a nonprojective subtree is a subtree with discontinuous coverage
a prototype of a grammar checker for czech i
it is locally only a tiny bit simpler
intuitively we have a language which has an operational interpretation similar to prolog with the differences that we interpret it not on the herbrand universe but on n2 that we use ms0 constraint solving instead of unification and that we can use defined linguistic primitives directly
in implementing a program such as johnson s simplified parse relation see figure NUM we can in principle define any of the subgoals in the body either via precompiled automata so they are essentially treated as facts or else providing them with more standard definite clause definitions
first we present some of the mathematical background then we discuss na ive uses of the techniques followed by the presentation of a constraint logic programming based extension of mso logic to avoid some of the problems of the naive approach concluding with a discussion of its strengths and weaknesses
some obvious advantages include that we can still use our succinct and flexible constraint language but gain a a more expressive language since we now can include inductive definitions of relations and b a way of guiding the compilation process by the specification of appropriate programs
a derivation step consists of two parts goal reduction which substitutes the body of a goal for an appropriate head and constraint solving which means in our case that we have to check the satisfiability of the constraint associated with the clause in conjunction with the current constraint store
it is difficult to recognize how many collocations are in a corpus because the measure differs largely dependent on the domain or the application considered
take a key string strk from the strings stri i NUM n and retrieve sentences containing strk from the corpus
this method is applicable to various languages because it uses a plain textual corpus and requires only the general information appeared in the corpus
we use texts which are not explicitly structured
a nucleus is a set of clauses that presents a main claim of the text something that makes the text newsworthy while an adjunct supplements the main claim with some background or ancillary information
the asterisk means that no break even point was found for the associated experiment and the precision at the highest recall is listed instead the highest recall is given parenthetically
for instance a fixed length model using the first NUM word block requires only about one tenth of the words that are used in a full text model and still performs significantly and consistently better than the latter
jp summary the paper demonstrates how information on text structure can be used to improve the performance on the identification of topical words in texts which is based on a probabilistic model of text categorization
the idea of idf or the inverse document frequency is to give more points to words which have a localized distribution that is those that appear only in some of the documents and not others
considering the title NUM it is fair to say that the first sentence constitutes a main news of the article while the second is its elaboration providing supplementary details about the news
table NUM fixed lengthmodel flm a summary
notice that simple nouns are used as indices here
for all other values of form we would have something that is consistent with passive and thus the nonmonotonic rule will derive failure when applied
phase NUM disambiguation using gh bal rules try global rules one NUM y one
both of theln retain tagged syntactic structure for each sentence in thein
an experiment has proven that our hybrid method is both effective and applicable in practice
four global rules used in our disambiguatioi module are listed in table NUM
similarly we group nouns into place time state direction etc
conceptual relationships between v and n2 or between nl and n2 predict pp attaehnlent quite well in many eases
once all the semantic forms have been processed heuristic rules are applied to fill any empty slots from the text surrounding the forms that triggered a given ddo
table NUM container and measure classifiers
for example relative clauses in telegraphic input may be impossible for a human to interpret correctly at least unless a great deal of world knowledge information is applied
these programs are concerned with providing both an appropriate vocabulary and a set of icons appropriately placed on the keyboard so as to allow communication in an automatic fashion
characteristics of the language used by the particular population being studied has permitted us to apply some simple nlp techniques which are proving to be sufficiently robust for this task
some of the actions in the grammar are responsible for manipulating a particular register which encodes the generated string or expansion associated with each state in the network
we indicate that not only might the technique prove very useful for this population but by focusing on this population some of the problems with compansion can be eliminated
in addition to the words which are accessed via the icon sequences communic easetmcontains some morphology and allows the addition of endings to regular tense verbs and regular noun plurals
the system exploits information from multiple sources including letter n grams character confusion probabilities and word bigram probabilities
the retrieved candidates are ranked by the conditional probability of matches with the string given character confusion probabilities
the system can also learn character confusion probability tables by correcting ocr text and use such information to achieve better performance
recently statistical language models slms and feature based methods have been used for context sensitive spelling error correction
however some non word errors might become real word errors if the size of the word list or dictionary increases
these prepositional sfs were used to calculate a lower bound for the precision and recall rates of the system a sf is considered correct if and only if it is listed in the published dictionary NUM a lower bound for the recall rate of the system is given by the number of learned correct frames divided by the number of frames listed in the published dictionary or NUM NUM
for instance NUM is mapped with the vc nc and morphology rules to lcb pp an v denken pp an v gedenken rcb since g acht is the past participle of both the verbs nken to think and g enken to consider
an active verb second or verb final clause with one nc is m tpped to lcb pp p v v rcb if the nc precedes the finite verb auxi ary in the clause otherwise to lcb pp p v v pp p n n rcb
in each iteration of the algoritbrn the weight of a frame c is calculated by considering the totality of alternatives in which c occurs i.e. the sets for which z e x and ix NUM and its probability within each alternative
however it was observed that truly new prepositional frames frames not listed in broad coverage published dictionaries or even considered to be erroneous by a native speaker until confronted with examples from the corpus behaved with respect to their rank ngs very much like errors
for instance with this rule the clause in NUM is mapped to lcb pp auf a stolz pp auf n student rcb NUM stolz ist der student darauf da6
however considering the fact that the utterances requiring context for translation is relatively small it is practically acceptable for dialogue machine translation
when the speech act of the utterance yey is response it must be translated as yes or no
let a context free grammar g be a quadruple n t r s where n and t are finite disjoint sets of nonterminal symbols and terminal symbols respectively r is a set of rules of the form a a a is a nonterminal and a a possibly empty string of nonterminal or terminal symbols s is a specim nontermin l called start symbol
NUM roblenl and deal wilh undefined wor ls in the dictiolm ry
in table NUM we show sample h cal rules for preposition with
in particular we are deciding membership in the theory of a fixed structure af2 and not consequence of an explicit set of tree axioms
the solutions to constraints expressed in weak monadic second order mso logic are represented by tree automata recognizing the assignments which make the formulas true
despite the staggering complexity bound the success of and the continuing work on these techniques in computer science promises a useable tool to test formalization of grammars
so for example the parse tree shows up in the formalization as a second order variable rather than simply being a satisfying model cf
it provides a component and module design which has been jointly developed by a significant number of providers of advanced software of this type
extraction encompasses the technology which identifies specific entities and the relationships between entities in free text so they can be use to build a database
knowing the normally long time it takes research advances to make their way into standard technology the founders of the program rightly believed a concerted effort to place good technology in the hands of users would be necessary to insure that they received the benefits of the program as soon as possible
although the goal of strengthening the science through the application of the technology was not explicitly stated as a reason for the emphasis on technology transfer in the tipster program nonetheless i think the emphasis on tasks and on the usefulness of the technology is benefiting the underlying science of computational linguistics
analytical tools such as link analysis tools timelines or other displays showing document clustering are considered part of the user interface or the application
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 that meet these requirements
similarly the research community can take advantage of the architecture to facilitate the testing of new ideas in advanced text handling
in addition user interface gui requirements are not covered by the architecture but are unique to the specific application
annotation allows these two components to share information at a component level
use of the architecture for government procurements will also shorten the development process for new text handling applications because a basis for design would already exist and be understood by vendor and customer alike
section NUM gives an overview of the verbmobil project
the functor argumcnt application macro thus says the following
this would be contrary to the linguistic observation
these constraints are syntactically defined as if i l are labels h is a hole then it h NUM NUM and l NUM are lud constraints
lud fun arg result fun hrg
there are three types of constraints in ludrepresentations
the domain is the scheduling of business appointments
the generator then constructs the corresponding english expression
value flag exsst c el na on na e flag l ccount awe ght pcount rop name mve ght rcount rel name conc name awesght
we present an approachto text summanzatlon that m entirely rooted m the formal descnptlon of a classtficatmn based model of termmologlcal knowledge representahon and reasoning text summarization m conmdered an operator based transformation process by which knowledge representation structures as generated by the text understander are mapped to conceptually condensed representahon structures forming a text summary at the representation level
for example the skull icon indicates a body part word the masks icon indicates a feeling word and the apple icon indicates a food word
the element corresponding to the bottom of the order relation is denoted fail and represents inconsistent information or unification failure
in this section i provide give the formal definitions for nonmonotonic sorts and how nonmonotonic sorts are unified and explained
this restriction ensures that the application of one default rule will never cause previously applied default rules to be inapplicable
a common feature of recent unification based grammar formalisms is that they give the user the ability to define his own structures
i will use the terminology w application for applying one nonmonotonic rule to the sort and w ezplanation when applying all possible rules
one further example will illustrate that it is also possible to define negation as failure with nonmonotonic rules
considering unification of nonmonotonic sorts it is not necessary to simplify the nonmonotonic part of the resulting sort
concept transitiveverb isa verb requircs subj any value obj any value
class skickades isa verb requires lex skicka form passive
finally i will show how a fragment of a lexicon can be defined according to these rules
nymble performs well turning in f measures of NUM and NUM respectively in spanish and english on the muc NUM task
the mateher can be biased to prefer tags longer shorter or closest to this mean length
we do not currently learn the tag priorities although this is a logical extension to the learning technique
however immediately after applying a rule schema the features at the bottom of a domination link are compared with the foot nodes of auxiliary trees that have differing sfs at foot and root
in case f is list valued we assume that the rest of the elements in the list those that did not select any daughter are also contained in the f at the mother node
this is too strong a condition and will not allow the resulting tag to generate all the trees derivable with the given hpsg e.g. it would not allow unsaturated vp complements
this leads to the following multi phase compilation algorithm
null as far as we can see the only limitation arising from the percolation of slash only along head projections is on extraction out of adjuncts which may be desirable for some languages like english
as such category labels s np etc determine where an auxiliary tree can be adjoined we can informally think of these labels as providing selection information corresponding to the sfs of hpsg
factoring of recursion can then be viewed as saying that auxiliary trees define a path called the spine from the root to the foot where the nodes at extremities have the same selection information
because of the subj and slash values the head dtr is the foot of t2 below anchored by an adverb and comp dtr is the foot of t3 anchored by a raising verb
a phrasal schema count as functors and arguments
such trees are said to factor out recursion
we have shown robotag performance to be competitive with hand coded pattern based systems in very different languages like english and japanese
from an ai perspective these reconstructions resemble the operation of a truth maintenance system upon an abductive assumption that has proved to be incorrect
however predominant computational approaches to dialogue which are based solely on inference of intention already have difficulty constraining the interpretation process
in this section we will give both the knowledge structures that enable the participant s behavior and the reasoning algorithms that produce it
a more detailed description of the language independent system components their
this would be faster and simpler than lexicon based segmentation
for many foreign languages software tools are not readily available
NUM NUM NUM assessment of the merit of the technical approach and lessons learned
the syntactic constituents are allowed to combine to form a larger constituent only if their pragmatic counterparts the ordering categories can also combine
here i concentrate on further devel i would like to thank mark steedman ellen prince and the support of nsf grant sbr NUM
these simpler ordering categories also contain a feature which indicates whether they represent given or new information in the discourse model which is dynamically checked during the derivation
backward application y xl args u lcb rcb x args
each verb is assigned a function category in the lexicon which subcategorizes for a multiset of arguments without linear order restrictions
by indicating what is the topic and the focus of the sentence as will be defined in the next section
thus we must have more than one is available for verbs where verbs can be in the focus or the ground component of the is
clausal arguments just like simple np arguments can occur anywhere in the matrix sentence as long as they are case marked NUM a and b
the context appropriate use of free word order is of considerable importance in developing practical applications in natural language generation machine translation and machine assisted translation
multiset ccg can handle free word order among arguments and adjuncts in all clauses as well as word order variation across clause boundaries i.e. long distance scrambling
therefore we may choose a statistic to measure the correlation coefficient of neighboring characters then use this statistic to judge the probability that they belong to the same word
if two tags are far enough apart they can be matched independently without fear of one pairing precluding another
our concern is not to pinpoint this ideal nor to answer precisely why it is sought by speakers and writers but to gauge to what extent it is sought
we chose to divide at the sentence level and to partition the NUM NUM million sentences in the data based on a likelihood measure suitable for testing the hypothesis from section NUM
we seek to investigate for example whether the avoidance of primary stress clash the placement of two or more strongly stressed syllables in succession influences syntactic choice
the efficiency of the n gram training procedure allowed us to exploit a wealth of data over NUM million syllables from NUM million words of wall street journal text
lis ten to me close ly i ll en deav or to ex plain schwartz the tiniest of realistic training sets will cover the binary or ternary vocabulary
randomizing the word order in this way yields a fairly crude baseline as it produces asyntactic sequences in which for example single syllable function words can unnaturally clash
we regard these experiments as computing the entropy rate of a markov chain estimated from training data that approximately models the emission of symbols from a random source
to supplement the z binary vocabulary tests we ran the same experiments with NUM lcb NUM NUM p rcb introducing a pause symbol to examine how stress behaves near phrase boundaries
the model assumes word actors to communicate via asynchronous message passing
the lack of availability of basic development tools such as multi lingual editors and fonts can have a serious impact on development schedule
common system development will provide benefits to the government in the long term however it requires substantial initial investment and customer buy in
the design and development of the demonstration system was a valuable learning experience which will positively impact the success of future technology efforts
in particular word groups that are important to the domain and that may be detectable with only local syntactic analysis can be treated here
system software to support languages other than english is still minimal especially for languages not representable as ascii characters such as chinese
one way to reduce the risk of technology transfer is selecting a well defined problem and scope it appropriately in the development and protototype stage
in the initial stages of development it is tempting to select a problem that best matches the known technical capability of the systems
text retrieval evaluation for chinese will not be baselined until trec NUM in NUM and chinese extractions results were not baselined until spring NUM
prior to this effort the plum information extraction system had been applied to several domains in english and to two domains in japanese
better software tools and procedures to support quality control are also needed given the inherent difficulties in manually tagging large amounts of data
among these NUM queries we noted precision gains in NUM precision loss in NUM queries with NUM more basically unchanged
we would like to thank donna harman of nist for making her prise system available to us since the beginning of trec
the long queries on the other hand contain substantially more text as the result of full text expansion described in section NUM below
in today s information retrieval systems query expansion usually pertains content and typically is limited to adding deleting or re weighting of terms
this allows for an easy combination of alternative retrieval and routing methods creating a meta search strategy which maximizes the contribution of each stream
our goal here is to have the documents on the same topic placed close together while those on different topics placed sufficiently apart
proper names of people places events organizations etc are often critical in deciding relevance of a document
in our approach queries are expanded by pasting in entire sentences paragraphs and other sequences directly from any text document
the purpose of query expansion in information retrieval is to make the user query resemble more closely the documents it is expected to retrieve
our experiments show that selecting the right paragraphs from documents to expand the queries can dramatically improve the performance of a text retrieval system
n evaluation of our similarities we give a c if the largest similarity is right a a if 1st or 2ud largest similarities is right answer and x if neither of 1st and 2nd largest similarities is the right answer
our approach avoids the rather fuzzy concept of indirect antonyms introduced by wordnet
during the application of these rules if the immediate right context of a token is a derived form then the stem of the right context is also checked against the constraint imposed by the rule
hence locating a verb in the middle of a sentence is rather difficult as certain verbal forms also have an adjectival reading and punctuation is not very helpful as commas have many other uses
during the set up of the incontext table such a context is entered twice once with the top level feature constraints of the immediate unambiguous right context and once with the feature constraints of the stem
refers to the corresponding concept class c
x being the head of modifier y
they are organized at three layers
i the charge time of NUM NUM hour
in the area of machine translation one important interface is that between the speech recognizer and the parser
thus we obtained a total of NUM segment boundaries which means that on average approximately after every seventh token i.e.
their best results were achieved by using part of speech n grams enhanced by a couple of trigger words and biases
NUM concatenation of boolean pos and trigger encodings c NUM NUM NUM
the optimal threshold for a high f score lies in the NUM NUM NUM NUM NUM interval
window size NUM or NUM threshold between NUM NUM and NUM NUM number of hidden units between NUM and NUM
we trained on NUM words i.e. over NUM times less training data and we achieved an f score of NUM
learning to tag multilingual texts through observation
related work and future directions are presented
in addition we thank john gray for his discussions and implementation of many of the c aspects of the system and marjeta cedilnik for her work on the grammar and transformation rules
thus these actions are responsible for adding determiners etc sets of registers axe also maintained for recording semantic aspects of the partial sentence e.g. information such as what word is the agent
notice that assuming a root word can be selected in a single keystroke and endings added with additional keystrokes the initial input would take NUM keystrokes while the expanded version would have required NUM
all must agree on the minimum accepted system performance to determine its success
for locations this is generally just the first character of the location name
the demonstration project was very ambitious in its support of this goal
in such cases a development environment for nonprogrammers is highly desirable
chinese segmentation seems inherently harder than japanese based on our experience
in many written languages word boundaries are clearly marked by spaces
an automatic catalog is constructed from a collection based on word co occurrence
this may include the special problem of chinese and foreign name recognition
character encoding a given language may have multiple non ascii character encodings
since no grammar is included no semantic interpretation rules were written
it would be straightforward to add such an alias capability to robotag
for NUM strings whose entropy is greater than NUM NUM strings NUM NUM are complete sentences NUM strings NUM NUM are regarded as grammatically appropriate units and NUM strings NUM NUM are regarded as meaningful units even though they are not grammatical
we use the princeton wordnet technology for the database format database compilation as well as the princeton wordnet interface applying extensions only where necessary
konventionell is related to konvention regeln des urngangs social rule while konventzonal is related to konvention ouristiseher text agreement
in the current version of the database multi word expressions are only covered occasionaly for proper names olympische spiele and terminological expressions weifles blutk6rperchen
therefore we can reconstruct via the regular polysemy pointer that the meat sense is referred to in this particular sentence even though it is not explicitly encoded
so for example the sentence i had crocodile for lunch is very infrequent in that crocodile is no t commonly perceived as meat but only as animal
it is compatible with the princeton wordnet but integrates principle based modifications on the constructional and organizational level as well as on the level of lexical and conceptual relations
innovative features of germanet are a new treatment of regular polysemy and of particle verbs as well as a principle based encoding of cross classification and artificial concepts
the development of such a large scale resource is particularly important as german up to now lacks basic online tools for the semantic exploration of very large corpora
the results of these classifiers are then combined using a tag matching algorithm to yield complete tags of each type
nevertheless a skilled developer with a thorough knowledge of the particular pattern language is still essential
table NUM gives the figures for the message ranking criteria applied in the case of one single key word
getmanet however currently assumes only one basic meronymy relation
conse null quently the semantic material bearing new information is considered to be in focus
cset x s the subset of properties of s to be accented for contrastive purposes
the following sections elucidate how such a formalism can be integrated into the computational task of generating spoken language
the present framework for organizing the content of a monologue is a hybrid of the template and rst approaches
while the types of pitch accents h or l h are determined by the theme theme delineation and the aforementioned mapping onto tunes the locations of pitch accents are determined by the assignment of foci within the theme and rheme as illustrated in NUM and NUM
the result is a string of words and the appropriate prosodic annotations as shown in NUM
for example analysts usually label relations with either nouns or verbs giving rise to paraphrases such as a committee determines issues verb or a committee has an issue as its topic noun
having a second view in an easily accessible code allows him or her to more easily detect semantic errors
this means that the system is fully portable between modeling domains and is not overly costly to use
mccullough and m white and two anonymous reviewers for their comments and criticism of modex and the present paper
for example it would be illegal for the section NUM not to belong to any courses
the analyst fixes this and reruns modex on this relation obtaining the description shown in figure NUM bottom
let d be the sorted candidate table ds be a sub table of d starting from the beginning of d two evaluation standards precision and recall were defined as follows becomes higher mi is better than others
in fact terminology phrase belongs to one certain kind of collocation m fixed collocation whether two or more words can compose a collocation is measured by the correlation coefficient of these words NUM
among all the corpus l ased researches some of them are quite similar to the work reported here including sublanguage vocabulary identification NUM automatic suggestion of significant terminology NUM identification and translation of technical terminology NUM automatic extraction of terminology NUM
second in most indo european languages even a word could n t be found in the dictionary it still could be separated by the spaces between it and neighboring words however chinese is written in character sequences with no delimiters between successive words
to collect new words each article was scanned and all the bi gram tri gram and NUM gram candidates with frequency greater than threshold t were extracted for cw corpus tr NUM for xn corpus t NUM
most of these pseudo phrases can be divided into two classes terminology phrases were extracted from the top NUM with precision of about NUM terminology phrase candidates these candidates were examined manually and NUM NUM were accepted
let pc w be the frequency of word w in domain corpus p w be the normal frequency of w if pc w p w w is extracted and further examined by professionals otherwise it is discarded
compare each bi gram NUM b candidate to every two neighboring characters c c NUM in the text sequence c cic c c z cn where n is the size of the text and record the comparison results
i will instead make use of the two nonmonotonic definitions below
as stated previously nonmonotonic sorts allow multiple explanations of a nonmonotonic sort
as if the application of the nonmonotonic rule should not be allowed
otherwise the default rule would have no effect and can be removed
the rule is used for stating that verbs are active by default
another option would be to interpret fail
i will start with defining default values
two examples will further illustrate this
the messages which have been found are displayed on the screen the order corresponds to their score
the placental mammal is a type of mammal that carries its developing young inside the mothers womb
if z and y are categories then x y respectively z y is the category of an incomplete x that is missing a y at its right respectively left
by contrast the method proposed below is purely syntactic just like any ordinary parser so it never needs to unpack a subforest and can run in polynomial time
the notation here is from NUM
adding a parse can therefore take exponential time
but what does semantically equivalent mean
the proof of theorem NUM see NUM actually shows how to construct nf a in o NUM time from the values of nf on smaller constituents
so far however the results of the simple method we have outlined seem promising
for semantic query expansion through semantically related words a comprehensive electronic dictionary containing extensive semantic information is needed
in order to form the noun phrase det aclj n two computation sequences will occur in a primordial soup parser attaching det to the n first then adding adj or vice versa
among the drawbacks of parallel processing one recognizes the danger of greedy resource demands and communication overhead for processors running in parallel as well as the immense complexity of control flow making it hard for humans to properly design and debug parallel programs
hence the major drawback of unrestricted parallel algorithms is their non confluency and subsequently either the large exponential in the worst case number of spuriously ambiguous analyses or the global operation of subsequent duplicate elimination
in such an entirely unconstrained parallel model a word actor is instantiated from the input string and sends search messages to all other word actors in order to establish a dependency relation eventually generating a complete parse of the input
the missing value for sentence NUM results from the chart parser crashing on this input because of space restrictions of the run time system the experiments were conducted on a sparcstation NUM with NUM mb of main memory
but such a restriction is detrimental to requirements set up in a realistic text parsing environment in which the analysis of possibly large fractions of un as well as extragrammatical input must be skipped
as a consequence we do not only supply an efficient parsing procedure but also one that is effective in the sense that it guarantees the generation of conceptual representations of the content of the text under feasible resource demands
container actors play a central role in controlling the parsing process because they encapsulate information about the preceding containers that hold the left context and the chronologically previous containers i.e. a part of the parse history
overall for complete real and non word error correction it achieved a NUM NUM rate of error reduction
in the following discussion we compare the three experiments using the results obtained from the second feedback step feedback NUM
all the partitive nouns themselves are fully countable
the system takes advantage of information from multiple sources including letter ngrams character confusion probabilities and word bigram probabilities to effect context based word error correction
our m alysis of classifiers in table d
a real word error occurs when a source text word is interpreted as a string that actually does occur in the dictionary but is not identical with the source text word
the interpretation of c is however ambiguous
while non word errors might be corrected without considering the context in which the error occurs a real word error can only be corrected by taking context into account
the disambiguation component produced a decision for NUM of these NUM sets
classical chinese has additional graphic character styles
this automaton could in turn be implemented itself as prolog code and considered to be an optimized implementation of the given specification
it outputs a minimized automaton and the minimal automaton is a unique up to isomorphism definition of the given relation
our initial constraint base which can be automatically generated from a prolog list of input words is the corresponding tree automaton
unfortunately as we have already noted the problem of generating a tree automaton from an arbitrary mso formula is of non elementary complexity
table NUM manual examination results of the universal dictionary
table NUM presents some example terminologies with high rank
in large scale natural langnage processing applications where context
following is the detailed description of this method
among them chi square test needs special attention
minologies are available in the universal dictionary
these words are also extracted in statistical method
thus they should be removed from candidate tables
table NUM shows the distribution of new words
for english the training text consisted of NUM messages obtained from the english joint ventures ejv domain muc NUM corpus of the us advanced research projects agency arpa
for several key words a combination of the semantic distances for different key words is used for ranking
the second computation step is similar
the process of determining foci within themes and rhemes can be divided into two tasks determining which discourse entities or propositions are in focus and determining how their linguistic realizations should be marked to convey that focus
in this paper we argue that commonly employed models of text organization such as schemata and rhetorical structure theory rst do not adequately address many of the issues involved in generating spoken language
that is suppose the generation program has finished generating the output corresponding to the examples in NUM through NUM and is assigned the new goal of describing entity e2 a different amplifier
NUM holds defn isa el amplifier holds design el solid state pres holds cost el e9 pres holds produce el e7 pres holds contrast praise e4 el revile es el past
conversely if imposing the restriction on the rset for a given property does not change the rset the property is not necessary for distinguishing x from its alternatives and is not added to the cset
for example although two discourse entities el and e2 can be determined to stand in contrast to one another by appealing only to the discourse model and the salient pool of knowledge the method of contrastively distinguishing between them by the placement of pitch accents can not be resolved until the choice of referring expressions has been made
NUM goal describe el input generat e int ention bel hl good t o buy e i information from the knowledge base is selected to be included in the output by a set of relations that determines the degree to which knowledge base facts and rules support the communicative intention of the speaker
second since accentual decisions are made with respect to the particular linguistic realizations of discourse properties and entities e.g. the choice of referring expressions these matters can not be fully resolved until the sentence planning phase
similarly in NUM the pitch accent on red may mark the referenced car as standing in contrast to some other car inferable from the discourse context NUM NUM it was john who spoke first
note that this is true even for variables appearing in the global table
it is obviously desirable to keep the automata as small as possible
we now have another well defined way of using the offiine compiled modules
there may be ways of building or precompiling a common comparator set automatically using the knowledge base and information from a user model but for the moment we assume that it has been preconstructed
null we could perform these comparisons using a similar approach to that which we adopted for clarificatory comparisons for each entity attribute pair we could specify some entity that can be used as a comparator
a clarificatory comparison is generated whenever the entity to be described is known to have a potential confusor our implementation of this strategy is currently very simple and is described in section NUM NUM
the procedure used here for finding the best match is one that in our current experiments looks acceptable although it is likely to be applicable only for a relatively narrow range of attributes
in the context of a language generation system like peba ii direct comparisons arise when the user enters a request such as what is the difference between the echidna and the african porcupine
why are some entities better comparators than others
what properties of these entities are used in comparisons
the african porcupine is a type of flacental mammal
the tipster software architecture was developed to permit such sharing of software developed at different agencies
but more broadly applications can be important to much research that deals with human language
in step five the use of a standard form of project management cycle becomes appropriate
further evaluation after installation can determine whether the system has in fact produced the expected improvements
the tipster program is keeping abreast of developments in standards such as z39 NUM and the document
commercial uses of the technology will not necessarily include all the functionality that government analysts require
it is important also to educate the user about the technology
the program therefore had to initiate this development itself
the wordkeys system offers the possibility of importing any text file to add it to the message database
in other words context unification can be considered as the problem of solving equality up to constraints over finite trees
c p x co x lam cl x
in this section we discuss the use of context unification for treating underspecification and parallelism by some concrete examples
the general case of equality up to constraints can not be handled by a system using subtree plus equality constraints only
however most algorithms suggested in the literature are designed for collections of larger documents containing several hundreds of words
in order to use the information in wordnet for our text retrieval algorithm some preparation was needed
we do this by first computing phonetic statistics for the language using available text materials then designing a recording script that exhaustively samples all diphones observed in the available text sample
we have adopted for the speech component a combination of approaches which although they rely on participation by native informants also make extensive use of pre existing acoustic and text resources
an initial system has been developed to run on a pair of laptop computers with each speaker using a graphical user interface gui on the laptop s screen see figure NUM
in contrast tipster phase two focused on creating an architecture to integrate the two technologies and on deploying these technologies at multiple government agencies
the best candidate for a platform for a user interface was the new mexico state university compuing research laboratory xat library of widgets based on the motif library for the x window system
the developer and system implementor must understand and agree on the risks involved in development especially in the situation when advanced technology is being applied to a completely new domain or language
NUM in phase NUM the message is forwarded from the head which d binds the initiator the original sender to the word actor which represents the sentence delimiter of the current sentence
for nominal anaphors the search for the antecedent is triggered by the attachment of a definite article as a modifier to its head noun so that a searchnomantecedent message will be issued
the arrival of a message at an actor is called an event it triggers the execution of a method that is composed of atomic actions among them the evaluation of grammatical predicates
summing up drt is fairly restricted both with respect to the incorporation of powerful syntactic constraints at the sentence level and its extension to the level of non anaphoric text macro structures
in NUM the pronoun belongs to the subordinate clause but in NUM the antecedent of the pronoun belongs to the subordinate clause and the example seems to be acceptable
der rechner hat eine taktfrequenz yon NUM mhz
that is the theme of the answer is what links it to the question and defines what the utterance is about
the photocopies were then scanned by a fujitsu 3097e scanner and the resulting images were processed by xerox textbridge ocr software
based on the results we can see that the predominant positive effect in correction occurs in the first pass
in this paper we describe a system that uses a word bigram slm technique to correct ocr errors
the minimum edit distance is the minimum number of operations that transform the source string to the target string
if we assume that strings produced under ocr are independent of one another we have the following formula
we then describe our incorporation of interactive error correction throughout the system design
this is being developed in the context of our haitian creole and korean systems
several of these systems have improved in ease of use particularly in the speed of the write pattern evaluate performance refine pattern loop which plays the central role in the development process
a clarificatory comparison serves to describe the focused entity thus it corresponds to the user entering a request such as what is the echidna
our position is that such methods can be a virtue rather than a vice since they allow broad coverage systems to be built more quickly
for the present discussion we will concentrate on the attributes of size and weight and the mechanisms used to produce illustrative comparisons that indicate these attributes of the entity being described
null similarity and difference are not completely distinct the similarity of two values for a particular attribute should be viewed as a scale of similarity rather than as a binary distinction
in such a case instead of describing the echidna in isolation the system may choose to describe it using a clarificatory comparison with the porcupine
the echidna is a carruvore and eats ants termites and earthworms whereas the african porcupine is a herbivore and eats leaves roots and fruit
a better method is to identify groups of words that create meaningful phrases especially if these phrases denote important concepts in the database domain
in ta the verb erinnern to remind subcategorizes for an accusative np and a pp headed by the preposition an on while in to the verb nehmen to take is a support verb and racksicht consideration a noun which subcategorizes for a pp headed by the preposition auf
the interviewee s response is recognized translated to en2we realize that backtranslation is also an error prone process but it at least provides some evidence as to whether the translation was correct to someone who does not speak the target language at all
for example part of speech information has been used to reduce the overall perplexity of the language model
the endotracheal tube is in satisfactory position there has been re expansion of the right upper lobe
for one thing we have thus far avoided the question of speaker dependence
more advanced linguistic methods include re ranking n best sentence hypotheses using syntactic and lexieal well formedness
the remaining occurrences of l constitute the refute set l r
in other words we refine rules by fitting them more closely to the existing evidence
using multiple srs in parallel may also increase the likelihood of locating and correcting spurious transcription errors
further research and evaluations are required with context sensitive correction rules and various sizes of the training data
original asr transcription errors highlighted indication colon and trachea to place
for exami le is an amtotated corpus coml risiltg about NUM NUM words of written american english text
when the test succeeds the message is forwarded to these modifiers where the anaphor predicates pronanaphortest or nomanaphortest are evaluated in parallel
only if the message reaches this head are two further messages with phases la and NUM sent simultaneously and the message in phase i terminates
interestingly enough when faced with some crucial linguistic phenomena such as topicalization gb must assume rather complex movement operations in order to cope with the data in a satisfactory manner
though maria is the subject of NUM only peter can be considered the antecedent of the reflexive since it is d bound by the head which d binds peter viz
since the structural criteria for the sentence position of both types of anaphors are the same the distribution mechanisms underlying the corresponding messages can be described by their common superclass searehantecedent
the relation permit c NUM v x r x NUM r characterizes the range of possible conceptual relations among concepts e.g. motn erboard has cpu cpu e permit
the second relation r bo u n d denotes preference relations dealing exclusively with multiple occurrences of bound elements in the preceding utterance
altogether the ellipsis handler was triggered NUM times thus it was incorrectly triggered in NUM cases NUM NUM
as a consequence a textellipsis antecedentfound message is sent from akku to the initiator of the searchantecedent message viz
l he computer is because of this new type of accumulator for NUM hours with power provided
hence the same ordering of path markers as in table NUM can be applied to compare two cp lists of
this shortcoming is simply due to the fact that drt is basically a semantic theory not a full tledged model for text understanding
lh the omputer i bacaube of thib new typ of ac mulator for NUM hours with powar provided
chargetime of accumulator of indicating a whole for part metonymy while the concept accumulator is related to charge time via a plausible path viz
actually the sort of constraints we considered seem much more rooted in encyclopedic knowledge than are they of a primarily semantic nature anyway
these are almost always foreign proper names words adapted into the language and not in the lexicon or very obscure technical words
final adjectivalization by the relative ki suffix here the original root is verbal but the final part of speech is adjectival
we have also incorporated a rather sophisticated unknown form processor which extracts any relevant inflectional or derivational markers even if the root word is unknown
their table choosing among the last three is rather problematic if the corresponding genitive form to force agreement with is outside the context
NUM finally the delete rules that have been learned are applied repeatedly to unambiguous contexts until no more ambiguity reduction is possible
4upper cases in morphological output indicates one of the non ascii special turkish characters e.g. g denotes u denotes i etc
these rules examples of which are given above are independent of the corpus that is to be tagged and are linguistically motivated
NUM for each unambiguous context encountered c lc rc 1deg around an ambiguous token w with parses p1
we have extended the rule learning and application schemes so that the impact of various morphological phenomena and features are selectively taken into account
in table NUM the tokens considered are that are generated after morphological analysis unknown word processing and any lexical coalescing is done
if we extend bunrui goi hyou these unused cooccurrence data may be useful
in his work repair addresses the problem of incompleteness in a taxonomy of plans rather than errors in interpretation
on the contrary thanks to the similarity in their architectures the same lexicon can be used on mac and on unix machines
whether we use a discrete or continuous random variable is as we shall see of no importance
k NUM c fl where f means no information corresponding to the nnigram probabilities
we will first consider estimating the n gram probabilities p t tk n i tk NUM
since this is the most general context this will be the context with the most training data
since each test corpus consisted of about NUM NUM words and the error rates are between NUM and NUM the NUM percent significance level for differences in error rate is between NUM NUM and NUM NUM depending on the error rate and the NUM percent significance level is between NUM NUM and NUM NUM
null i wish to thank greatly thorsten brants slava katz khalil sima an the audiences of seminars at the university of pennsylvania and the university of sussex in particular mark liberman and the anonymous reviewers of coling and acl for pointing out inaccuracies and supplying useful comments and suggestions to improvements
the square root of the variance which is the standard deviation should thus be a suitable quantity
a standard statistical trigram tagger has been implemented that uses linear successive abstraction for smoothing the trigram and bigram probabilities as described in section NUM NUM and that handles unknown words using a reversed suffix tree as described in section NUM NUM again using linear successive abstraction to improve the probability estimates
understanding how far along one s technology is on the path toward deployment and carefully evaluating the technology at each stage in its progress can prevent many mistakes
this is easy enough to understand if one stops to think about the kinds of applications that most potential users of detection for example have
however it can not be stated strongly enough that appropriate preparation via steps NUM NUM must have occurred in order for rapid insertion to take place
both document detection and information extraction imply an eventual user someone who needs documents or someone who needs particular information about particular kinds of entities or events
those desiring information about this type of software contact the committee or other participants and can be directed to contractors who have the kinds of software which is required
in any project involving the joint investment of resources a clear agreement worked out ahead of time concerning the obligations and resources coming from each party is important
the technology is being brought to serve the user s task and not solely to bring credit to the technology s developer
both of these failings occur because needed steps in the progression of technology from research to the user environment have been short changed
some feel best with developers sitting in their midst some like their developers working in hi tech labs at the developers spaces
it is important for the technologist to communicate that he she is promoting the user s ends not the technologist s ends
these temphttes built by hand use logical ol eralors and or etc to combine features strongly asst ciated with proper names including proper mun ampersand hyphen and comma
let d denote a dialogue which consists of a sequence of n utterances u1 u2 un and let si denote the speech act of ui
our experimental results with trigram showed that the proposed model achieved NUM NUM accuracy for the top candidate and NUM NUM for the top four candidates even though the size of the training corpus is relatively small
ifiers to the subject as adjuncts
technically however it is straightforward to move from text categorization to topic identification provided that we are able to somehow isolate themes in texts and use them as categories to be assigned to texts
model ii is the model based on hierarchical recency
a unit classifier will be realized in japanese a a jos lcb shi
NUM er eived phenom ha in similar ways
thr ughout the pallor we us th
consider the japanese classifier mai sheet which is used for counting fiat objects
n is always parted NUM kireno inu l slice of dog NUM slice of dog
if n is uncountable then the classifier is translated as the default default
in general proper names and abbreviations are not integrated even though the lexicographer may do so for important and frequent cases
selectional restrictions for particles include aktionsart a particular semantic verb field deictic orientation and directional orientation of the base verb
emptiness of the language t NUM is decidable by a fixpoint construction computing the set of reachable states
2the current version of the mona tool works only on the mso logic of strings
null sets of trees which are the language of some tree automaton are called recognizable
in our own prototype we minimize the outputs of all of our automata constructions
t a is empty if and only if no final state is reach1
the general form of such a locality condition lc might then be formalized as follows
then we conjoin a description of the input with the grammar automaton as given be
the model uses syntactic patterns and n grams reflecting the hierarchical discourse structures of dialogues
the dim s on the other hand have balanced fractions
NUM NUM NUM ii NUM NUM NUM NUM NUM
we also propose metrics for measuring directed lexical influence and compare performances
the language module must update these confidence values to reflect contextual knowledge
NUM NUM NUM NUM ii NUM NUM NUM
lexicosemantic associations are exemplified by phrasal verbs eg fix up and are characterised by morphological complexity in the verb part and spatial flexibility in the phrase as a whole
quantifying lexical influence giving direction to context
our formulation of directed influence is still evolving
note that very few of these pairs exhibit comparable influence on each other
the arrows indicate the direction of lexical influence or information flow
we can intuitively suppose that the two characters are more correlate to each other when they belong to the same word
the number of n grams with n NUM is very small and the occurrence of most of them is rare
similarly a NUM gram can be looked either as the combination ofa tri gram and a character or two bi grams
those bi gram candidates with correlation coefficient smaller than a pre defined threshold are considered to occur randomly and should be discarded
since terminologies are highly relevant to the text s domain they are proved to be much valuable index words
therefore the generation of terminology dictionary would inevitably require a great deal of tedious and time consuming manual work
the rest of the paper is structured as follows
alternatively preferableto may call an intonation or discourse module to pick the parse that better reflects the topic focus division of the sentence
it also treats conjunction lexically by giving and the generalized category x x x and barring it from composition
the relevant rule s i p np s will actually be blocked when it attempts to construct 5f
the function pre ferableto r step NUM provides flexibility about which parse represents its class
ignoring the tags ot fc and be for the moment 8a is a normal form parse
thus verb phrases are analyzed as subjectless sentences s np while john likes is an objectless sentence or s np
theorem NUM is proved by showing that the terms for a and a differ somewhere so correspond to different semantic recipes
NUM thenp n bign n that likes john n n n n
the other more complex algorithm solves the spurious ambiguity problem for any ccg grammar by using normal forms as an efficient tool for grouping semantically equivalent parses
more colloquially NUM says that the output of rightward leftward composition may not compose or apply over anything to its right left
representations in lud have the following distinct features
finally embedded properties those evoked for building referring expressions for discourse entities are realized by adjectival modifiers if possible and otherwise by relative clauses
the effects of the focus assignment algorithm are easily shown by examining the generation of an utterance that contrasts with the utterance shown in NUM
finally the system includes a set of rhetorical constraints that may rearrange the order of presentation for information in order to make certain rhetorical relationships salient
after the coherence constraints from the previous section are applied the sentence planner is responsible for making decisions concerning the form in which propositions are realized
the system is able to produce appropriate intonational patterns that can not be generated by other systems which rely solely on word class and given new distinctions
such approaches fail to consider contextually bound focal distinctions that are manifest through a variety of different linguistic and paralinguistic devices depending on the language
props z a list of properties for object x ordered by the grammar so that nominal properties take precedence over adjectival properties
furthermore the focus may include semantic material that serves to contrast an entity or proposition from alternative entities or propositions already established in the discourse
while this mapping is certainly overly simplistic the results presented in section NUM NUM demonstrate its appropriateness for the class of simple declarative sentences under investigation
certain constraints such as the requirement that objects be identified or defined at the beginning of a description are reminiscent of mckeown s schemata
the resultant chart ix analyzed to i hmtitly the best parsc
these marked up texts are then automatically scored against manually marked up texts
null tables NUM NUM an t figure NUM enabh us to make
during parsing semantic representations of constituents are constructed using prolog terin unification
the grammars it processes are unification style feature based context fl ee grammars
this is done by matching tile input against pre stored lists of proper nalnes
brand narnes book and movie names and ship names are just
null human titles about NUM titles e.g.
we incrementally add information to this constraint base by applying and solving clauses with their associated constraints
we divide these rules into two categories a rule whi tl nit be applied to most of NUM rel ositions is cmled global rule a rule tying to a particular prel osition on the other hand is called local rule
for each sentence with aml igu us pp both ill syntaeti and semantie d level pete system will produ e t structure with unattached pp s and call the disambiguation 1nodule to resolve ambiguous pp s
phase NUM attachment NUM y default if f p NUM then lcb if NUM NUM then choose np attachment f otherwise choose vp attachment rcb otherwise choose np attachment
in this section we consider two kinds of pp attachment in our corlms t ased al l roaeh nalnely attachment to verb phrase vp atta lmmnt and to nmm i hrase np attachment
for examt le given the two concet ts of open and key the dictionary will tell us that there may be a implement relationship NUM etween them means that key may be act its an instrument for the action open
with the first threshohl in ca oh case we can avoid using low frequency tul les with the second one in each case we throw away the ra score which is close to NUM NUM ls tlfis wdue is rather unstabh
ra v nl p n2 a score fi om NUM to NUM ix defined as a value of counts of vp attachments divided by the total of occurrences of v nl NUM n2 in the training data
disambiguation like other work we use fonr head words to make decision on pp attachment the main verb v the head noun nl ahead of the preposition p and the head noun n2 of the object of the preposition
we have changed the order for the following reasons an experinlent has proven that using the data of qua lrul les and triples as well as tut les with high occurrences i s good enough in success rate sec tal lc NUM
when porting to a new language these features need to be converted partly by hand partly by on line lists after which point machine learning ml techniques build decision trees that map features to name classes
this subdialogue continues up to the utterance NUM as shown in the above example dialogues can not be viewed as a linear sequence of utterances
each entry in the table is a compiled dag which represents the relation between a non terminal category and a rule used to rewrite the constituents in the teachability relation i.e. reflexive transitive closure of the left corner path
in this method an operation called anti unification often referred to as generalization as the counterpart of unification is applied to the root and leaf terms of a cyclic propagation and the resulting term is stored in the reachablity table as the result of applying restriction on both terms
it also can help to rank documents with user queries
utterances without verbs belong to frag fragment
sentence type represents the mood of an utterance
table NUM shows the average accuracy of two models
table NUM the distribution of speech acts in corpus
figure NUM a part of the dialogue transition network
the last case is to introduce a new sub
NUM q which amplifier does scott prefer
the algorithm appears in pseudo code in NUM NUM
two points concerning the role of intonation in the generation process are emphasized
several aspects of the contrastive intonational effects in these examples also deserve attention
a mary drove th the red car rh
for the purposes of generation and synthesis these distinctions are crucial
three different pause lengths are associated with boundaries in the modified notation
NUM q which car did mary drive
NUM q do critics prefer the british amplifier
precision and recall and the ability to inspect the examples from the texts that justify the induced tagging procedure
however simply using n utterances linearly adjacent to an utterance as contextual information has a problem due to subdialogues which frequently occurred in a dialogue
in the core language engine approach to syntactic underspecification the representation must be unpacked to perform disambiguation by sorts
the formalism also allows any kind of partially disambiguated structures since thc variables for the readings do not interact
to derive a dpf from a parse forest every edge must be assigned a set of tree readings
then the maintenance effort is reduced to the effort of extrapolating the tree readings from the parse forest
we then traverse the graph in top down fashion applying to each new vertex v the following procedure let ei be the set of tree readings at edge i ending in v and b the set of tree readings at edge j starting in v then the following actions must be performed
for underspecification with respect to s ope atnbigullies the present approach makes use of under this work was funded by the germ m federal ministry of edu ation science research and te hnology bmbf in the flamework of the verbmobil project under grant NUM
to get packed udrss the udrs language is extended by adding reified contexts semantic readings to it
both operations are monotonic in the sense that the pointers are not altered their value is only specified
we call a word or an expression which classifies the text its potential topic and those that appear in the title actual topics
this diagram is continued by response in utterance NUM in utterance NUM this diagram is popped from the stack by response for ask refin utterance NUM
as the result of applying these syntactic patterns to all utterances in corpus we found that the average number of speech act ambiguity for each utterance is NUM NUM
the sentential probability p ui isi represents the relationship between the speech acts and the features of surface sentences
our project builds a processing method on top of the communic ease maptmwhich will expand the telegraphic mis ordered input on the part of the user into well formed sentences
some particles establish a regular semantic pattern which can not be accounted for by a simple enumeration approach whereas others are very irregular and ambiguous
the last test in the branch prior to the shown window tests to see if the word prior to the current word is a person title like president secretary or judge when a decision is being made about whether to start a name with reagan robert or galloway respectively
a step up from this is to determine how to generalize the rule so that it is more broadly applicable or to suggest to the developer which parts of the context have highvalue for inclusion in the pattern
as the root shares some selector feature values slash and sub j with a frontier node this node becomes the foot node
the final derivation step then involves the adjunction of the tree for the equi verb into this tree again at the topmost domination link
this yields the interpretation of the elliptical clause which is given by xt speak chinese bill
these labels figure in equations as well as subordination constraints to express scope relations between quantifiers
we propose a unified framework in which to treat semantic underspecification and parallelism phenomena in discourse
a tree itself represents a formula of some semantic representation language
this seel ion discusses two evaluation criteria for approaches to semantic underspecification
at this point information about positions in the input string is lost
none of the compared approaches makes any claims about theorem proving and transfer
in particular the successors of v need not be checked again
the sets sl and s2 are now equal modulo associativity and commutativity
two operations disjoint union and multiplication are defined for these sct pointers
in the semantic grmnnmr every nonterminal is assigned a list of arguments
first a detinition of udrss is given
let us now define a disambiguated parse forest dpf for short
a natural language correction model for continuous speech recognition NUM
the final set of context sensitive correction rules is generated
s2 portable frontal view of the chest
this would require another cycle of rule validation
some further details are discussed in subsequent sections
mild changes of the 8th rds persist bilaterally
citizen china trade NUM proper noun s might contain such as warren commission national air traffic controller
narr narrative to be relevant a document must describe an effort being made other than routine border patrols in any country of the world to prevent the illegal penetration of aliens across borders
an automated process then scans these NUM documents for all paragraphs which contain occurrences including some variants of any of the key concepts identified in the original query
we first describe the manual process focussing on guidelines set forth in such a way as to minimize and streamline human effort and lay the ground for eventual automation
NUM morphological stemming we normalize across morphological word variants e.g. proliferation proliferate proliferating using a lexicon based stemmer
little work has been done in nlp on the subject of punctuation owing mainly to a lack of a good theory on which computational treatments could be based
the corpus of sentences to run the grammars over should ideally be large and consist mainly of real text from external sources
NUM the big man om this theoretical approach it appears that punctuation could be described as being adjunctive i.e.
after this further pruning procedure the number of rule patterns was reduced to just NUM more than half of which related to the comma
it will also be useful to compare the results with those of studies that have a less formal basis for their treatments of punctuation e.g.
here both tt the tag of the word to the left and tr the tag of the word to the right are one step generalizations of the context NUM rcb tr and both have in turn the common generalization no information
by calculating the estimates of the probability distributions in such an order that whenever estimating the probability distribution in some particular context the probability distributions in all more general contexts have already been estimated we can guarantee that all quantities necessary for the calculations are available
a simple example is estimating the probability p x i n j lc in of word class x given l j l ln tile last j letters of a word ll l
we thus have that ln m h ck or m e iiick the variance of these uniform distributions is m NUM NUM t in the continuous case and in the discrete case
this data can be collected from the words in the training corpus with frequencies below some threshold e.g. words that occur less than say ten times and can be indexed in a tree on reversed suffixes for quick access
since we really want to estimate cr in the more specific context and since the standard deviation with the dependence on context size factored out will most likely not increase when we specialize the context we will use
baum welch reestimation is however prohibitively time consuming for complex contexts if the weights are allowed to depend on the contexts while successive abstraction is clearly tractable the latter effectively determines these weights directly from the same data as the relative frequcncies
however relevant information would be lost if only one particular aspect was chosen with respect to hyponymy
informal experiments indicate that it is possible to achieve slightly better performance by replacing the expression for ro ck with a fixed global con1 stant while retaining the factor i kl which is most likely a quite accurate model of the dependence on context size
these categories are associated with lists of lexical items or already existing features
much human effort is needed to port the system to a new domain
these numbers are useful for associating a confidence level with each classification
determining keyword and key phrase features amounts to selecting prudent subject categories
a desirable approach is one that maximizes reuse and minimizes human effort
next the delimited proper names are classified into more specific categodcs
original john smith chairman of safetek announced his resignation yesterday
during the delimit step the boundarics of all proper names are identified
the additional spanish specific features derived for s s s are shown in table NUM
NUM he 316lt is with a nickel metal hydride accumulator equipped
we present a hybrid text understanding methodology for the resolution of textual ellipsis
zassociated with the set of conceptual roles is the set of their inverses
der rechner wird durch diesen neuartigen akku ftir NUM stunden mit strom versorgt
given in the previous section in terms of so called word actors
both phenomena tend to interact as evidenced by the examples below
the conceptual strength criterion for role chains is already specified in table NUM
lc contains the definite noun phrase die ladezeit
the structural condition is embodied in the predicate ispotentialelliptic antecedent cf
hence akku is determined as the proper elliptical antecedent NUM
information and local parsing are available the precision would be increased certainly
many delays were introduced into the effort by unavailability of infrastructure resources
the developer should identify any dependencies in the schedule for system deployment
any other inferences are also added to the database
for the demonstration system user guided expansion was supported
chinese names present a different problem
should rigorous evaluation metrics by employed
the processing modules are briefly described below
a grammar makes higher level processing possible
however the optimal value for this parameter varied more than an order of magnitude and the improvements in performance were not very large
a further aim is the integration of the retrieval module with other aac systems
the lemmas are looked up in the semantic lex null icon to retrieve related words
it is the latter type of communication aid that will be discussed further here
this is based on the idea that a longer string is more significant as a unit of collocations if it is frequent enough
among them NUM NUM NUM strings are extracted over the entropy threshold NUM NUM NUM NUM respectively
the corpus used in the experiment is a computer manual written in english comprising NUM NUM NUM words in NUM NUM sentences
it means that the distribution of adjacent words is effective to judge whether the string is an appropriate unit or not
according to the sentences shown in figure NUM the is always placed next to refer to
calculating the entropy of both sides of the string we adopt the lower one as the entropy of the string
thus there is the possibility that the method misses significant collocations even though one of the strings have strong cohesiveness
another distinction is that our method does not require any lexical knowledge or language dependent information such as part of speech
next manual and for specific instructions are deleted as the inequation NUM is satisfied
a training set and a test set were obtained by extracting nouns from the newspaper corpus which involves as a sub step tokenizing each article into a set of words
in addition it is illegal for a section to belong to more than one course
he clicks on the word belong and obtains the text shown in figure NUM top
suppose that a university has hired a consultant analyst to build an information system for its administration
NUM is a section and belongs to three courses mathlg0 physicsl00 and engl00
for example a designer looking at a graphical representation of a data model may
if the skipping process encounters a linguistically valid boundary in the most trivial case the punctation mark of the previous sentence it stops and switches to a backtracking mode leading to a kind of roll back of the parser invalidating the currently pursued analysis
our future work mainly includes the utilization of deeper text processing techniques such as part of speech tagging and partial syntactic analysis in phrase generation
figure NUM illustrates the alternative choices and the optimal path found during the processing correcting of the sentence john fornd he man
the less words with inappropriate syntactic categories are included in the index the higher precision will be achieved by the system because less expansions will be generated
in the current system m is NUM NUM and n is NUM NUM use the viterbi algorithm to get the best word sequence for the strings in the sentence
the results for these tests for weak and strong secondary stress are shown in figures NUM and NUM including the difference curves between the randomized word and original entropy rates
contrary to most existing message based system in an aac system based on text retrieval in order to select a message the users do not have to remember any message numbers or another code
the grammar checker is composed basically of three parts i morphological and lexical analysis this part is in fact an extended spelling checker
every time a new item is created the interpreter checks if such an item with the same structure and coverage already exists
if such a syntactic tree exists the evaluation phase tries to decide if there should be an error message warning or nothing
NUM evaluation this phase takes the results of the previous phase in the form of syntactic trees containing markers describing individual syntactic inconsistencies
the grammar checker uses specialized grammar formalism which generally enables to check errors in languages with a very high degree of word order freedom
when a message box containing an error message appears on the screen the user may switch to grammar and get an additional information
to build a syntactic dictionary of about NUM NUM items is a task which exceeds our current capacities with respect both to manpower and funds
if we try to create a prepositional phrase without constraint relaxation we get one resulting item pp animate accusative sing
most of the unnecessary strings consist of only punctuation marks and function words
in this example the position of the is examined first
in the first stage of this method NUM NUM strings are produced
as a result NUM combinations of units are retrieved as collocations
no NUM and NUM in table NUM are the examples of invalid collocations
table NUM examples of collocations extracted at the second stage
this told us that the precision of the first stage is NUM NUM
some of them contain punctuation and some of them terminate in articles
table NUM top NUM strings extracted at the first stage
one approach in name tagging is to assist in the creation of hand coded rules by making it easier for the developer to mark parts of the name and its surrounding context to include in the pattern
it has to be noted however that the use of full scale syntactic analysis is severely pushing the limits of practicality of an information retrieval system because of the increased demand for computing power and storage
thlls whell w know ford motor co is an organisati m name bill have n t lassificd f rd in the same text or f w n
NUM how proper names are recognized and classified as indicated in section NUM our approach is a heterogeneous one ill which the system makes use of grai hological syntactic selnantic world knowledge and discourse level intb mation for the recognition and classification of proper names
with a known locati m nanm the former name is also classified as a location e.g.
secondly these four name classes account for the bulk of proper name occurrences in business newswire text
table NUM illustrates the contribution of each system module to the task for all classes of proper names
the overall precision and recall scores for the four classes of proper naines are shown in table NUM
note that both of these tcctmiques inake use of external evidence i.e. rely on information supplied by the
f r simple verbs and llotnls i h mort hologi al
we have also examined how the contribution of each component varies from one class of proper nanm to another
at iiosition when an unclassilied proper name ix apt s d
thus even though the government owned software can be shared reasonably freely among agencies costs related to the distribution would need to be born by someone presumably the agency requinng the software
this push can be accomplished during the development of a specific application but these intermediate steps generally steps NUM NUM must be incorporated into the planning and budgeting for such an application
however these efforts alone will not result in the rapid development of inexpensive robust and well supported commercial products which also meet the government analyst s requirements for information handling of text
it is the user who knows what task has to be accomplished but it is the technologist who understands what the technology can do and how it can be configured to potentially help
in the case of the tipster program most of the leadership has been taken by the technologists albeit technologists with considerable experience in analytical tasks and knowledge of present and future user requirements
i have outlined in this paper much of what i have learned from working in the tipster program about transferring technology from the research stage to daily use in an operational environment
in working jointly on this endeavor we have all learned these things and many of the ideas recorded here have been or are being incorporated into the program to help guide future technology transfer
some technology transfer observations from the tipster text program
canis and ftm are both examples of this step
NUM what is meant by technology
lud cont ext equal fun result context fun funcontext context hrg argcont ext subcat result resultsc subcat fun hrgcontext resultsc
tug is a formalism that combines ideas from government and binding theory namely the use of traces with unification in order to account for for example the free word order phenomena found in german
the functor is looking for the argument as the first element on its subcat list while the result s subcat list is that of the functor minus the argument which has been bound in the rule
the context of a lud representation is a three place structure consisting of the ludrepresentation s main label and top hole as described in section NUM NUM and its main instance which is a discourse marker or a lambda bound variable
to see how the composition interacts with the lexicon consider the following lexical macro defining the semantics of a transitive verb subcat cat lud argl lud arg2
drss k1 and k and returns a i rs which domain is the nnion of the set of the domains of k1 and k2 and which conditions form the union of the set of the conditions of k1 and k2
more generally it can express the equality up to relation over trees which captures non local parallelism between trees
they use an algorithm for higher order matching rather than context unification and they do not distinguish an object and meta language level
NUM and NUM respectively of the two sentences here rather than to solved forms of NUM and NUM separately
the client connects to the robotag server on a network using tcp ip
table NUM shows some of the features used by robotag for learning
these attributes are referred to as fealures
as at the same time only one of these can be recognized is suitable to join the single class descriptions into decision list covering the whole set of examples
if such a feature or features are found they will be used to restate t r as a context sensitive rule xly xry where x and y are context features
this in turn leads to breaking down some unwieldy and unlikely candidate rules such as the one that may arise from aligning two or more consecutive transcription errors together as illustrated by the above example
many advanced speech recognition systems use trainable language models that can be optimized for a particular speaker speaker adaptable or speaker independent as well as for a specific sublanguge usage e.g. radiology
since such words will be eventually entered into the srs lexicon thus eliminating transcription errors we decided not to generate correction rules for them at this time
the candidate rules derived in the previous step are validated by observing their applicability across the training collection which consists of the parallel text training data as well as a much larger radiology text corpus
in practice we face limits of rule learnability due to sparse data as well as rule interference i.e. one rule may undo another etc
however adding a one word context of the word view i.e. replacing the context free rule with a context sensitive from view frontal view produces a very good correction rule
m strube and a grant from dfg ha NUM NUM NUM u hahn
we have outlined a model of anaphora resolution which is founded on a dependency based grammar model
the system comes with a clock frequency of 50mhz
the results we present rest upon two major assumptions NUM
at the last step all the remaining word pairs are manually examined
for example automatic indexing is the foundation of many other relevant tasks
however most of the terminologies could n t be found in the dictionary
those words with highest correlation coefficient are almost terminology words and proper names
therefore only a small number of them have entries in universal dictionaries
among the whole vocabulary more than NUM are extracted new words
the denotation of a demonstrative noun phrase is the denotation of its r modulo any further restriction imposed on it by its determiner
correlated with this are several other criteria cardinal numerals and quasi cardinal numerals e.g. several modify count nouns never mass nouns
this conversion appears with such nouns as breads cheeses clays coffees salts minerals oils teas and wheats
i now turn from the semantics of demonstrative noun phrases to how they are evaluated with respect to the predicates of which they are arguments
moreover little and much modify mass nouns never count nouns whereas few and many modify count nouns never mass nouns
in other words the concrete individual a can be seen as an aggregate a the smallest aggregate which can be formed from a
common nouns for animals which humans eat can be used to denote the largest aggregate of those parts considered suitable for human consumption
thus the quantified noun phrases in the sentence in NUM can be assigned the scopal configuration shown in NUM
the second class of words include the following stone rock ash string cord rope and tile
mass nouns do not tolerate the indefinite article an advice whereas singular count nouns do a suggestion
this work is partially supported by the engineering and physical sciences research council epsrc grant j19221 by bc daad arc project NUM by the commission of the european union grant lre NUM and by the onr grant n00014 NUM NUM NUM
when this is finished the kgml generator is called once for each sentence to produce the actual text
walkthrough of the system in this demo we illustrate how a technical author would work with drafter
clearly however the entities acquired automatically are not all that is needed to document the interface properly
locations by contrast exhibited the lowest performance
however if they fail to identify a misunderstanding the communication might mislead them into prematurely believing that their goals have been achieved
the claim that a pronoun in the superordinate clause must not be coreferent to an antecedent in a subordinate clause is then obviously false cf
the second utterance in the figure if it occurs immediately after an utterance such as the first one would be an acceptance
secondly some of the raw data is being associated with multiple interpretations deemed reasonable by one of the team members
our second version of the system has an automatic segmentation component
the infinder technology shows a lot of promise in this area
we employ corpus based likelihood analysis to choose most likely attachment
however corpus based approaches suffer from the sparse data problem
tl is information comes from a machine readable dictionary
lcb wu furugori rcb ophaeton cs
for triplecombimttion the c mdition is
finally we show an experiment and its result
we put the hybrid approach in an disambiguation algorithm
this algorithm differs from the previous one de scribed
so that they make the computation efficient and extensible
particular properties may also be labeled as distinguishing for a specific class
the program then consults the facts in the knowledge base verifies that the property does indeed hold and consequently includes the corresponding facts in the set of properties to be conveyed to the hearer as shown in NUM
in other words given an object x a list of its properties and a set of alternatives the set of alternatives is restricted by including in the initial rset only x and those objects that are explicitly referenced in the prior discourse
the determination of contrastive focus and consequently the determination of pitch accent locations is based on the premise that each object in the knowledge base is associated with a set of alternatives from which it must be distinguished if reference is to succeed
first since intonational phrasing is dependent on the division of utterances into theme and theme and since this division relates consecutive sentences to one another matters of information structure and hence intonational phrasing must be largely resolved during the high level planning phase
the author is grateful for the advice and helpful suggestions of mark steedman justine cassell kathy mckeown aravind joshi ellen prince mark liberman matthew stone beryl hoffman and kris th6risson as well as the anonymous acl reviewers
if imposing this restriction on the rset for a given property decreases the cardinality of the rse then the property serves to distinguish x from other salient alternatives evoked in the prior discourse and is therefore added to the contrast set
for example although the representation in NUM specifies that e2 stands in contrast to some other entity it is the property of e2 having a tube design rather than a solid state design that needs to be conveyed to the hearer
this is accomplished by the rhetorical constraints that determine the two propositions to be contrastive because e4 and e5 belong to the same set of alternative entities in the knowledge base and praise and revile belong to the same set of alternative propositions in the knowledge base
the higher tier which delineates the lheme that which links the utterance to prior utterances and the theme that which forms the core contribution of the utterance to the discourse is instrumental in determining the high level organization of information within a discourse segment
the proposed method has an obvious difficulty the complexity caused by the repeated propagations could become overwhelming for some grammars
this time it results the same d NUM therefore the propagation terminates
NUM in the proposed method a and b are first checked for subsumption
for future work we intend to apply the proposed method to other grammars
although the current definition covers most cases it is by no means complete
however due to the nontermination of parsing with left recursive grammars top down constraints must be weakened
set NUM then using the updated restrictor propagation is re done from a
notice the filtering function p is applied only to d
composition can be thought of as a default which can be overwritten by explicit entries in the database
figure NUM NUM gram stress entropy rates and difference curve for el weak secondary stress
treatment of lexical stress across word boundaries is scarce in the literature however
the atomic elements in the text stream the words contribute regularity independently
the stress patterns of individual words within a phrase or sentence are generally context independent
null this is verified when we run the stress entropy rate computation for each bin
pauses in the text arise not only from semantic constraints but also from physiological limitations
comparing the unrandomized results with this control experiment allows us therefore to factor out everything but word order
we tested three different encodings NUM boolean encoding of pos xi NUM i c NUM is set to NUM NUM if the word s part of speech is the i th part of speech and to NUM NUM otherwise
the definition of a small clause which we wanted the neural network to learn the boundaries of is as follows any finite clause that contains an inflected verbal form and a subject or at least either of them if not possible otherwise
the transcripts are tagged with part of speech pos data from a set of NUM tags NUM and were processed to extract trigger words i.e. words that are frequently near small clause boundaries b
we use a fully connected feed forward three layer input hidden and output artificial neural network and the standard backpropagation algorithm to train it with learning rate NUM NUM and momentum NUM NUM
we have shown that using neural networks for automatically segmenting turns in conversational speech into small clauses reaches a level of less than NUM error rate and achieves good precision recall performance as measured by an f score of more than NUM
in this paper we bring their idea to the realm of speech and investigate the performance of a neural network on the task of turn segmentation using parts of speech indicative keywords or both of these features to hypothesize segment boundaries
future work on this problem includes issues such as optimizing the set of pos tags adding acoustic prosodic features to the neural network and using it for pro drop languages like spanish to assess the relative importance of pos vs trigger word weights and to examine the performance of the system for languages where pos tags may not be as informative as they are for english
these results suggest that adding features relevant to speech repairs such as whether words were repeated or features relevant to detecting the use of and as a non clausal conjunct might be useful in achieving better accuracy
first we ran NUM experiments on the small data set exhaustively exploring the space defined by varying the following parameters encoding scheme pos only triggers only pos and triggers
for instance in a situation where the user has typed an adjective only icons that begin valid next words e.g. adjective noun will be highlighted thus facilitating learning
individual nouns have associated semantic types
simple nlp techniques for expanding telegraphic sentences
cognitive difficulties may also be present
most of these words are coded as NUM icon sequences
the second icon denotes the specific word
assimilation makes use of existing acoustic models from a language that has a large phonetic overlap with the target language
we see that the stack of exponents involved is determined by the number of quantifier alternations
only those are present in both which are part of the constraint in an applied clause
the associated constraint tree is easily compilable and serves as the initialization for our parse tree
we can also use offline compiled modules in a t ws2s parsing program
the applications we mention here could be adapted to strings with finite state automata replacing tree automata
the usefulness of the approach is discussed with examples from the realm of principles and parameters based parsing
we can then percolate a2 as long as the other branch does not immediately dominate xl
so we can use these sets to pick out arbitrarily large finite trees embedded in n2
before we will outline a way of processing decomposable idioms in section NUM we will briefly introduce the necessary tools for the parsing process in a few brief words in section NUM
the following example shows a rule connecting an object and the verb phrase of a sentence checking if both the verb and the noun are NUM arts of the same idiom
today it becomes more and more evident that a too restricted view on idiomatic phenomena is of limited use for the purpose of natural language processing
a discom se referent for biir or hock respectively tall tale or mi stakc is already introduced dm ing the initialization of the chart
this will reduce the munber of lexical lookups to pitrasi o lex as well as the number of edges in the t arser
einen b en anfbinden is now decomposable into the noun phrase cinen biiren referring to a tall tale and the verb aufbinden referring to the activity of telling
we adopt the i oint of view that itenis as l ock or a c3dlllot be onsidered as quasi argmnents but as jigurativc
the syntactic feature structures of the literal and the idiomatic reading are the same as there is no pure syntactic difference between tile two readings
the notation in germanet differs from the celex database in providing a notation for the subject and a complementation code for obligatory reflexive phrases
i in cases of temporal inclusion of two events as in schnarchen snoring entailing schlafen sleeping
germanet provides frames for verb senses rather than for lemmas implying a full disambiguation of the celex complementation codes for germanet
each verb sense is linked to one or more syntactic frames which are encoded on a lexical rather than on a conceptual level
because of the cognitive impairments it is also likely that the user will have a great deal of difficulty if the system acts in unexpected ways
for example given the goal describe x the intention persuade to buy hearer x may result in a radically different monologue than the intention persuade t o s ell hearer x
a two tiered information structure representation is used in the high level content planning and sentence planning stages of generation to produce efficient coherent speech that makes certain discourse relationships such as explicit contrasts appropriately salient
rather than imposing strict rules on the order in which information is presented the order is determined by domain specific knowledge the communicative intentions of the speaker and beliefs about the hearer s knowledge
the augmented transition network formalism was chosen for this work mainly because it allowed parallel traversal of all possible parses and therefore the ability to predict next input words
while there are certainly a number of linguistically interesting aspects to the sentence planner the most important aspect for the present purposes is the determination of theme foci and rheme foci
in english a variety of large scale online linguistic resources are available
lartlfact i iorganizativnl i NUM if ilityl lin titutio
syntactic information in germanet differs from that given in wordnet in several ways
first germanet is searched for an explicit entry of the particle verb
it is generally agreed that a pure sense enumeration approach is not sufficient
definition NUM let a p be a formula and d p be a tuple of formulas wh ich is brokcn into 4il p NUM NUM p NUM p where p is a tv ple of predicates used in these formulas
biguation wil hin gm hclp la ngua ge by showing correslmndc ncc between a priorit y oww preference rules in prioril ized cir mms rit t ion m d a olmtr fint hi rar hy in ltci p
figure NUM without artifical concepts figure NUM lexical gaps
even though circumscrit tion is one of the most pottular fornlmisn s in the collllllllllit y of llolllllollotollic reasoning rcs ar h it is surprishlg that w ry few h ts examined feasibility of ircumscrit tion for disa mbiguation
we lo not nec l to assign detailed mlmcri al val null ues to t referellce rules in ord r to express t riority over t r ference rules but just specify a t ref rence level of the rules
wordnet does contain artificial concepts that is non lexicaiized concepts
every object in a class is assumed to contain at least the information given by the constraints specified for it and all its ancestors
the parameters in the rule definition are variables which can be used within the actual default rule at the end of the description
this is the case because we follow young and rounds approach and separate the unification operation from the explanation of a nonmonotonic rule
the first and most important is that we require a subsumption order on the set s of objects denoted by the formalism
an intuitive interpretation of the defined rule below is that if x is believed NUM e x failure should be derived
when using nonmonotonie sorts containing nonmonotonic rules we also need to know how to merge the monotonic and nonmonotonic information within the sort
one use of value constraints in lfg is to assert a condition that some grammar rules can only be used for passive sentences
thus in this case there are two explanations for the structure dependant on which of the rules that has been applied first
therefore the order on which nonmonotonic rules at different substructures are applied is important and all possible application orders must be considered
as shown by the definition a w explanation is computed by choosing one w applicable default rule at a time and then applying it
in addition two heuristic rules are adopted to modify the weights introduced for this reason
the normal frequency could be obtained either from a balanced on line frequency dictionary or a universal corpus
this helped to explained that most of the terminology words in the universal dictionary had been extracted
figure NUM demonstrates the recall precision curves of computer world corpus using original dictionary and augmented dictionary respectively
terminologies are specialized words and compound words used in a particular domain such as computer science
weights of possible words were calculated using their frequency and length information NUM
if these words correlation coefficient is large enough they may probably make up a collocation
this identification procedure was also done by several graduate students major in computer science
NUM NUM of all terminologies found by experimenters were als0 found by the program
the key point here is that direct comparisons are generally user initiated
figure NUM the architecture of the peba ii sys tem
the old parser refused to build non nf constituents the new parser will refuse to build constituents that are semantically equivalent to already built constituents
this says the nf parser is safe for pure ccg we will not lose any readings by generating just normal forms
it can detect redundant parses by noting via an o NUM array lookup that their nfs have been previously computed
yet for intentionally knock twice this is not the case these adverbs do not commute and the semantics are distinct
by contrast the two readings of softly knock twice are considered to be distinct since the parses specify different recipes
NUM if there are no rules in any group that exceed its threshold group thresholds are reduced by multiplying by a damping constant d NUM d NUM and iterations are continued
then for every unambiguous context c lc rc with either an immediate left or an immediate right components or both so n obviously these features are specific to a language
then delete rules of the sort if lc and rc then delete pi are generated for all parses with a score below a certain fraction NUM NUM in our experiments of the highest scoring parse
for example a very appropriate comparator for the echidna is the porcupine but the two entities are not closely related within the linnaean taxonomy of animal classes
the size and coverage of wordnet led to the decision to base the indexing module and the semantic expansion in the wordkeys system on this lexical database
at a first glance the implementation of a text retrieval system for aac users might seem straightforward as retrieval techniques have been investigated for decades
for example wing and l rwer airlines is ahnost certainly a company given tile presence of the word airlines
the version of the system reported here achieves ahnost NUM combilmd precision and recall scores on this task against blind test data
this is the full fledged system results using tagging exact phrase matching trigger word detection and parsing setting NUM
the second activity whi h is arguably not l rolterly lis ourse intcrilr t ation
thor are thwcr rules lin location ttmnes as th y are i h ntiti d
we claim that the alternate view should be provided by an explanation tool that represents the data in the form of a standard english text
we therefore conclude that graphics in order to assure maximum communicative efficiency needs to be complemented by an alternate view of the data
both user groups showed semantic error rates between NUM and NUM for the separately scored areas of entities attributes and relations
we also argued in favor of incompleteness in order to break the text parsing complexity barrier
upon receiving the analyzewithcontext message from the parseractor of
this avoids severe problems usually encountered in parsers with unrestricted parallelism
these experiences led to a redesign of the first prototype
the parsetalk grammar we use for a survey cf
4thus synchronization of protocols enables word wise scanning backtracking etc
we encounter a trade off between robustness efficiency and completeness in parsing
thus ill formed input does often still have an incomplete analysis
the search messages are then asynchronously distributed to words within each phrase
actors communicate by sending messages usually in an asynchronous mode
as mentioned earlier the peba ii system allows the user to request one of two actions to describe a single entity or to compare two entities
words or word bigrams with frequency less than three were discarded
the system requires several passes to correct an ocr text
it can correct non word errors as well as real word errors
unknown words and errors caused by changing correct lexicon words
using bayes formula we can rewrite NUM as
however the estimatation of unseen bigrams is a problem
this method proves to be quite effective in improving system performance
these rewards are important because the process of technology transfer is difficult and messy
central to the initial tipster planning then was the goal of technology transfer
thus i hesitate to say that one set of issues is more important than any other
some like formal reviews others like people to stop by and chat and so forth
first we will briefly compare our approach with work done in the context of government binding gb grammar and discourse representation the ory drt
in this section we present quite informally some constraints on intra sentential anaphora in terms of dependency grammar dg
in this position the pronoun does not c command the antecedent and the adjunct of the subject is also in an a position
similarly for pronominal anaphors the selected antecedent must be permitted in those conceptual roles connecting the pronominal anaphors and its grammatical head
these two predicates cover the knowledge related to the resolution of intra sentential as well as inter sentential anaphora
for all three tags we used a radius of NUM and certainty factors of NUM NUM for pruning
this includes the parameter settings for the learning algorithm feature usage statistics and preprocessor output
considering all possible begin end tag pairings quickly becomes intractable as the number of potentially interacting tags increases
for proper noun tagging the priority order from highest to lowest is person entity place
the client consists of a tagging tool interface written in tk tcl a cross platform gui scripting language
the interface shown in figure NUM is designed primarily to function as a tagging tool
the monologue generation program produces text and contextually appropriate intonation contours to describe an object from the knowledge base
this research was funded by nsf grants iri91 NUM and iri95 NUM and the generous sponsors of the mit media laboratory
initially the program assumes that the hearer has no specific knowledge of any particular objects in the knowledge base
NUM delist a collection of discourse entities that have been evoked in prior discourse ordered by recency
in each of these examples the answer contains the same string words but different intonational patterns and information structural representations
the remainder of this section contains a description of the computational path through the system with respect to a single example
the next phase of content generation recognizes the dependency relationships between the properties to be conveyed based on shared discourse entities
during the content generation phase the content of the utterance is planned based on the previous discourse
the first of these tasks can be handled in the content phase of the nlg model described above
the relationship between intonational structure and information structure is illustrated by NUM and NUM
this allows interfaces to remain stable preventing any need for retraining of users or redesign of inter operating software
it is well known that in first order logic and in certain related forms of feature structures there is always a most general unifier for any equation that is solvable at all
thus allowing two different readings the corefential or strict reading vp p e lcb ax ex x y i
in this section we present one such constraint the so called weak crossover constraint and show how it can be implemented within the hocu framework
as remarked in section NUM NUM we have to interpret the color pe as the concept of being not primary for ellipsis which includes pf primary for focus
perhaps the most convincing way of showing the need for a theory of colors rather than just an informal constraint is by looking at the interaction of constraints between various phenomena
the relevance feedback stage of the experiment was based on a bigram model which means that a number of two character sequences from the relevant documents were selected for query expansion
but if this expression is stored as a single term then perfectly reasonable queries such as physics institutes in china or beijing technical institutes would fail to match that term
as a reinvention laboratory the tipster program offers the government not only an opportunity to foster large scale research and development but also avenues to deploy the resulting enhanced technologies
to enable query input to the chinese language version of inquery it was desirable to have a graphical user interface platform that would allow the input and display of chinese characters
this is equivalent to assigning a count of one to the occurring and one third to the non occurring outcomes
their best result of NUM NUM accuracy was achieved with a context of NUM words and NUM hidden units
the real numbers xl xc are the values given as input to the first layer of the network
by rapid deployment we mean being able to develop an mt system that performs initial translations at a useful level of quality between a new language and english within a matter of days or weeks with continual graceful improvement to a good level of quality over a period of months
moreover these technologies differ not just in the quality of their translations and level of domain dependence but also along other dimensions such as types of errors they make required development time cost of development and ability to easily make use of any available on line corpora such as electronic dictionaries or online bilingual parallel texts
the diplomat project is designed to explore the feasibility of creating rapid deployment wearable bi directional speech translation systems
finally due to the goals of our project language modeling is necessarily based on small corpora
thus pluggings where the predicate for sentence mood is subject to a leq constraint should not be considered
they would result in a resolved structure expressing that the mood predicate does not have scope over the remaining proposition
the contents of this slot is handed up the tree from the daughters to the mother completely monotonically
this is caused by the overall architecture of the verbmobil system which does not provide for fully interconnected components
some sample lexical entries for german as well as a sample derivation are shown in figure NUM
if this modification is done by proper unification the monotonicity of the formalism will still be guaranteed
there is subordination strict subordination and fimdly presupposition c
the actual implementation is described in section NUM which also discusses coverage and points to some areas of further research
there is e.g. no direct connection between the speech recognizer and the component for semantic evaluation
the size of the lexicon will then be about NUM entries which amounts to about NUM lemmata
at this time it is also an open question how much improvement can be achieved using this method i.e. if there is an upper bound and if so what that is
define pr ilj to be the conditional probability that the substring sl j is recognized as substring tl i by the ocr process i.e. pr tl ilsl j
formula NUM may determine that a sequence of four operations NUM substitute f for f NUM substitute t for l NUM substitute a for o and NUM delete g maximizes the conditional probability pr flo l flag
we will have to develop an all speech version of the interviewee side interface
we have explored two strategies for acoustic modeling
in 4d the pronominal adverb daran occurs in a correlative construct with a subordinate daft that clause immediately following it
for instance sentence NUM is a verb second clause with an adverbial in the first position in the clause and one nc following the verb
in this construct the pp headed by the pronominal adverb may potentially be attached to the verb phrase or to the nominal phrase immediately preceding it
NUM er lobte die reaktion der 5ffentlichen meinung in rubland he praised the reaction the public opinion in russia als beweis dafiir dab
the method described in the previous section was applied to NUM year of the newspaper ffrankfu er allgemeine zeitung containing approximately NUM million word like tokens
given the fact that the cues produced by the system are not perfect predictors of subcategorization a test of significance could be introduced in order to filter out potentially erroneous cues
the aim of the wordkeys project is to enhance a communication aid with techniques based on research in text retrieval in order to reduce the cognitive load normally associated with retrieving pre stored messages in augmentative and alternative communication aac systems
the main differences between standard information retrieval and text retrieval for an aac system were presented namely the size and type of texts retrieved by the system and the necessity to minimize the cognitive load which leads to the minimal input requirement
this is most obvious in the case of ellipsis interpretation NUM but is also evident for the resolution of the anaphor in the correction in NUM and in the variation case NUM where the context is unknown and has to be inferred
for example the tree many language lamx spoken by john varx represents the hol formula poke by j note that the function symbol represents the application in hol and the function symbols lamx the abstraction over x in hol
a conlezt function NUM is a function from trees to trees such that there exists a variable x and a context t with hole x satisfying the equation NUM r t r x for all trees or
in the correction pair of sentence NUM it provides a certain unambiguous reading for the pronoun in NUM it gives x8 speak chinese x as a partial description of the overheard or unuttered source clause
for example the first two constraints in example NUM result from applying rule iii where the values for the quantifiers two language and many linguist are already substituted in for the variables xr in both cases
we decided to use the semantic database wordnet for the following reasons it is very comprehensive it contains most relevant semantic links the information contained in wordnet is stored in text files and can be easily converted to any other format
we chose a format which was easily portable a text file containing lemmas together with their syntactic category and related words corresponding to the differ null ent senses the semantic paths that the wordkeys soft null ware uses for query expansion were defined
word in message is same word form as NUM exact match best rating input word word in message is lemmatized in index NUM lemmatization leads to less semantic and matches input word distance than derivational analysis word in message is reduced to root in NUM derivational analysis index to match input word semantically related word is looked up NUM depends on semantic relation in lexicon we will illustrate the message ranking with an example
many thanks are due to m dorna j dsrre m einele e ksnig bamner c rohrer c j rupp attd c vogel
one of the most inl eresting problems comes about by the tendency of natural language discourse to be ambiguous and open to a wide variety of interpretations
the algorithm visits every node in the parse forest only a bounded number of times so that a significant increase in efficiency is registered for ambiguous sentences
a focus in a rule is the constituent which gets assigned all argument fi om the ba kground constituents of the rule
furthermore the semantic grammar specifies the udrs conditions introduced by lexical items and rules and determines the arguments to be matched in rules and lexical items
in addition labels and discourse referents are matched as specified in the semantic part of the grammar rules the semantic grammm
so instead of postulating a fixed set of readings the present approach uses pointers implemented as prolog variables to refer to sets of tree readings
this section reports on an experiment in which the efficiency of the proposed underspecified construction mechanism was measured against the cost of generating all udrss separately
let us assume a boi toln ut traversal of the parse forest and let e be the edge fi om v to one of il s successors w
as indicated above our approach to coping with error prone speech translation is to allow user correction wherever feasible
the evaluation of a particle verb takes the following steps
the stems stream is the simplest yet it turns out the most effective of all streams a backbone in our multi stream model
note that the standard stemmed word representation stems stream is still the most efficient one but linguistic processing becomes more important in longer queries
other types of query expansions including general purpose thesauri or lexical databases e.g. wordnet have been found generally unsuccessful in information retrieval cf
these documents are not judged for relevancy nor assumed relevant instead they are scanned for passages that contain concepts referred to in the query
in fact the presence of such text in otherwise non relevant documents underscores the inherent limitations of distribution based term reweighting used in relevance feed null back
NUM each query is manually expanded using phrases sentences and entire passages found in any of the documents from this query s expansion subcollection
in evaluating the expansion effects on query by query basis we have later found that the most liberal expansion mode with no pruning was in fact the most effective
for each query the resulting ranking was filtered to keep for each document the highest score obtained by the fragments of that document
these types of pairs account for most of the syntactic variants for relating two words or simple phrases into pairs carrying compatible semantic content
one of our goals was to demonstrate that robust if relatively shallow nlp can help to derive a better representation of text documents for statistical search
NUM phase NUM is triggered independently from phase NUM and NUM the path leads from the initiator to the sentence delimiter of the previous sentence where its state is set to phase 3a
a in phase 2a the message is forwarded from the head which d binds the sender to each of its modifiers excluding the sender of the message where both anaphor predicates are evaluated
morphosyntactic conditions require that a pronoun must agree with its antecedent in gender number and person while a definite np must agree with its antecedent in number only
criteria for anaphora resolution within sentence boundaries rephrase major concepts from gb s binding theory while those for text level anaphora incorporate an adapted version of a grosz sidner style focus model
in NUM where the subject valet is modified by the genitive attribute des gewinners the antecedent is governed by the head which also d binds the pronoun
gb on the other hand is strong with respect to the specification of binding conditions at the sentence level but offers no opportunity at all to extend its analytic scope beyond that sentential level
some of the equations in the examples have multiple most general solutions and indeed this multiplicity corresponds to the possibility of multiple different interpretations of the focus constructions
since the hocu is the principal computational device of the analysis in this paper we will now try to give an intuition for the functioning of the algorithm
colors are not sorts in particular it is intended that different occurrences of symbols carry different colors e.g.
for example if a is very high then the probability pr sls will be too high to be affected by subsequent processing and will not be changed
since a major goal of diplomat is rapid deployment to new languages the gui uses the unicode multilingual character encoding standard
h NUM with the imitation 6for NUM we have the same situation here the cor t responding equation is tpf ex pf c i pf ipf
in contrast 3d is not a possible solution since it assigns to an pe coloured variable a term containing a pe coloured symbol i.e. a term that is not pemonochrome
that is not only do colors allow us to correctly capture the interaction of the two pors restricting the interpretation of ellipsis of focus they also permit a natural modeling of focus projection cf
recursively visiting and optimally splitting each concurrent subset results in the generation of NUM nodes not including leaf nodes
the weighted average of the p r for companies persons locations and dates is NUM NUM
how much additional eltort will be required and what degradation in performance if any is to be expected
how can a system developed in one language be ported to another language with minimal additional effort and comparable performance results
delimitation is the determination of the boundaries of the proper name while classification serves to provide a more specific category
the fea ture which minimizes the weighted sum o1 tiffs function across both child nodes resulting from the split is chosen
an example of a tree which was generated for companies is shown in figure NUM
the date grammar is rather small in comparison to other name classes hence the performance for dates was perfect
the approach taken here is to utilize a data drivcn knowledge acquisition strategy based on decision trees which uses contextual information
this experiment involved removing one feature at a time from the text used for testing and then reapplying the stone tree
the ranking imposed on the elements of the ci rellccts the assumption that the most highly ranked element of cy u is the most preferred antecedent of an anaphoric or elliptical expresskm in ij while the remaining elements are ordered by decreasing preference for establishing refereutial links
preferredconceptualbridge y x n c ispotentialellipticantecedent y x n a NUM z ispotentialellipticantecedent z x n a strongerthan cp cp y v asstrongas cp cp j
here the elliptical expression taktfrequenz clock frequency can tentatively be related to three antecedents in the preceding sentence er it which is an anaphoric expression for 316lt stunden hours and strom power
while grosz et al assume that grammatical roles are the major determinant for the ranking on the c t we claim that for languages with relatively free word order such as german it is the functional information structure is of the utterance in terms of the context boundedness or unboundedness of discourse elements
thus it complements the phenomenon of nominal anaphora where an anaphoric expression is related to its antecedent in terms of conceptual generalization as e.g. rechner computer refers to 316lt a particular notebook in lb and la
it will only be triggered at the occurrence of the definite noun phrase np when np is not a nominal anaphor and the referent of the np is only connected via certain types of relations e.g. has property has physical part NUM to referents denoted in the current utterance at the conceptual level
the theme rheme hierarchy of un is determined by the c u NUM the most rhematic elements of u are the ones not contained in c u j unbound discourse elements they express the new information in u
ispotentialelliptieantecedent y x n c y isac nominal a x isac noun a NUM z x headz a z isac detdefinite a x e u ay r e cf u x the predicate preferredconceptualbridge cf
a lexical item y is determined as the proper antecedent of the elliptic expression x iff it is a potential antecedent and if there exists no alternative antecedent z whose conceptual strength relative to z exceeds that of y or if their conceptual strength is equal whose strength of preference under the is relation is higher than that ofy
since ladezeit charge time does not subsume any word at the conceptual level in the preceding text the anaphora test fails the definite noun phrase die ladezeit has also not been integrated in terms of a significant relation into the conceptual representation of the utterance as a result of its semantic interpretation
with dtn and the stack the system makes expectations for all possible speech acts of the next utterance
in this paper we approximate utterances with a syntactic pattern which consists of the selected syntactic features
in this paper we propose a statistical dialogue analysis model based on speech acts for dialogue machine translation
in his way it is guaranteed l hat argument matching is done 4if several tree readings correspond to a single context semantic reading this is reeognised in the la st step determining unambiguous arguments where the tree readings are merged
this seems to be true for any approach relying on delay of semantic construction operations in order to apply the sortal restrictions of e.g. a verb to one of its argument discourse referents it must be known which discourse referents could possibly fill the argument slot
the resulting database is an inquery database which can be updated as desired so that as new usages appear in the text they can be added automatically to the infinder database
the message level representation is a list of discourse domain objects ddos for the top level events of interest in the message e.g. succession events in the muc NUM domain
the fpp is a near deterministic parser which generates one or more non overlapping parse fragments spanning the input sentence deferring any difficult decisions on attachment ambiguities
when cases of permanent predictable ambiguity arise the parser finishes the analysis of the current phrase and begins the analysis of a new phrase
the primary focus of tipster phase one was to advance the state of the art in document detection and information extraction through multiple contract awards for different algorithmic approaches
the xat library supports display of several different languages and two important characters encodings for chinese the traditional or big5 encoding and the simplified or guobiao gb encoding
therefore the entities mentioned and some relations between them are processed in every sentence whether syntactically illformed complex novel or straightforward
in communication mode wordkeys displays the search field where the user can type in search words the list of predicted input words the list of messages found and the field containing the selected message
a formal model of text summarization based on condensation operators of a terminological logic
it follows that similarities used in first estimation are close to each other
and by using the obtained similarities we can modify bunrui goi hyou
it is difficult to build knowledge corresponding to each domain from zero
the proposed method redefines the similarity in a handmade thesaurus by using corpora
redefining similarity in a thesaurus by using corpora
the level of the timation is NUM NUM
tab NUM shows the results of evaluations
the goal is to learn to predict the class label from the other features in the vector
a noun phrase of the form xc no n where c is a group classifier will be translated as x c of n where n will be plural if it is headed by a fully or strongly countable noun or a pluralia tanta
the three types of unit classifier are summarized in table NUM a having established three possible translations of the xc no n construction we can proceed to divide unh classifiers into three types depending on which of the above alternatives is most suitable
NUM i tsu no koppu l piece of cup NUM c tp genei ai however if n is a noun that denotes an attribute such as price or weigiit then the translation process becomes more complicated
distinct ion b l we m lassitiers of frequen y and ol her unit lassitiers rcb y u qing our general sema nl i
ilowever if the noun phrase is used ascriptively then it should be converted ither to an adjective it is lore high or a prel osit ional pin as it is lo yen in price
there are however several fiat objects for which piece is inappropriate in english food stuffs slice paper glass cloth and leather sheet bacon rasher and financial contracts contract
the framework we propose offers a variety of subtle parameters on which scalable text summarlzahon can be based
this data was hand tagged with the locations of company names person names locations names and dates
this can be attributed mainly to NUM locations are commonly associated with commas which can create ambiguities with delimitation and NUM locations made up a small percentage of all names in the training set which could have resulted in overfitting of the built tree to the training data
NUM esignators are features which altmc provide strong evidence lbr or against a partic ular nantc type
various parameterizations were used for system development including NUM context depth NUM feature set size NUM training set size and NUM incorporation of hand coded phrasal templates
a l or r following the feature name indicates that the feature must occur to the left of or to the right of the proper name s left boundary respectively
the context level for this example is NUM meaning that the feature in question must occur within the region starting NUM words to the left of and ending NUM words to the right of the proper name s left boundary
this differs from other approaches which attempt to achieve this task by NUM hand coded heuristics NUM list based matching schemes NUM human generated knowledge bases and NUM combinations thereof
this is not at all surprising as their advocates pay almost no attention to the text level of linguistic description with the exception of several forms of anaphora and also do not seriously take conceptual criteria beyond semantic features into account
given these basic relations we may formulate the composite relation s table NUM it summarizes the criteria for the ordering of the items on the cf x and y denote lexical heads
among the NUM cases where the ellipsis handler started processing NUM were correctly analyzed recall rate of NUM NUM NUM modelling bugs were encountered in the knowledge base and one case was due to incorrect conceptual constraints
thus in the path finding mode paths from clock miiz pair the conceptual representation for taktfrequenz to sponding to the searchtextellipsisantecedent message and of being tested as to whether they fulfill the required criteria for an elliptical relation
in phase NUM the message is forwarded from its initiator ladezeit to the forward looking centers of the previous sentence an acquaintance of that sentence s punctation mark where its state is set to phase NUM
with respect to accuracy however we still have to consider the actual number of textual ellipses processed including false positives i.e. cases where the for text ellipsis resolution ellipsis resolution is carried out although no textual elm lipsis actually occurs
at the beginning the set of variable bindings is empty
cases of lacking concept specifications half of which were gaps that can easily be closed the other half constituted by soft concepts e.g. referring to spatial knowledge which are hard to get hold of
this is simply due to the fact that the semantic interpretation of a phrase like the charge time of the accumulator already leads to the creation of the pof type relation the resolution mechanism for textual ellipsis is supposed to determine
is the probability that the word wi appears given that wl w2 wi appeared previously
extensions of context unification may be useful for our applications
however a sentence can be used as several speech acts depending on the context of the sentence
that is the speaker utters a sentence which most well expresses his her intention speech act
equation NUM represents the approximated contextual probability in terms of hierarchical recency in the case of using trigram
if a subdialogue is initiated a dialogue transition network is initiated and a current state is pushed on the stack
nevertheless it remains to be seen how far the system can be advanced with the use of an optimized theorem prover
in utterance NUM the speaker asks for the type of rooms without responding to b s ques null NUM
since b asks for the type of rooms push operation occurs and a ri diagram is initiated
for example let us consider dialogue NUM figure NUM shows the transitions with the dialogue NUM
we use the dialogue transition networks dtn and a stack for maintaining the dialogue state efficiently
we have specified a set called lexicon via a disjunctive specification of lexical labels e.g.
currently we are investigating the use stochastic taggers and local grammars for determining syntactic information in these cases
the language module can in principle perform several types of post processing on the wordcandidate lists that the recogniser outputs for the different word positions
the problem addressed in this research is that of improving the performance of a natural language recogniser such as a recognition system for handwritten or spoken language
since natural language abounds in contextual information it is reasonable to utilise this in improving the performance of the recogniser by disambiguating among the word choices
the definition of mis implicitly incorporates the size of the corpus since it has two p0 terms in the denominator and only one in the numerator
lexical or associative context is characterised by rigid word order and usually implies that the primer and the primed together act as one lexical unit
however to our knowledge all attempts at arriving at a theoretical basis for formalising the intuitive notion of context have treated the word and its context symmetrically
maximum co occurrence frequency in the corpus and appears to be a better normalisation factor than the size of the corpus itself
the preliminary results described in this work establish clearly that non standard metrics of lexical direction have been selected randomly from the test set
it is noteworthy that all the three dim s capture the notions of lexical ie fixed and lexico semantic associations in one formula albeit to differing degrees of success
researchers in information theory have come up with many inter related formalisations of the ideas of context and contextual influence such as mutual information and joint entropy
the lexicon is derived from the wordnet database and additionally includes frequency information
for example once the first icon is hit e.g. mask lights will appear on icons that lead to a word e.g. all icons that complete a valid feeling word will be lit
because the videotaped sessions contain both input and its expansion these are being used primarily as a means for tuning the expansion rules used in the grammar and the appropriateness heuristics that order the expansions produced by the system
null over the past NUM years the applied science and engineering laboratories asel at the university of delaware and the dupont hospital for children has been involved with applying natural language processing nlp technologies to the field of aac
over the past NUM years the applied science and engineering laboratories asel at the university of delaware and the dupont hospital for children has been involved with applying natural language processing nlp technologies to the field of aac
for instance it may be the case that selecting from a set of expanded sentences may prove very difficult for this population who may become confused or may be unable to retain their desired sentence when given a list of sentences to choose from
wife watered therefore its processing ends here
it tries to analyze the input sentence
this causes a combinatorial explosion of mostly incorrect results
noun animate genitive or accusative sing
there are two ways how to solve this problem
the fourth step constructs a collocation by arranging the strings in accordance with the word order in the sentences retrieved in the first step
the method is practical because it uses plain texts without any information dependent on a language such as lexical knowledge and parts of speech
in the experiment NUM strings are retrieved as appropriate units of collocations and NUM combinations of units are retrieved as appropriate collocations
in this paper we described a robust and practical method for retrieving collocations by the co occurrence of strings and word order constraints
stri f req strk stri the third step reorganizes the strings to be optimum units in the specific context
although evaluation of retrieval systems is usually performed with precision and recall we can not examine recall rate in the experiment
discarding rear portions of a text turns out to be more effective for large texts NUM NUM than for short texts NUM l NUM
log log df tdf is the number of segments which have an occurrence of t df is the total number of segments in d log df is a normalization factor
a node labeled with NUM represents a nucleus of the text and a node labeled with NUM an adjunct to the nucleus
but the problem with using text categorization for topic identification is that categories are arbitrarily given by humans with no regard for documents that are to be classified
the figure next to each break even point indicates the improvement or drop as compared to a topic identification task using full texts
document retrieval and message routing and information extraction functions to text handling applications
then the process of estimating consists in computing the likelihood value c i d for each c in s d such that s d c w d
societd general a major french bank disclosed on the 15th a plan to open a resident office in kiev shown in fig NUM is a sample news article from the corpus we used
there are four components detection extraction annotation and document management
the high level architecture is described in the architecture design document
selected requirements in these areas may be part of tipster phase iii as the architecture is expanded
the tipster architecture is a software architecture for providing document detection i.e.
the document management component handles the document storage and archive
the third message in the list contains a derivation of the key word
this work has been supported by the project a8 of the sfb NUM of the deutsche forschungsgemeinschaft
in umrs this is modified by expressing the scoping possibilities directly as disiunctions
its content in terms of semantic prcdicates is handled differently
for each plugging there is a corresponding drs
this pointer is semantic and not morphological in nature because different morphological realizations can be used to denote derivations from different meanings of the same lemma e.g.
let s consider an example dialogue
table NUM shows the distribution of speech acts in this dialogue corpus
the word forms and lemmas are looked up in the message index
table NUM the distribution of new words terminolo
results were compared to the union set of three experimenters
those accepted phrases as well as terminologies words compose the final terminology dictionary
for the moment a common comparator set is defined for each attribute we might wish to describe there may be some scope for interesting generalisations later
a corpus analysis has been conducted to identify how comparisons are used in encyclopeedia articles so that these techniques may be built into the peba ii system
well misunderstand the gulfinkel worrow relation above and interpret it in the opposite manner from what the requirements analyst who devised the model intended
in this paper we concentrate only the behavior of the lasie system with regards to recognizing and classifying expressions in the first four classes i.e. those which consist entirely or in part of proper names though nothing hangs on omitting the others
there are NUM such heuristic ruh s for matchlug organization names i tmuristi s for NUM ns n names m t NUM rules lbr h al ion names
thus the ontribution of difl ercnt system coillt oilenl s vm ies fl om one lass of t rot r name l o anol hcr
rain ailtl nmnc2 if name2 is consists if an initial sllbst lllclt of th words in namel then name2 matctms namel t g
the rule 0rgan hip names np names np means that if an as yet unclassified or ambiguous proper name nanes p is followed by and another mnbiguous proper nmne then it is an organization name
so for example marks spell null n and american telct hone le gratth will tie lassilicd as rganisat i m names by this rule
NUM NUM NUM proper name coreference orcfcr mcc rcsolul ion for i ropcr names is carried out in or let to rtx ognis alternativ forms specially of otganisation names
for person names location names and time expressions the results are shown in tables NUM NUM figure NUM shows graphically how the system components contribute for each of the four different classes of proper names as well as for all classes combined
if we use robotag with our hand coded rules for dates and number the overall f measure on the muc NUM english task is NUM NUM
similarly for end tags the tuple is labeled true if the token is the last token in a training tag and false otherwise
in some extreme cases this can lead to decision trees that never predict a tag begin or end no matter what the input
our experimental results with trigram showed that the proposed model achieved NUM NUM accuracy for the top candidate and NUM NUM for the top four candidates although the size of the training corpus is relatively small
there is a well defined interface to the server so it can act as a learning engine for other text handling applications as well
however any other logical language can be used in principle so long as we can represent its syntax in terms of finite trees
it is important to keep our semantic representation language hol clearly separate from our description language context constraints over finite trees
so far our treatment of ellipsis does not capture strict sloppy ambiguities if that ambiguity is not postulated for the source clause of the ellipsis construction
this process continues until a dialogue is finished
this variable binding is applied to the remaining constraint where x8 is substituted by s c j
in such a case one can either apply a projection rule that binds c to the identity context ay y or an imitation rule
an equality up to constraint has the form x1 x y1 y and is interpreted with respect to the equality up to relation on finite trees
NUM two european languages are spoken by many linguists and two asian ones are spoken by many linguists too
in the case of performative verbs ex
also request confirm and ask if are probable candidates
because it was important to facilitate interaction between the user and the learning system it was essential to show learned results rapidly
for each tag learning task robotag builds two decision trees one to predict begin tags and one to predict end tags
robotag was trained with NUM training texts and proceeded to automatically tag the NUM blind test texts
pr siw ri pr s lw NUM i NUM so argmaxw pr w pr slw n argmaxw l ipr wilwi l pr silwi NUM i NUM
pr i llj pr ins ti pr ilj max pr ilj NUM pr del sj lsj NUM pr i l j NUM pr t lsj
in step NUM the system re ranks the words in the candidate list using channel probabilities as described above
pr ylx num sub x y num x NUM pr del x num del x num x NUM pr ins y num ins y num kall letters NUM
thus for example if the system encounters the word mobiledata a correct name in the ocr output but does not have mobiledata in its lexicon it might change mobiledata to mobilecomm a word that does exist in the training corpus lexicon
once potential verbs and sfs are identifled a final com null portent attempts to determine when a lexical form occurs with a cue often enough so that it is unlikely to be due to errors an automatically computed error rate is used to filter out potentially erroneous cues
this component makes use of shallow parsing tte hn ques to detect possible prepositional sf ructures a standard cfg parser is used with a hand written grammar d qn ng pairs of main and subordinate clauses in correlative constructs such as 4d
yet another type of error stems from pronominal adverbs which are conjunction adverb homographs or which are used anaphorically while the verb in the main clause subcategorizes for a daj that clause so the sentence is erroneously considered to be a correlative construct
this method makes use of a stochastic tagger to determine part of speech and a finite state parser which r m on the output of the ta er identifying auxiliary sequences noting putative complements after verbs and collecting histogram type frequencies of possible sfs
a problem in the current version of the system was the fact that segmentation of nominal constituents was not optimally handled by the detection component leading to a large mlmher of verbal frames with correct prepositions but with an additional erroneous accusative dative nc in the frame
a copula clause with two nominal constituents nct nl and nc2 n2 such that nc2 follows nc1 and n2 is a noun is mapped to lcb pp p n n2 rcb
this rate is a lower bound for the actual precision rate of the system since it does not take the fact into account that the system did learn true sfs not listed in the published dictionary so the precision rate of the system is actually higher
for instance the occurrence of the pronominal adverb damn in the correlative construct in 4d can be used to infer that the verb denken to think subcategorizes for a pp headed by the preposition an about
we then present experimental results which compare robotag to both human tagged keys and to the best hand coded rule systems
this knowledge is used to construct a tagging procedure that can find additional previously unseen examples for extraction
the preprocessor produces output in a well defined format across languages which the server uses in carrying out the learning
it is also less general to rely on hand coded rules for a significant part of the tagging task
for instance the discourse la has lb as a semantic representation where the value of r is given by equation lc with solutions ld and le
finally all other terms are taken to 5we abbreviate exp x cl y blt i to ex x y i to increase legibility
fortunately in our case we are not interested in general unification but we can use the fact that our formulae belong to very restricted syntactic subclasses for which much better results are known
the semantics of these ground color formula is that of propositional logic where d is taken to be equivalent to the disjunction of all other color constants
in this section we show that the restriction which was originally proposed by dsp to model vp ellipsis is in fact a very general constraint which far from being idiosyncratic applies to many different phenomena
neither the notion of direct association nor that of parallelism is given a formal definition but given an intuitive understanding of these notions a source parallel element is an element of the source i.e.
the motivation behind this layout is that in most eases syntaclic ambiguity has some impact on the semantic readings NUM the construction algorithm traverses the dpf and assigns to each vertex the argument list associated with its category in the semantic grammar
context sets at he edges st arl ing at v a predicate match1 matches arguments proper as given in the lexical e ntries and the starl symbol de laratkm onto tim lions as used in the rules
let d1 be a context set lcb dl dn rcb let lexarg be an argument as provided l y a lexicm item or startsymbol declaration i let art be mt argument as occurring attached to a nonterminal on the righthand side of a grammar rule
if an argument originates in an item becmlsc it is e.g. its instance discourse referent or label then the value of this argument is unambigous for the item NUM in adjunct on structures the modified constituent assigns and the modifier receives the shared discourse referent
i detection encompasses the technology which does document retrieval and document or message routing
the tipster architecture is intended to facilitate the deployment into the workplace of advanced document detection and information extraction software
the tipster architecture is explained in more detail in the tipster text phase ii architecture concept in this volume
it aims at making the possibly ambiguous semantics captured by a lud unique
this however results in structures which can not be fltrther resolved
each syntactic rule gets annotated with a semantic counterpart
finally section NUM sums up the previous discussion
the bci does translation at the level of quasi
when a noun has the feature ct its denotation is the set whose sole member is the greatest aggregate of which the noun is true
but many do for example joy embarrassment delight sorrow disappointment anxiety dislike love like and care
for example two drinks is acceptable since drink is a count noun but two milks is not since milk is a mass noun
the first set of facts had been pointed out by quine even before the criterion itself had been first suggested let alone come into vogue
thus this desk that oil and those odds are acceptable whereas these desk that oils and thai odds are not
in other words it is the largest subset of the domain of discourse such that the noun is true of each element in the set
summarizing then we conclude that mass nouns under conversion give rise to count nouns with a limited variety of shifts in denotation
it is not surprising then that these different kinds of noun phrases are sensitive to the features t pl in different ways
second if the cardinality of a plural noun phrase were required to be greater than one then the following sentences would not be true
the model does not distinguish between complements and adjunct prepositional cues
NUM weft dies ein hinweis darauf ist da6
the inheritance hierarchy is used to be able to define any values in a simple way
in example a the application of one rule does not make the other inapplicable
allowing multiple extensions gives a higher computational complexity than allowing only theories with one extension
based on these observations a sufficient condition for w explanation is defined as follows
with the generalized versions of the definitions explanations that simultaneously explain all substructures of a nonmonotonic sort will be considered
in this paper i have proposed a method allowing the user to define nonmonotonic operations in a unification based grammar formalism
in this paper the notation a u b is used whenever a subsumes b i.e.
nonmon name parameter1 parametern when a NUM
to be able to define these operations i assume the inheritance hierarchy above without the nonmonotonic definition of any
in example b however the application of one of the rules would block the application of the other
the basic assumption of our approach is that a majority of transcription errors can be attributed to either some inherent limitations of the language model employed by a speech recognition system or else to the specific speech patterns of a speaker or a group of speakers
there can be multiple servers running on the same machine each independently handling a single client s tasks
robotag learns decision tree classifiers that predict where tags of each type should begin and end in the text
the decision trees can refer to classes of words by their lexicon features but not individual words themselves
unlike some of the name tagging systems robotag is being compared to robotag has no alias generation facility
4the f measure formula they report seems to be in error and they reported with a fl of NUM NUM
these somewhat more powerful formalisms appear to be adequate for some phenomena such as extraction out of adjuncts recall ss2 and certain kinds of scrambling which our current method does not handle
for any schema s in which specified sfs of n are reduced try to instantiate s with n corresponding to the sd of s add another node m dominating the root node of the instantiated schema
as mentioned this anchor is not always equivalent to the hpsg notion of a head in a tree projected from a modifier for example a non head adjunct dtr counts as a functor
also as the algorithm does not currently include any downward expansion from complement nodes on the frontier the resulting trees will sometimes be more fractioned than if they had been specified directly in a tag
of course such nodes would still unify with the sd of the filler head schema which reduces slash but applying this schema could lead to an infinite recursion
therefore head features and other local features can not in general be raised across domination links and we assume for now that only the sfs are raised
our objectives are met by giving clear definitions that determine the projection of structures from the lexicon and identify maximal projections auxiliary trees and foot nodes
as the target of our translation we assume a lexicalized tree adjoining grammar ltag in which every elementary tree is anchored by a lexical item saj88
finally for the filler headschema the head dtr is the sd as it selects the filler dtr by its slash value which is bound off not inherited by the mother and therefore reduced
this compilation strategy illustrates how linguistic theories other than those previously explored within the tag formalism can be instantiated in tag allowing the association of structures with an enlarged domain of locality with lexical items
positive experience in phase one with japanese led the government and contractors to downplay the port to chinese as a risk factor
preliminary experiments with context free rules have already shown interesting results we noticed that the average word error rate decreased from NUM NUM to NUM NUM a NUM reduction on a test sample after running it through a c box equipped with only a few cf rules
the authors would like to thank all members of the hitecc ims project at ge cr d gems sms scra ummc advanced radiology and camc for their invaluable help particularly glenn fields skip crane steve fritz and scott cheney
pronominal and nominal anaphors i the antecedent c to which an anaphor r refers must not be governed by the same head which d binds 7r unless ii applies
in phase NUM the message is forwarded from its initiator to the word actor which d binds the initiator
for pronominal anaphors the search for the antecedent is triggered by the occurrence of a personal pronoun
NUM die frage konnte eri noch nicht beantworten ob peteri nach dublin fahren sollte
the question hei could n t decide whether peteri should go to dublin
NUM die frage ob peteri nach dublin fahren sollte konnte eri noch nicht beantworten
the question whether peteri should go to dublin hei could n t decide
we provide a unified account of sentence level and text level anaphora within the framework of a dependency based grammar model
in NUM the subject maria is d bound by the same head as the reflexive sich
in this case the antecedent of a pronoun may be governed by the head which d binds the pronoun
we were able to achieve good recognition performance for vocabularies of up to NUM words using this technique
the main intent of our system is to achieve a morphological ambiguity reduction in the text by choosing for a given ambiguous token a subset of its zwith a slightly different but nevertheless common glossing convention
b we also generate any new unambiguous contexts that this newly disambiguated token may give rise to and add it to the incontext table along with count NUM
it is certainly possible that a given token may have multiple correct parses usually with the same inflectional features or with inflectional features not ruled out by the syntactic context
although the results obtained there were reasonable the fact that all constraint rules were hand crafted posed a rather serious impediment to the generality and improvement of the system
the columns labeled NUM NUM NUM and NUM denote the number and percentage of sentences that have NUM NUM NUM and NUM tokens with all remaining parses incorrect
the columns labeled ua c and a c give the number and percentage of the sentences that are correctly disambiguated with one parse per token and with more than one parse for at least one token respectively
the unambiguous token in the right context is also entered to the count table once with its top level feature structure and once with the feature structure of the stem
using a second set of templates which are more specific than the templates used during the learning of the choose rules we introduce features we were originally projected out
table NUM shows the precision and recall scores using the model theoretic measures for al l of the examples given in sundheim et al
we represent a sentence as the concatenation of the stress sequences of its constituent words with p symbols for the n2 experiments breaking the stream where natural pauses occur
of the NUM NUM words in the dictionary NUM have more than one pronunciation of these NUM have more than one distinct stress pattern of these NUM have different primary stress placements
but we claim that our results predict how effective prediction would be for the small state space in our markov model and the huge amount of training data translate to very good state coverage
as figure NUM demonstrates an n gram model is simply a stationary markov chain of order k n NUM or equivalently a first order markov chain whose states are labeled with tuples from ek
traditional approaches to lexicai language modeling provide insight on our analogous problem in which the input is a stream of syllables rather than words and the values are drawn from a vocabulary n of stress levels
sentences with perplexity greater than NUM which numbered roughly NUM thousand out of NUM NUM million were discarded from all experiments as NUM unit bins at that level captured too little data for statistical significance
commas dashes semicolons colons ellipses and all sentenceterminating punctuation in the text which were removed in the e1 tests were mapped to a single pause symbol for e
we have quantified lexical stress regularity measured it in a large sample of written english prose and shown there to be a significant contribution from word order that increases with lexical perplexity
in language modeling unseen words and unseen n grams are a serious problem and are typically combatted with smoothing techniques such as the backoff model and the discounting formula offered by good and turing
frequency counts from text corpora serve as a guideline for the inclusion of lemmas
sampling ratio creating one tuple from each token in a text leads to many more negative training examples than positive since only the tokens at the beginning or end of a tag generate positive training tuples
naturally the semantic fields are closely related to major nodes in the semantic network
by generating an alias from a recognized name a system can scan for that alias e.g. a company s acronym or an individual s first name in order to improve the likelihood of identifying it
the paragraphs that follow describe the common interface to these two components
for each tag type the table gives the total number of tags of that type present in the training and testing sets and the recall precision and f measure NUM as measured on the test set
depending on the severity of fragmentation changes in the parsing strategies which drive the text analysis might also be reasonable
in contrast the proposed model model ii achieved NUM NUM accuracy for the top candidate and NUM NUM for the top four candidates
our goal in developing robotag was to make it possible for an end user to build a tagging system simply by giving examples of what should be tagged rather than requiring the user to understand a pattern language
in section NUM the proposed treatment is compared to related approaches in computational semantics
in this work positive examples are the stem variant pairs subordinating to current stein changing rule
preferred is the direction in case of which the cover extent of the discriminative conjunctions are higher
this model accounts for sentence level anaphora with constraints adapted from gb as well as text level anaphora with concepts close to grosz sidner style focus models
if n is fully countable then the classifier will not be translated individuate otherwise the classifier is translated part
this process can be automated however the challenge is to devise more precise automatic means of paragraph picking
voorhees hou NUM voorhees NUM note that again long text queries benefit more from linguistic processing
a more accurate but arguably more expensive method would be to use a substring comparison procedure to recognize variants before matching
to facilitate passage spotting we used simple word search using key concepts from the query to scan down document text
the second point is to actually decide whether to include a given passage or a portion thereof in the query
it provides a convenient testbed to experiment with algorithms designed to merge the results obtained using different ir engines and or techniques
currently the state of the art statistical and probabilistic ir system perform at about NUM NUM precision range for arbitrary ad hoc retrieval tasks
stream indexes are built using a mixture of different indexing approaches term extracting and weighting strategies even different search engines
i have extended the system to also handle yes no questions involving the question morpheme mi which is placed next to whatever element is being questioned in the sentence
for example 5when to open causes often open
additionally germanet makes use of systematic crossclassification null
at this point it is particularly important for a developer or someone who communicates well with developers to become intimately familiar with the tasks of a number of potential user groups
on the part of the project leader frequently the tipster representative this requires including in the process from the beginning all those people who will be affected by the application
dariaberhinmls ist die ladezeit mit NUM NUM smnden sehr kurz
der NUM wird mit einem nicke l metatt llydride akku bestfickt
it integrates language independent conceptual criteria and language dependent functional constraints
table NUM path labels ordered by concepttml strength
table NUM path lists compared by conceptual strength
table NUM functional ranking constraints on the cf
this work has been funded by lgfg baden wiirttemberg m
every other token forms a negative example a place where a tag did not begin or end
we find that with a limited amount of data less than NUM words for the training set a small sliding context window NUM tokens and only two hidden units the neural net performs extremely well on this task less than NUM error rate and f score combined precision and recall of over NUM on unseen data
pos and trigger encoding w NUM h NUM NUM NUM NUM as for the output layer in all the experiments it was fixed to a single output unit which indicates the presence or absence of a segment boundary just before the word currently at the middle of the window
the input to their network is a window of words centered around a period where each word is encoded as a vector of NUM reals NUM values corresponding to the word s probabilistic membership to each of NUM classes and NUM values representing whether the word is capitalized and whether it follows a punctuation mark
we created two data sets for our experiments all from randomly chosen turns from the original data i the small data set a NUM NUM NUM split between training validation and test sets and ii the large data set a NUM NUM NUM split
of the harmful errors three were due to the word and being used as a conjunction in a non clausal context two were due to a failure to detect a speech repair and one was due to an embedded relative clause most people that move into nursing homes die very quickly
reason context false positive no trigger word false positive yes non clausal and false negative yes speech repair false positive
fewer hidden units yield better results generally we get the best results for just two hidden units
table NUM shows NUM representative errors that one of the best performing neural network made on the test set
there are different understandings of the definition of a word
indexing each chinese character as a term ensures that no information is lost
the university of massachusetts ported their inquery system with the development of hanquery
information retrieval in a foreign language requires modification to text and user interfaces
how many words do we have here one two or three
the stems undergo changes in vowels or doubling of consonants
begin technology development only after the support infrastructure is identified
thus the pointer can be conceived of as an implementation of a simple default via which the net can account for language productivity and regularity in an effective manner
in the first sentence of NUM the searchanteeedent message is caused by the occurrence of the personal ihn cf
the probability of each possible adjacent word p wi is then y eq wi p wi freq str NUM at that time the entropy of str h str is defined as
this is based on the assumption that most of the lexical relations involving a word w can be retrieved by examining the neighborhood of w wherever it occurs within a span of five NUM and NUM around w words
in contrast to these methods our method focuses on the distribution of adjacent words or characters when retrieving units of collocation and the co occurrence frequencies and word order between a key string and other strings when retrieving collocations
the precision is NUM NUM when the number of meaningful collocation is divided by the number of the key strings and NUM NUM when it is divided by the number of the collocations retrieved in the second stage NUM
the table shows that the ungrammatical strings such as for more information on and for more information refer to act more cohesively than the grammatical string for more information in the corpus
taking the example mentioned above the words which follow local area are practically identified as network because local area is a substring of local area network in the corpus
str h str n fveq str for more NUM NUM NUM NUM for more information NUM NUM NUM NUM for more information NUM NUM NUM NUM for more information see NUM NUM NUM NUM for more information refer to NUM NUM NUM NUM for more information on NUM NUM NUM NUM for more information about NUM NUM NUM NUM
to filter out the fragments we measure the distribution of adjacent words preceding and following 1a word is recognized as a minimum unit in such a language as english where writespace is used to delimit words while a character is recognized as that in such languages as japanese and chinese which have no word delimiters
an example for the hierarchical structure is given in figure NUM without selectional restrictions for matters of simplicity where heraus is a hyponym of her and aus
two basic types of relations can be distinguished lexlcal relations which hold between different lexical realizations of concepts and conceptual relations which hold between different concepts in all their particular realizations
in addition bankl may have hyponyms such as credit union agent bank commercial bank full service bank which do not share the regular polysemy of bank1 and banks
germanet shares the basic database division into the four word classes noun adjective verb and adverb with wordnet although adverbs are not implemented in the current working phase
therefore it is not possible to merge such a type of polysemy into one concept and use cross classification to point to both institution and buil ng as in figure NUM
ga rd a logi al interprelqal ion a s a possibh
therefore we directly represent rules in the hierarchy in prioritized circumsccil tion
moreover this prioritization is general since we can repr sent a various kind of priority besides specilicity
null we believe that circumscritttion has the following allvantage s in the ask of resolving ambiguity
it is important to retain NUM ossible readings if we can not w solve aml iguity yet
for instance one sees the form carter a where the last vowel in carter is pronounced so that it harmonizes with a in turkish while the e in the surface form does not harmonize with a
however since fail is allowed as a valid explanation for a nonmonotonic sort there is as for normal default logic always at least one explanation for a sort
if we assume that v represents the information already given this means that the default rule can be applied whenever y c a and y i i does not yield unification failure
definition NUM the nonmonotonic unification n v of two nonmonotonic sorts sl a1 and s2 a2 is the sort s a where
this means that only nonmonotonic rules whose first component is w are considered and that it is possible to choose which nonmonotonic rules should be applied in a particular point at some computation
one difference is that nonmonotonic sorts allow that the application of a nonmonotonic rule leads to fail i.e. an inconsistent structure while default logic does not allow this outcome
this implies that the only case where this rule would not apply and thus not give fail as a result is when the value of form actually is passive
the next two conditions in the definition a s and fl s guarantee that the default rule is or can be applicable to the nonmonotonic sort
note that even though there is an order dependency on the application order of nonmonotonic rules this does not affect the order independency on nonmonotonic unification between application of nonmonotonic rules
i will here assume that a representation for verbs where passive verbs have the value passive for the attribute form but where other verbs have no value for this attribute
in the approach described in this paper the user is allowed to define the actual nonmonotonic rule that should be used for a particular operation by using the following syntax
we divide classifiers into four major types iin t section NUM NUM metric section NUM NUM group section NUM NUM and species section NUM NUM
this paper describes an automatic context sensitive word error correction system based on statistical language modeling slm as applied to optical character recognition ocr postprocessing
consequently in each lexicon entry the following information is stored syntactic category of word which is used for morphological analysis and semantic links
continuously taming the parallel activities of the parser and furthermore sacrificing highly esteemed theoretical principles such as the completeness of the parser i.e. the guaranteed production of all analyses for a given input led us to determine those critical portions of the parsing process which can reasonably be pursued in a parallel manner and thus give real benefits in terms of efficiency and effectiveness
the performsearchhead message triggers via performsearchhead 2o messages asynchronous searchheadfor messages to be forwarded by each receiving p h r a s e ac t o r to its right most we rdact o r
fig NUM x hence the incompleteness property of our parser stems firom the selective storage of analyses i.e. an incomplete chart in chart terms partially compensated by reanalysis
table NUM group and species classifiers
in the first pass the system has no information on the character confusion probabilities so it will assume a prior belief o as the probability that a character is correctly recognized
a ranked list can be generated by scoring matches using a simple term frequency tf count the number of matches between the query vector and the n gram vector of a candidate word
NUM context dependent non and real word error correction the system treated all input strings as possible errors and tried to correct them by taking into account the contexts in which the strings appeared
instead the system operates in two steps first to generate the candidates and then to specify the maximal number of candidates n to be considered for the correction of an ocr string
under such circumstances a dynamic programming method can be used to determine the operations that maximize the conditional probability when transforming the original word to the ocr string given a character confusion probability table
here property p is meant to be the property identifying a relevant intervener for the relation meant to hold between x and y
in particular without this lexicon would have the additional arguments sees v john n mary and all free variables appearing in the other definitions
as discussed before automata are a way to represent even infinite numbers of valuations with finite means while still allowing for the efficient extraction of individual valuations
these words are called new words
table NUM the distribution of terminology
most of them have s in the universal dictionary
the main knowledge base of segmentation is the dictionary
keyword chi square test automatic indexing mutual information
most terminologies do n t have entries in universal dictionaries
table NUM presents the vocabulary distribution of cw corpus
similar results were obtained from tri gram and NUM gram candidates
one is a computer world corpus cw
table NUM contingency table of characters a and b
it uses a greedy algorithm to generate and apply rules incrementally refining the target concept
knowing the strings of words to which these two tunes are to be assigned however does not provide enough information to determine the location of the pitch accents h and l h within the tunes
moreover unlike systems that rely solely on word class and given new distinctions for determining accentual patterns the system is able to produce contrastive accents on pronouns despite their given status as shown in NUM
rheme s acu pres indei el amplifier cl tube el isa el el np a s elerh
note that it is in precisely those cases where thematic material which is given by default does not contrast with any other previously established properties or entities that this material is intonationally unmarked as in NUM
additionally note that the propositions praise e4 el and revile e5 el are combined into the larger proposition contrast praise e4 el revile e5 e i
by these similarities we can construct a large and general thesaurus
because the similarity in bunrui goi hyou is rough multiple answers may arise
comparisons are typically employed to distinguish similar entities or to illustrate a property of an entity by referring to another com monly known entity which shares that property
based on an analysis of a corpus of encyclopedia texts we define three types of comparisons and outline some strategies for applying these in the generation of entity descriptions
these are cases where one or more attributes of an entity being described are compared to those of a common object with which the reader is assumed to be familiar
a clarificatory comparison is a comparison whose purpose is to describe an entity by distinguishing it clearly from another entity with which it might be confused or with which it shares a number of salient properties
instead of describing the size and proportion of the viscacha s ears in absolute terms a reference to the rabbit s ears makes it easier for the reader to understand what the ears really look like
more interesting from the point of view of text generation are clarificatory and illustrative comparisons here the entity being described by the system is described in relation to some other entity chosen by the system
similar to goats sheep differ in their stockier bodies the presence of scent glands in face and hind feet and the absence of beards in the males
an illustrative comparison is a comparison whose purpose is to describe one or more attributes of an entity by referring to the same attribute s of another entity with which the user is familiar
in this text the focused entity the sheep is very similar and might often be confused with the comparator entity the goat this is particularly true of some wild sheep
the inductive nature of the proof allows us a fairly free choice of signature as long as our atomic relations are recognizable
at the moment at least the question of which grammatical properties can be compiled in a reasonable time is largely empirical
the use of the memt architecture allows the improvement of initial mt engines and the NUM morphological analysis part of speech tagging and possibly other text enhancements can be shared by the engines
the goal of our lexical modeling approach is to create an acceptable quality pronouncing dictionary that can be variously used for acoustic training decoding and synthesis
we have carried out initial testing using local naive subjects e.g. drama majors and construction workers and intend to test with actual end users once specific ones are identified
while the effectiveness of this approach depends on the quality and quantity of the text sample that can be obtained we believe it produces appropriate data for our modeling purposes
this use of subword concatenation is especially important since it is the only currently available method for rapidly bringing up synthesis for a new language
if parallel corpora are available for a new language pair the ebmt engine can provide translations for a new language in a matter of hours
of course such overlaps can not be relied upon and in any case will not produce recognition performance that approaches that possible with appropriate training
we attempt to achieve this primarily through user interaction wherever feasible the user is presented with intermediate results and allowed to correct them
editing the backtranslation allows the interviewer to recognize correct forward translations despite errors in the backtranslation if the backtranslation can be edited into correctness the forward translation was probably correct
as we have done with previous interface designs we will carry out user tests early in its development to ascertain whether our intuitions on the usability of this version are correct
in this evaluation the short queries are one sentence search directives such as the following what steps are being taken by governmental or even private entities world wide to stop the smuggling of aliens
others are sorted according to their correlation coefficient in descending order
the 100m bytes corpus contains more than 40m chinese characters
this note describes a scoring scheme for the coreference task in muc6
working through the arithmetic we have
one attraction of monadic second order tree logics is that they give us a principled means of generating automata from a constraint based theory
assume an alphabet e e0 lj e2 with eo lcb a rcb and e2 being a set of binary operation symbols
notational conventions will be that constraints associated with clauses are written in curly brackets and subgoals in the body are separated by s
as it is mentioned the stem variants can be bounded by more than one stem change
NUM of the errors were errors we considered to be harmful to the parser NUM were errors of unknown harmfulness and the remaining NUM were considered harmless
an intuitive reading of a general default rule is if a is believed and it is consistent to believe then believe NUM in default logic this is usually expressed as the next question is how such defined nonmonotonic rules are going to be interpreted in a unification framework
as seen by the definition a nonmonotonic sort is considered to be a pair of monotonic information from the subsumption order and nonmonotonic information represented as a set of nonmonotonic rules the user can assign nonmonotonic information to a nonmonotonic sort by calling a nonmonotonic definition as defined in the previous section
the nonmonotonic sort NUM lcb posterior any no value fail rcb will be posterior explained to fail while the sort lex kalle lcb posterior any no value fail rcb will be posterior explained to lex kalle the last nonmonotonic operations i want to discuss are completeness and coherence as used in lfg
the first of these rules is used to check coherence and the effect is to add the value none to each attribute that has been defined to be relevant for coherence check but has not been assigned a value in the lexicon
since the definition of w applicability and the condition that NUM in all nonmonotonic rules ensures that whenever a nonmonotonic rule is applied it can never be inapplicable there is no need to check if the preconditions of earlier applied nonmonotonic rules still hold
by cross classifying between these two hierarchies the taxonomy becomes more accessible and integrates different semantic components which are essential to the meaning of the concepts
first the baseline srs system used in these experiments is much weaker only about NUM accurate
one possible way to produce higher quality rules is to specialize them by adding context
in addition a fair sized text corpus of historical fully verified transcriptions is required
at the time this paper is prepared some NUM reports have been redictated
in fact one way of proceeding is to work with shorter l rules first
we use character level alignment for support especially if the replacement scope is uncertain
can be used to validate the rule on change unchanged even though the misaligned sections are much longer here
correction rules used and trachea to endotracheal tube size factor satisfactory is and has been
we also like to thank j alcantara corneu u who kindly took the role of the native speaker via internet
whether a word pair is a phrase is measured by its weight
in order to determine suitable conceptual links between m antecedent and an elliptic expression we distinguish two modes of constraining the linkage between concepts via conceptual roles
the distinction between context bound and unbound elements is important for the ranking on the cf since bound elements are generally ranked higher than any other non anaphoric elements cf
this relation can only be made explicit if conceptual knowledge about the domain viz the relation charge time of between the concepts ciiarge timf and accumu lator is available
obviously the criterion which ranks conceptual paths according to their associated path markers is applicable as all paths in a single cp list have the same marker
in order to illustrate our approach under slightly varying conditions consider text fragment NUM NUM a der 316lt geht sparsam mit energie um
accordingly we distinguish each utterance s backwardlooking center cb u and its forward looking centers cf u
under these circumstances conceptual linkage could not be established via a plausible path but only via a metonymic path corresponding to a whole for part metonymy
more typically as in chinese different conversion tables use different character order and are not in one to one relation with each other
if used in a combination query it is possible that the results would equal or surpass the more expensive automatic segmentation performance
the input to the plum system is the text of a document from the document manager i.e. a message
these results outperform those obtained by other methods as reported in the literature
a b indicates a small clause boundary
the field of augmentative and alternative communication aac is concerned with developing methods to augment the communicative ability of such people
an alternative would be to base compatibility on the notion of consistency in the underlying logic if a complete logic has been defined
the tests in the grammar may be made on the basis of syntactic or semantic features stored in the lexicon on each word
while people exhibiting these kinds of production problems may be understandable they will often be perceived negatively in both social and educational situations
we will consider each of these predicates in greater detail in the next section when we discuss the third component of the model
misunderstandings are recognized when an utterance is inconsistent or incoherent strategies for repair suggest reanalyzing previous utterances or making the problem itself public
we have used the portability frame presented in this paper for the main tools of our system a morphological parser and a morphological generator which use a root and endings lexicon to parse or generate about NUM NUM french forms
it would nonetheless be interesting to see if the formula could be improved on especially seeing that it was theoretically derived and then directly applied to the tagging task immediately yielding the quoted results
the latter conditioning is usually on the tags of the neighbouring words and very often on the n NUM previous tags so called tag n gram statistics
it has been tested on a part of speech tagging task and outperformed deleted interpolation with context independent weights even when the latter used a globally optimal parameter setting determined a posteriori
for this reason we will estimate the probability distribution conditional on an unknown word from the statistical data available for words that end with the same sequence of letters
one could of course multiply this quantity with any reasonable real constant but we will arbitrarily set this constant to one i.e. use r ck itself
however we want the weight to have the same dimension as the statistical mean and the dimension of the variance is obviously the square of the dimension of the mean
we want to know the probabilities p t i i ll ln for the various tags ti NUM since the word is unknown this data is not available
one of two different main ideas behind these techniques is that complex contexts can be generalized and data from more general contexts can be used to improve the probability estimates for more specific contexts
while perhaps some of these changes can be responded to if the budget allows they are best tracked separately from fixes to the software
a third strategy can also reduce the risk of technology transfer projects which can be described as start small and out of the way
in addition language is a human construct and whatever its imperfections a person is i this material has been reviewed by the cia
at the same time to avoid runaway cost inflation on a project there must be clear delineation between fixes and changes at this stage
developing a product for commercial market itself takes time so that advances promoted by tipster left to their own might take longer than we wish to reach the commercial market
a number of difficulties i have observed in technology transfer projects have occurred because integration has occurred before component functions were sufficiently well researched and understood
if the preliminary work has been well done i.e. steps NUM NUM the development of an actual application should be relatively rapid given a reasonably stable environment to integrate into
these in turn can lead to systems that are not used to dissatisfied customers and to heavy reworkings of the system under the guise of o m operations and maintenance
while a great deal can and should be done to reduce the risk there is never any guarantee that such a project will succeed in all or even most of its goals
resources in this case means not only funding but also the personnel time necessary to do things such as manage the project develop requirements review interface designs and so on
the next overall category is metri classifiers a noull phrase of the form xc no n where c is a metri classifier will be translated as x c of n where n will be plural if it is headed by a fully countable or pluralia tanta noun
NUM NUM hako no pen NUM box of pen NUM boxes of pens xc no n NUM pen no hako box of pen a box of pens n no c whether a notln is a gill up classifier or not carl also be used to help determine the irtlmber of ascriptive and appositive noun phrases
the resuli ing amtlysis ix shown lcb rcb e similar t o n pr pose t tbr thai an unrelaj xl lajtguage suggesl iug thai it may i e more widely al pli al le
however there are three possible translations of a japanese noun phrase of the form xcno n where c is a unit classifier individuate translate as x n where the classifier c is not translated and the numeral directly modifies the countable english noun phrase NUM hiki no inu l piece of dog NUM dog
table NUM performance comparison to other work
in the above example d NUM is incompatible with d NUM and therefore gets entered into the table
once a procedure is defined the automated drafter takes the procedure specified with the developer s tool and produces text expressing that procedure
we will see how this is done ill the next section but the point can be appreciated immediately
the corresponding exponential blowup in the state space is the main cause of the non elementary complexity of the construction
in the clp extension the appearance in the table is not coupled to the appearance in the constraint store
is not the case that every constraint in the grammar has to be expressed in one single tree automaton
be minimized by a construction which is yet again a straightforward generalization of well known fsa techniques
however the clp extension discussed below can be used to amplify the power of the formalism where necessary
that formula was chosen because it is an extremely straightforward formalization of the prose definition of the relation
the resulting structure is an extension of the underlying constraint structure with the new relations defined via fixpoints
so the alphabet size with the encoding as bitstrings will be at least NUM ialphabet
null tarter september NUM dr vander linden s address will be department of mathematics and computer science calvin college grand rapids mi NUM usa kvlinden calvin edu
because of this and because of the potential for the user to be without a supported interface design tool like visualworks drafter provides a manual definition facility
the automated drafter this com null prises two major components the text planner or strategic planner and the tactical generator
the outer box represents the main user goal of saving a document a goal which is achieved by executing all the actions inside the box
our example involves defining the procedure for saving a new file in a microsoft word like text editor and then to generate text for that procedure
french enregistrement d un document NUM ouvrir la fen re save as file en choisissant l option save sur le menu file ou en cliquaat sur le bouton save file
NUM open the save as file window by choosing the save option on the file menu or by clicking on the save file button
attempts at rapid insertion without the needed preparation leads to many problems such as applications that are not robust poorly planned user work flows poor estimates of integration times and badly prioritized or developed requirements
NUM q i know the american amplifier produces muddy treble defines how the information conveyed by a sentence is related to the knowledge of the interlocutors and the structure of their discourse
in particular it 1in this example and throughout the remainder of the paper the intonation contour is informally noted by placing prosodic phrases in parentheses and marking pitch accented words with capital letters
finmly a small set of rhetorical predicates rearranges the linear ordering of propositions so that sets of sentences that stand in some interesting rhetorical relationship to one another will be realized together in the output
consider for example the intonational pattern in NUM in which the pitch accent on amplifier in the response can not be attributed to its being new to the discourse
while these heuristics account for a broad range of intonational possibilities they fail to account for accentual patterns that serve to contrast entities or propositions that were previously given in the discourse
the relation of entailment is kept for the case of backward presupposition
synonymy and antonymy are bidirectional lexical relations holding for all word classes
germanet lists synonyms along with each concept
on the other hand if a subdialogue is ended then a dialogue transition network is ended and a current state is popped from the stack
the relevance of context in disambiguating natural language input has been widely acknowledged in the literature
cmax a max wlw2 is defined to be the
the word choices together with their confidence values constitute a confusion set
ii NUM NUM NUM ii NUM NUM NUM NUM NUM NUM
d1 is a straightforward estimation of the conditional probability of co occurrence
the four measures including mis are defined as follows
the step functions in d2 and d3 represent two attempts at minimizing such errors
since on line frequency dictionary is not available for us another universal corpus is used
thresholds can be applied to limit this effect but a n t eliminate it
this paper is supported by chinese natural science foundation and high technology NUM project
it needs to automatically identify those words which most appropriately reflect a text s theme
word pairs would be discarded if there are no consistent syntactic relations between constituent words
we can find that the precision is significantly improved arer new words were appended
since this does not take us beyond the power of mso logic and natural language is known not to be context free the extra power of tc ws2s offers a way to get past the context free boundary
we have conducted a set of experiments to see how a full text and discard model compare in terms of the performance on the topic identification task
in the experiments we were interested in finding out the effectiveness of a segment model which considers a starting block of the article and ignores everything else
each graph corresponds to articles of a particular length the one marked with NUM NUM means that it is for articles from NUM to NUM character long
ps cld p c p tle p tld tes d p t NUM
NUM shows how the proportion of actual topics to indices that is tw d changes with the increase fig in text length
put simply the equation above says that the greater the number of indices associated with both c and d is the more likely d is to predict c
although it is quite possible to expand the notion of topic to include nouns semantically related to title terms the possibility is not explored here
NUM societg general a major french bank disclosed on the 15th a plan to open a resident office in kiev capital of ukraine
later we will be concerned with whether a particular choice of the set s d will in any way affect the performance on topic identification
indeed a new area of research known as passage retrieval has emerged to explore methods for using information from various levels of a document s structure e.g.
hence an appropriate parser can compute and cache the nf of each parse in o NUM time as it is added to the chart
structure sharing does not appear to help parses that are grouped in a parse forest have only their syntactic category in common not their meaning
return all parses from c NUM n having root category s simpler normal form parser will suffice for most grammars
given a sentence every reading that is available to the grammar has exactly one normal form parse no matter how many parses it has in toto
such a parser repeatedly decides whether two adjacent constituents such as s np and i p n should be combined into a larger constituent such as s n
it can be shown that when the restriction NUM is used together with NUM the system again finds exactly one NUM
in the sort of restricted grammar where theorem NUM does not obtain can we still find one possibly non nf parse per equivalence class
moreover the simple mapping described above does not account for the frequently occurring cases in which thematic material bears no pitch accents and is consequently unmarked intonationally
h ll for the determination of pitch accent placement we rely on a secondary tier of information structure which identifies focused properties within themes and rhemes
then for each property of x in turn the rset is restricted to include only those objects satisfying the given property in the knowledge base
the system exhibits the ability to intonationally contrast alternative entities and properties that have been explicitly evoked in the discourse even when they occur with several intervening sentences
the second step in the focus assignment algorithm checks for the presence of contrasting propositions in the isstore a structure that stores a history of information structure representations
the content generator starts with a simple description template that specifies that an object is to be explicitly identified or defined before other propositions concerning it are put forth
after applying the second step on the focus assignment algorithm contrasting discourse entities are marked with the contrastive focus operator as shown in NUM
propositions are considered contrastive if they contain two contrasting pairs of discourse entities or if they contain one contrasting pair of discourse entities as well as contrasting functors
rset z s the set of alternatives for object z as restricted by the referring expressions in delist and the set of properties s
for the projection solution we instantiate z z for x and obtain kz z ac which f reduces to ac
the lasie parser is a simple bottom up chart parser iinplemented in prolog
parsing takes place in two passes each using a separate grammar
all the rules were produ ed NUM y hand
table NUM overall precision and recall scores
table NUM module contrilmtions for person names
table NUM module contributions for org names
iml lement v NUM vl tttach pl a ol jeet nl n2 r possessor nl NUM and not implen ent v n2 np tttach pp default vi attach pp
government owned software covers a broad spectrum of readiness and robustness
these selectional properties will be generated automatically by clustering methods once a sense tagged corpus with germanet classes is available
an example for meronymy is arm arm standing in the meronymy relation to ksrper body
holds isa e7 watts per channel
that is content bearing words e.g.
performance issues NUM NUM NUM can be evaluated by observation of the system for failures for the speed of various tasks and by comparison of human generated output with machine output
any automated processing of language can not be really evaluated outside the context of the actions of expressing and understanding both actions which are highly situation or task dependent
even the applications such as insurance and law which have many similarities to the intelligence application rarely have the same farreaching cost associated with failure as the intelligence application
when the technologist is leading an important part of any technology transfer project we have found is the establishment and maintenance of trust between the technologist and the user
in order to get those more advanced versions of the technology for its use the government has to help push the final steps of technology transfer of those advanced features
additionally the cost of missing something is measured in dollars not the loss of life or the security of the country as it may be for an intelligence analyst
besides the potential this technology may have to improve detection capabilities its major application to date is the filling of data bases from unformatted text to support further analytical tasks
as with any job the task of bringing an application to the operational environment will more likely be successful if the people who are involved work as a unified team
there are several criteria to determine salient concepts the most simple less knowledgeable criterion conmders all those concepts sahent whose activation weight exceeds the average actwatlon weight of all active concepts NUM a second criterion renders a concept sahent ff the total sum of references made to propertms of it and to relationships to other concepts m
one tuple is created for each token in the text with the label true or false
we have outlined a model of textual ellipsis resolution
finding a balance of precision and recall by tuning this parameter is essential for best results
the following shows a part of an annotated dialogue corpus
in this case hello is an appropriate expression in english
for example if both in the source is rendered as hotn in the ocr text it is not possible for the system to generate both as one of the high ranked candidates they share only one feature the bigram ot despite the fact that the conditional probability pr hotn l both might be high
table NUM gives a part of the syntactic patterns extracted from corpus
the above numbers therefore represent an optimal performance of the system for this speaker although there are some hard to measure mitigating considerations
in the training phase the correction rules are learned by aligning the recognized speech samples with their original fully correct versions on sentence by sentence basis
in this approach an output from a speech recognitio n system is passed to a trainable correction box module which attempts to locate and repair any transcription errors
the validated transcriptions have been extracted from the hospital database by the hospital personnel and then sanitized to remove any patient information such as names addresses etc
finally we may consider an open box solution where the information encoded in c box rules is fed back into the srs language model to improve its baseline performance
generally we observed significant word error rates in automated speech recognition in some cases as high as NUM with the average of NUM NUM
some srs produce ranked lists of alternative transcriptions n best which ean be used to further improve the chances of making only the right corrections
the training data must contain parallel text samples a manually verified true transcription and the output of the asr system on the same voice input
the basic premise of our approach is to utilize linguistic and sublanguage specifie information even speaker specific features in order to improve the accuracy of speech transcription
rp NUM rtjp e ez acount c rp rp 6rup ez stcount c rp rpactzve e rp rpj 6rup
for illustrative comparisons there are two questions to be answered how do we decide whether an illustrative comparator should be introduced
a forest a tree reading of forest f is a tree in f that contains the root and all leaves
for every operator e.g. an np a lower label and a series of upper labels must be given
the boll ore up kssuml tion makes sure that vertex w has t een treated
constituent NUM can be focus with respect to argument i while constituent NUM is focus for argument j in a rule
a drawback of the mrs approach might be that it generates semantic readings which are not licensed by the syntactic structure
to give an example consider the sentence l saw a man in the apartmerit with a telescope
a typical example of such a technique is deleted interpolation which is described in section NUM NUM below
this indicates that the proposed formula is doing a pretty good job of approximating an optimal parameter choice
an optimal weight setting was determined for each test corpus individually and used in the experiments
may depend on the conditionings but are required to be nonnegative and to sum to one over j
one way is to construct an equivalent numerical random variable and use the standard deviation of the latter
the information generated for each word consisted of a data label a unique tracking number the actual word and its part of speech a vector of real values xl xc and a label or indicating whether a segment boundary had preceded the word in the original segmented corpus
in term based representation a document as well as a query is transformed into a collection of weighted terms derived directly from the document text or indirectly through thesauri or domain maps
a tree is an auxiliary tree if the root and some frontier node which becomes the foot node have some non empty sf value in common
intuitively it would not be appropriate to reduce it further because the lexical anchor adverb does n t semantically license the subj argument itself
if no sf were raised we would lose all information about the saturation status of a functor and the algorithm would terminate after the first iteration
as mentioned we assume that bridge verbs i.e. verbs which allow extraction out of their complements share their slash value with their clausal complement
for instance it is possible to declare specific non sfs which can be raised thereby reducing the number of useless trees produced during the multi phase compilation
the compilation algorithm assumes that all hpsg schemata will satisfy the condition of simultaneous selection and reduction and that each schema reduces at least one sf
however this identity itself is n t sufficient to identify foot nodes as more than one frontier node may be labeled the same as the root
adjoining separates them by introducing the path from the root to the foot node of an auxiliary tree as a further specification of the underspecified domination link
quite obviously we must raise the sfs across domination links since they determine the applicability of a schema and licence the instantiation of an sd
these selection techniques attempt to produce the best overall result taking the probability of transitions between segments into account as well as modifying the quality scores of individual segments
they clearly are not of the quality that would be expected if conventional procedures were used but nevertheless are sufficient for providing crosslanguage communication capability in limiteddomain speech translation
the interviewee s gui is now extremely simple and a touch screen has been added so that the interviewee is not required to type or use the pointer
we decided the syntactic pattern which consists of the fixed number of syntactic features
finally let us look at the problem case of parallelism constraints for structurally underspecified clause pairs
the latter is a byproduct of the applications of rule iii to the two nps
context constraints plus existential quantification can express subtree constraints over finite trees
the unique solution of the constraint in figure NUM can be described as follows
mary reminds her friend on it that mary reminds her friend of the fact that b mary nimmt keine rficksicht darauf daft
so at this significance level we can conclude that smoothed trigram statistics improve on bigram statistics alone
this recall rate is a lower bound for the actual rate with respect to the corpus since there are prepositional sfs listed in the published dictionary with no instance in the corpus
this sentence allows the hearer to infer what the speaker s speech act is
porting to a new language introduces an array of challenges
a second stage of the experiment tested relevance feedback on the same queries
they also show that the contribution of each system component w ries fl om one lass of name expression to another
each sentence is fed to the recogniser and all single and multi word matches are tagged with special tags which indicate the name lass
in addition the interviewer s gui controls the state of the interviewee s gui
the higher quality higher investment kbmt style engine typically requires over a year to bring online
typical examples are tsu piece and ko piece
high building or a post modifying prepositional phrase a chocolate NUM yen in price
since dtn is defined using recursive transition networks it can handle recursively embedded subdialogues
crossclass relations are particularly important as the expression of one concept is often not restricted to a single word class
in contrast germanet enforces the systematic usage of artificial concepts and especially marks them by a
additionally the final version will contain examples for each concept which are to be automatically extracted from the corpus
contrary to wordnet germanet enforces the use of cross classification whenever two conflicting hierarchies apply
thus they can be cut out on the interface level if the user wishes so
an example for synonymy are torkeln and taumeln which both express the concept of the same particular lurching motion
they are very productive which would lead to an explosion of entries if each particle verb was explicitly encoded
the set of documents contained NUM NUM strings and the overall word error rate after ocr processing was NUM NUM NUM NUM
the first solution yields a narrow focus reading only sarah is in focus whereas the second and the third yield wide focus interpretations corresponding to a vp and an s focus respectively
in this case dan is taken to be a primary occurrence because it represents a source parallel element which is neither anaphoric nor controlled i.e. it is directly associated with a source parallel element
we set up experiments on english and japanese name tagging using the same texts that were used for the named entity task of the muc NUM and met competitions
intuitively there are two reasons for this
the initial raw text is tokenized and tagged with parts of speech
experimental results were obtained by applying the generated trees to test texts
the same three experiments conducted lor spanish are being conducted for japanese
thc set of derived features is attached
figure NUM weighted p r scores comparison
more advanced possibilities include adding non terminals to the rules as in had cd horn at cd hours where cd stands for any number here the word tagged with cd part of speech
the maximum possible rate for a ternary sequence is log NUM NUM NUM NUM
hence their indetermination with respect to conceptually driven inferencing in the context of text understanding
actttally only few systems exist which deal with texttml ellipsis in a dedicated way
we would like to fllank our colleagues in tim cpsz9 r group for fmitfifl discussions
in this approach discourse entities linking successive utterances in a discourse segment are organized in terms of centers
the evaluation used our knowledge base from the information technology domain which consists of NUM concepts and NUM relations
of documents returned as a result of a query the user s query has threet bearing on the content of the documents returned
full text summanzauon as a major task an tipster phase hi tipster phase i sponsored research an reformation extracuon and reformation retrieval and supported the message understanding conferences muc
this allowed us to keep steady the effects of multiple copies of the same sentence in the same perplexity bin
these idiom compoimnts are taken to have referents
equivalent english idioms with an equivalent meaning
antecedent for the anapher in l he
with this group we are dealing here in depth
null our parsing engine is an active chart parser
the semantic representation of a phrase in the text only includes information contained nearby the discourse module must infer other longdistance or indirect relations not explicitly found earlier and resolve any references in the text
the high error rate on unknown words in chinese is consistent with our expedenee with english if ending analysis and capitalization are not employed the error rate on unknown words is roughly NUM
three factors seem critical the amount of part of speech training data whether the written language supports ending analysis and the size of the list of words plus their parts of speech
therefore it was necessary to have the xat library receive stc or big5 encodings and display traditional characters and to have inquery translate the traditional encodings into simplified characters to relrieve documents from a text collection
fillers found nearby are of high confidence while those farther away receive worse scores low numbers represent high confidence high numbers low confidence thus NUM is the highest confidence score
some number of the top terms can be automatically added to the original query to add coverage and specificity or the user can be prompted to select which terms to add to the original query
for the demonstration the baseline evaluation metrics of muc and trec for information extraction and information retrieval respectively had not previously been applied to chinese information technology
for the demonstration system part of speech data which was prepared early in the project before all the requirements for downstream modules were clear often had to be revised
this will produce a more readable query but ongoing research suggests that the results may be the same or worse than those produced by the bigram model
although one of the purposes of the demonstration systems was to provide valuable feedback in the iterative design cycle of the tipster architecture development this strategy in retrospect was detrimental to successful system development
a radius of NUM means the current token and both the previous and next tokens will be part of the tuple NUM token in each direction
since there are no possible antecedents within the sentence in phase NUM possible antecedents are checked which are stored as the acquaintances focus and potfoci of the sentence delimiter for the previous sentence
although it is very simple technique almost all the useless strings are excluded through this step
through these steps only the strings which significantly co occur with the key string strk are extracted
in the example appropriate or installation is filled in the first gap
thus we retrieve an arbitrary length of interrupted or uninterrupted collocation induced by the key string
common collocations are tend to be ignored because they are not used repeatedly in a single text
null table NUM illustrates how the entropy is changed with the change of string length
actually the former strings are more useful to construct collocations in the second stage
punctuation marks and function words in the strings are useful to recognize how the strings are used in a corpus
as the method focuses on the co occurrence of strings most of the collocations are specific to the given domain
several modules are part of cogent cogentex s generator shell
aberdeen ab9 2ue scotland ereiter csd abdn
the text is viewed via a world wide web browser such as netscape or mosaic
modex is implemented using the now fairly standard modular pipeline architecture
NUM is a section and does not belong to any courses
cardinality it is illegal for a section to belong to zero courses
for example s1 is a sectioa and belongs to the course cs100
a user domain expert performs a validation of the formal model
modex includes examples in its texts as well as conventional descriptions
analogously the general concepts which pertain to the upper levels of h class such as human being physicalentity modality etc form a sort of upper level ilwariable ontology
oblig atory suggests that someone is obliged to do or to endure something e.g. by authority and pertains to the deontic modulators series
nkrl is a conceptual language which intends to provide a normalised pragmatic description of the semantic contents in short the meaning of nl narrative documents
throughout this paper we will use the italic type style to represent a concept the roman style to represent an individual
the data structures used for the concepts are substantially frame like structures h class corresponds relatively well therefore to the usual ontologies of terms
if the unification succeeds tile right haud sides consequent parts are used e.g. to generate well formed templates ctriggering rules
giving rise at a later stage to the occurrence c2 of fig NUM the x symbols in fig NUM correspond to a variables
the concepts correspond to sets or collections organised according to a generalisation specialisation tangled hier chy which for historical reasons is called h class es
their generality and their precise formal scmautics make it possible e.g. tile quickly production of useful sets of new rules by simply duplicating and editing the existing ones
this process eventually identifies all the rule candidates generated by that template set that would have a positive effect on the current tag assignments anywhere in the corpus
the internal structure of these v type chunks loosely followed the treebank parse though v chunks often group together elements that were sisters in the underlying parse tree
for example from the maximal np le disque dur de la station de travail it extracts the two terminological phrases disque dur and station de travail
however because its goal is terminological phrases it appears that this system ignores np chunk initial determiners and other initial prenominal modifiers somewhat simplifying the parsing task
pascale fung extended a tool originally designed for extracting english compounds cxtract to collect new words in order to improve the segmentation precision NUM
it is composed of all articles of the newspaper computer world t lij l from NUM to NUM
these potential words are carefully examined by skillful computer professionals and many of them are accepted and then appended to the dictionary in order to improve segmentation precision
thus there are four types of results altogether result NUM c a and c b which is noted as NUM
the relatively lower precision can be attributed to the fact that some terminologies especially those available in the original dictionary have meaning outside computer domain
the correlation coefficient could be measured by several methods such as co occurrence frequency mutual information generalized likelihood estimation chi square test dice coefficient
we can find terminologies extracted from the universal dictionary are much fewer than those extracted from new words of the NUM NUM words only NUM were accepted finally
in the following experiment this formula is replaced with pc w t2 p w where t2 is a threshold
number of new words is limited and most of new words are domain specific words qnologies and proper names this work is also done manually
in the next section we motivate the focus on this population
our effort involves designing the system around the specific needs and abilities of the particular population
thus we must carefully study this population to determine exactly what kind of input to expect
notice that the rate enhancement power of compansion is heightened when sophisticated linguistic constructions are used
e.g. verb frame information is associated with each verb
first this population will rely on a limited vocabulary
additional support has been provided by the nemours founda tion
these interpretations will be used to further tune the grammar expansions
some people have disabilities which make it difficult for them to speak in an understandable fashion
also it is possible to spell words that are not included in the core vocabulary
another logical step is to remove all other first names and titles which are placed immediately in front of their governing words
let us demonstrate the problem on a sample sentence from the corpus of czech newspaper texts from the newspaper lidov noviny
the gain of speed would be even greater would we have worked with a negative or a nonprojective variant of the parser
it introduces three measures by means of which it is possible to classify the degree of nonprojectivness and incorrectness of a particular sentence
because the grammar checker is running as an independent application the user may also look at the complete results provided by it
this paper describes the implementation of a prototype of a grammar based grammar checker for czech and the basic ideas behind this implementation
the grammar checker is implemented as an independent windows application grammar exe which runs on the background of the word
the core of the system is the second grammar checking phase therefore we will concentrate on the description of that phase
b positive nonprojective negative projective this phase tries to find a syntactic tree which either contains negative symbols or nonprojective constructions
the followings are the procedures to retrieve a collocation NUM
the second step eliminates the strings that are not frequent enough
then its position is determined to follow refer to
we introduce entropy value which is a measure of disorder
this process is repeated until no string satisfies the inequations
str is accepted only if the following inequation is satisfied
take in spite of for example
necessary thresholds are given by the following set of equations
table NUM shows top NUM strings in order of entropy value
by changing the threshold various levels of collocations are retrieved
see table NUM and fig NUM
t denotes the number of articles considered
n is the number of words that appear in h
there is thus always a danger of misrepresenting documents
figure NUM a sample news story
the structure of two successor functions h2 has for its domain n2 the infinite binary branching tree
in recent years there has been a continuing interest in computational linguistics in both model theoretic syntax and finite state techniques
since the automaton represents the parse forest we can run it to generate parse trees for this particular input
finally the architecture will allow systems to be upgraded in a modular fashion as new text handling technology becomes available
primarily at present it is the method for recording and passing forward the information developed by the extraction component
so no particular degree of cleverness is assumed on the part of the formalizer optimization is done by the compiler
so if we view the inductively defined relations as part of an augmented signature this signature contains relations on sets
as usual a definite clause is an implication with an atom as the head and a body consisting of a satisfiable mso constraint and a possibly empty conjunction of atoms
accepts a tree t iff ha t e f the language recognized by a is denoted by t a lcb tlh t e f rcb
al on the left subtree and a2 on the right one we go to the final state aa which again can be percolated as long as empty symbols are read
the projection construction yields a nondeterministic automaton but again as for fsa s bottom up tree automata can be made deterministic by a straightforward generalization of the subset construction
and furthermore the clp extension offers an even more powerful language which allows a clear separation of processing and specification issues while retaining the power and flexibility of the original
the yield and ecp predicates can easily be explicitly defined and if practically compilable which is certainly the case for yield could then be treated as facts
the recognizable sets are also closed under projections mappings from one alphabet to another and inverse projections and again the construction is essentially that for finite state automata
in linguistic applications we generally use versions of c command which are restricted to be local in the sense that no element of a certain type is allowed to intervene
this description will be adequate to the open model of language
9b overcome data sparseness methods to estimate the distribution of unseen eooecurrence frorn the distribution of similar words in the seen cooccurrence has been proposed
endwhile optimize c procedure lnit sets up tile initial class
morphological and phonological phenomena arc usually described by rewriting rules
the description is specified by adding conditions extending the context
given this new goal the text planning component selects any relevant information from the knowledge base and organises the information according to the current discourse plan
search the stem grade or stem end change NUM form the corresponding rule r NUM NUM
there are two reasons why a clarificatory comparison might be used the focused entity might be extremely similar to another entity and therefore often confused with that entity
null the knowledge base that currently underlies the system has been hand constructed from an analysis of encyclopedia descriptions of animals and constitutes a taxonomy of the linnaean animal classes with their associated properties
table NUM module contribution scores table NUM shows thai we can attain reasonable
the se lists are compiled via a flex program into a finite state recogniser
for organization names using the same settings as above scores are shown in table NUM
here are some examt les of the i roper nmne grammar rules
in tile second section an overview of the i asie system is presented
when llarsing for a senten e is omplet
the system applies a mixture of techniques to perform this task and these are described in detail
we perform this analysis not only for all classes of names but for each class separately
setting NUM only the lexical preprocessing teehifiques are used tmrt of speeeh tagging and name phrase matching
zhang shu wu also presented a strategy which made use of co occurrence frequencies to collect new words NUM
the bottom line is that government analysts particularly intelligence analysts have text handling and information handling needs that are more difficult to satisfy than those of the general software buying and using commercial world
much of the risk of a technology insertion project can be mitigated by two strategies paying careful attention to the user issues addressed above and paying careful attention to the steps of technology transfer detailed above
software design issues NUM NUM NUM are currently being evaluated through examination of the design in the tipster engineering review board as well as by monitoring of the integration and maintenance phases by the project leader
however a current tipster project is using a combination of interviews with user testers after their sessions with the prototype and observations of the user tester sessions to try to determine how easy to use the application is
rapid prototyping of interfaces including the gui has been successfully used in adept NUM for example to help define the requirements and to flush out technical problems
NUM cots gots and the alternative one of the issues the tipster program has had to struggle with is the form in which its technology can best be provided to the government user
tipster pilots such as canis NUM to some extent perform the same purpose although a less broad range of issues can be experimented with under canis than in ftm
a clear understanding between the developer and the user organization concerning how much effort is being put into fixes and how much into actual modifications to respond to a change in the requirements is very helpful
information from the test and use of these interfaces will be fed into specific requirements for further interface and work flow development if either system is actually going to be moved into full time operational use
in step four the deployment in the experimental environment is used to develop a plausible concept of operations in terms of work flow a plausible user interface is developed to go with it
this hmction is defined with respect to a plugging p we represent a i rs a box di where d is the set of discourse markers and c is the set of conditions
the ambitious overall objective of the verbmobil project is to produce a device which will provide english translations of dialogues between german and japanese businessmen who only have a restricted active but larger passive knowledge of english
in order to achieve this the system is composed of time limited processing components which on the source language german or japanese side perform speech recognition syntactic semantic and pragmatic analysis as well as dialogue management transfer on a semantic level and on the target language english side generation and speech synthesis
section NUM introduces lud description language for underspecified discourse representations the semantic formalism we use
firstly all elementary semantic bits conditions entities and events are uniquely labeled
the predicates of a lud representation are stored in a special slot provided for each category by the trug systcm
this property is used for the semantic counterpart of the rule lud fun arg is a call to a semantic rule a macro in the tug notation which defines functor argument application
since it is impossible to consider all preceding utterances NUM NUM si NUM as contextual information we use the n gram model
clue word is the special word used in the utterance having particular speech acts such as yes no o k and so on
section NUM gives an outlook on future work
the algorithm does in fact compute a third kind of solved constraint for NUM where none of the quantifiers two language and many linguist are required to be within the scope of each other
let each node p in the syntactic structure be associated with three semantic meta variables xp x p and cp and let i p be the scope boundary for each node p
these boundary points in the text are found first
we focus on english and japanese in this paper
the tool reads and writes texts in sgml format
altogether these make up the learned tagging procedure
this is especially true for a system such as word keys which uses semantic relationship for retrieval and performs message ranking which can increase the impact of inaccuracies in the morphological analysis
the list contains the most frequent NUM words in this corpus evaluation of a comparison between a frequency counting lexicon and a lexicon without word frequencies are summarized in the next section
after the affix removal the unaffxed form is looked up in the lexicon considering the possible syntactic category returned by the affix removal process
it is typically used in two different settings when the user wants to prepare a communica null tion new messages are typed in
out of the NUM NUM NUM cases NUM are mistranscripfions of the word effusion therefore the context flee rule fusion effusion could be proposed
we estimate that about a day worth of dictation is sufficient to initiate the training process some NUM reports or about NUM kbytes of text
chest x ray is the most prevalent form of radiology and we decided to start with this sub area because of its the largest potential practical significance
the experimental data was obtained from the university of maryland medical center in baltimore which is one of the clinical sites used in this project
the redictation was done over a period of several months by a final year radiology resident at albany medical center a native north american english speaker
this says that the word fusion has been mistakenly generated NUM times in the training sample of the srs output and this constitutes at least NUM of its total observed occurrences
one advantage of this two pass approach is its independence from any particular srs and indeed our correction box c box module can be used as a back end of any speech system
step NUM the above process may lead to alignment re adjustments as suggested in step NUM upon re alignment additional rules may be postulated while other rules may be invalidated
this rule is not perfect since it will potentially mjscorrect other occurrences of fusion but its application can be expected to reduce an overall transcription error rate on similar data samples
the messages retrieved from an experimental database for the item swim are in that order NUM would you like to go or a swim
the associated text parser is based on the actor computation model
schacht et al NUM hahn et al NUM
phase la yields no positive results and the message terminates
whigh the lte lite develops equips it with a pcl motherboard
consequently any of these subprotocols constitutes part of the grammar specification proper
additionally it tests whether the definite np agrees with the antecedent in number
this violates the given constraints hence coreference is excluded
hence coreference between anaphor and antecedent must be granted
maryi tells a story about hersel i
it is our view that this methodology is the only one that is likely to be successful for broad coverage practical nlg systems
a direct comparison is a comparison whose purpose is to compare two entities where neither entity is more central to the discourse than the other
this is exhibited in such expressions as the following NUM NUM bill got a lot of house for NUM NUM
one interesting case is fear which as a count noun denotes both kinds of fear and objects of fear
to make clear what these conditions are i shall turn to the introduction of a few mereological and set theoretic concepts
yes plural mass 4more precisely singular quantified noun phrases exhibit different scope like interpretations while singular demonstrative noun phrases do not
quantified count noun phrases range over elements in the aggregation formed from elements in the denotation of the noun phrase s count noun
indeed emotion is a mass noun which converts to a count noun with the correlated shift in meaning of kinds of emotion
these are words which were mentioned earlier stone rock ash string cord rope and tile
NUM a this specification is to be distinguished from phonological specifications pertaining to the phonological reafization of the features pl
for example advice is a mass noun advices whereas suggeslion is a count noun suggestions
the mapping is defined as follows
his system did not consider nomlnal and adjectival frames
unlike current generation systems which aim at the automated production of instructions and thus keep the authors out of the loop drafter is a support tool intended to be integrated in the technical author s working environment hopedally automating some of the more tedious aspects of the authors tasks
while drafter obtains as much as it can of this knowledge base automatically from external sources it also allows the authors to specify the portions that can not be acquired automatically and provides for a parallel development of knowledge base and natural language text
choo saveopticnfro cancellauon method click on save file butts type name of arbitrary d open save as method NUM click on save button open save as method NUM click on no sutton save document method click on yes button c ase micro tt word wlndot cic save as file windo cic save changes window open microsoft word windo open save changes window choose exit option from file start te wold program ouff test word program
it contains a single method specifying a cancellation action i.e. that the save as file window may be closed by performing a particular method and a set of sub steps i.e. opening the save as file window typing the name of the file and clicking the save button
m is a marking function from vertices to grammar symbols
the lower label points to material which must be in the scope of the operator e.g.
NUM every vertex except the root has exactly one predecessor
in udrt one introduces labels that behave very much like variables for drses
this work is currently in progress and a deeper comparison between the approaches has yet to be carried out
a natural approach for describing underspecified semantic information is to use an appropriate constraint language
the signature of context constraints contains a unary function symbol lamx and a constant var
in brief the scheme operates by comparing the equivalence classes defined by the link s in the key and the response rather than the links themselves thus this is only wel l defined for identity links at the moment
in NUM the antecedent peteris not d bound by the head which d binds the anaphor er and peter precedes er
the initiator of the searchanteeedent message viz the anaphor upon receipt of the antecedentfound message changes its concept identifier accordingly
moreover conceptual criteria have to be met as in the case of nominal anaphors which must subsume their antecedents at the conceptual level
in NUM the subordinate clause immediately follows its head word while in NUM the subordinate clause is extraposed
in particular improvements are due to discussions we had with s schacht n br6ker p neuhaus and m klenner
let u be a complex feature term and a feature then the extraction u l yields the value of l in u
the definition of the grammatical predicates below is based on the following conventions ii denotes the unification operation NUM the inconsistent element
on the other hand if the anaphor belongs to the matrix clause and the antecedent to the subordinate clause coreference is excluded cf
we would like to thank our col leagues in the cz z r group who read earlier versions of this paper
a problematic cas e consider the first problematic example from the coreference task guidelines key links a b b c b d response links a b c d note that the key links generate an equivalence class the set a b c d i
many of the errors robotag makes come from the matching algorithm where the decision trees correctly predict tag begins and ends but the wrong tag pairings are chosen
currently each tuple contains the preprocessor information for a window of tokens in the text but the actual token text is not available to the learning
gain ratio is used to measure at each step of tree construction which feature test would best distinguish the examples on the basis of their class
we use this disambiguation method to buihl a disambiguation module in pfte system l in what follows we first outline the idea of using hybrid information to sui ply preferences for resolving ambiguous pp attachment
we cluster words verbs or nouns hi h have s une feature or syntactical function into a ncel t class
ur exl erimcnt shows tha t the hybrid al proach we taken is both effectiv and a NUM l li able ill practice
the algorithm used in the nn hfle is if ra v nl NUM n2 NUM NUM then choose np attachment otherwis choose vp attachment exit
unlike traditional proposals corpus based approaches need not to prepare a large amount of handcrafted rules they have therefore the merit of being scalable or easy to transfer to new domains
to cope with this problem we introduce a hybrid method of integrating corpus based approach with knowledge based techniques using a wide variety of information that comes from annotated corpora and a machine readable dictionary
traditional proposms are mainly based on knowledge based techniques which heavily depend on empirical knowledge encoded in handcrafted rules and domain knowledge in knowledge base they are therefore not scalable
where the occurrence frequency is too low to make a reliable choice wc turn to use conceptual infornlation froln a machine readable dictionary to to make decision on pp attachments
here the first bit position indicates membership in p the second is for x and the third
nevertheless we know that a regular polysemy exists between meat and animal
a major concern in the design of the diplomat system has been to cope with the error prone nature of both current speech understanding and mt technology to produce an application that is usable by non translators with a small amount of training
jassem postulates that speech is composed of extrasyllable narrow rhythm units with roughly fixed duration independent of the number of syllable constituents surrounded by varia ble length
the culminative property of stress states that every content word has exactly one primary stressed syllable and that whatever syllables remain are subordinate to it
the bin at perplexity level pp contains all sentences in the corpus with perplexity no less than pp and no greater than pp NUM
NUM holds rating x powerful holds produce x y holds isa y watts per channel holds amount y z number z z NUM
additionally when presenting the proposition audiofad is an audio journal the generator is able to recognize the similarity with the corresponding proposition about stereofool i.e. both propositions are abstractions over the single variable open proposition x is an audio journal
as we treat each sentence as an independent event no cross sentence n grams are kept only those that fit between sentence boundaries are counted
many experiments lead to the common conclusion that english is stress timed that there is some regularity in the absolute duration between strong stress events
the program remains committed to continuing an aggressive technology transfer effort
particles pose a particular problem in german
subsystem of morphology plays the central role in processing of the morphologically complex languages as the estonian language is
in case of data presented as strings class is often defined by the substring varying in length
given an entity z and a referring expression for x the contrastive focus feature for its semantic representation is computed on the basis of the contrastive focus algorithm described in NUM NUM and NUM
in phase la the message reaches the subject firma which is the leftmost modifier of the verb and determines the noun lte lite as the only possible antecedent of ihn
the success of pronanaphortesl leads to the sending of an antecedentfound message the result of which is the update of the concept identi null tier of ihn with that of lte lite
both define salience metrics capable of ordering alternative antecedents according to structural criteria several of which can directly be attributed to the topological structure and topic comment annotations of the underlying dependency trees
otherwise a possible loop is detected
figure NUM dags used in the example
is able to create a less restrictive restrictor
in this paper a general method of maximizing top down constraints is proposed
if b is subsumed by a the propagation for this path terminates
note that in this paper restrictor
directed aeyclie graphs dags are adopted as the representation model
can be collected incrementally as the cyclic propagations are repeated
top down propagation can be precomputed to form a teachability table
more subtly there is widely believed to be rhythm in english prose reflecting the arrangement of words whether deliberate or subconscious to enhance the perceived acoustic signal or reduce the burden of remembrance for the reader or author
we wish to create a model that yields approximate values for probabilities of the form p sklso sl sk NUM where si e is the stress symbol at syllable i in the text
a model with separate parameters for each history is prohibitively large as the number of possible histories grows exponentially with the length of the input and for the same reason it is impossible to train on limited data
to measure the efficacy of these models in prediction it would be necessary to divide the corpus train a model on one subset and measure the entropy rate of the other with respect to the trained model
it is unpleasant for production and perception alike however when too many weak stressed syllables are forced into such an interval or when this amount of padding varies wildly from one interval to the next
for the purposes of this paper we shall regard stresses as symbols fused serially in time by the writer or speaker with words acting as building blocks of predefined stress sequences that may be arranged arbitrarily but never broken apart
given that speech can be divided into interstress units of roughly equal duration we believe the more interesting question is whether a speaker or writer modifies his diction and syntax to fit a regular number of syllables into each unit
to be fair we chose to take the epoch with the maximum f score on the validation set as the best configuration of the net and we report results from the test set only figure NUM shows a typical training learning curve of a neural network
note that at the beginning of each speaker turn or utterance the first c x w NUM input units need be padded with a dummy value so that the first word can be placed just before the middle of the window
this boosts productivity in hand coding rules but still requires a significant amount of effort by the developer to identify key parts of the pattern
in this section we describe robotag s decision tree learning learning representation learning parameters the tag matching algorithm and postprocessing
one of the leaf nodes of the tree has been selected producing a window which shows person names in context as classified at the leaf
because of this it is not clear what kind of interventions will be effective with these children
it furthermore prohibits joint polysemous entries with dependencies from applying for only one aspect of a polysemous entry
alt j e follows option b such nouns are entered into the lexicon twice once as a common noun and once as a jos ishi
although the collocations retrieved by the method are monolingual and they are not available to the machine application for the present the results will be extensible in various ways
it seems to be promising to improve the very basic clp interpreter too
of the mso constraint language into a constraint logic programming scheme
all other transitions are to at figure NUM automaton for local c command
it is far from the most compact and elegant formula defining that relation
note in particular the role of the compiler as an optimizer
consider again the definition of ac command in the previous section
the decidability proof for ws2s is inductive on the structure of mso formulas
we need only compile into the constraint store those which are really needed
this formula was compiled by us and yields the automaton in figure NUM
for completeness we sketch the definitions of trees and tree automata here
NUM examine how often each possible combinations of str and stri co occurs and extract stri if the frequency exceeds a given threshold tire q
psxay make x y psx mistake x for the same reasons the a drs for bock contains the predicate mistake x
in contrast to karat l llss are not constructed in a to t down fashion from a phrase structure tree but bottom up ltsing a version of a calculus
nevertheless different formalisms m c used to rapresent syntactic and semantic features having the adwmtage that for syntax as well as for semantics thc most appropriate tbrmalism can be chosen
in the following we will point to the fact that there are german decomposable idioms which can be decomposed into components having identifiable meanings contributing to the meaning of the whole
therefore to ml lerstand tit phenolnen we need other reasoning met hods rout in f t many researches h tve been using general reasoninn f am works in artificial intelligence
we assmne that is a formula which should be satisfied in the first place o in the second place pa in the third place q54 in the fourth place and in the fifth place
then we inst mtiztte universm quandfied varim le s in background knowledge mm free variables in preferen es wit h the relevmlt onsl mts and iul rodu e skolean fimctions for existentialqua ntified
in clp we a cmnulate labeled conslrmnts to form a constraint hierarchy by each 1m el while exe uling clp until clp solves all goms mm gives a reduced required constrmnts
we denote the conjunctions of the following ioms as a0 p where p d f eq is time act actor object recipient device sub j in the sentence
this approach integrates such methods as new word collecting terminology word extraction and terminology phrase generation
in this paper a semi automatic approach is developed to extract technical words and phrases from corpora
although xn corpus is only one tenth of cw in size it gains better results
for example left mouse key ig drag
class NUM the two words compose a verb object subject verb or other phrases
there are altogether NUM NUM phrase candidates with frequency greater than a threshold t3 here t3ffi3
in these tables many candidates are available in the universal dictionary others are potential words
we define the correlation coefficient of characters a and b to be the value of chi square test
now consider two neighboring characters a and b we call these two characters as a bi gram candidate
first those domain specific words which ca n t be found in the universal dictionary are identified
in order to actually build the new phrase a eopyandattach message is sent NUM fig NUM depicts the copying of the governing and modifying phrases zsince forwarded messages are sent asynchronously the processing of the searchhoadfor message takes place concurrently at the forwarding sender m d the respective receivers
given that the control flow of text understanding is globally unpredictable and also needs to be purposefully adapted to critical states of the analysis e.g. cases of severe extragrammaticality we drive lexicalization to its limits in that we also incorporate procedural control knowledge at the lexical gr unmar level
as we are faced however with the problem to work out text interpretations incrementally and within reasonable resource bounds we opt for a methodology that constrains the amount of ambiguous structures right at the source
that is in principle if we can formalize a grammar in an mso tree logic we can apply these compilation techniques to construct an automaton which recognizes all and only the valid parse trees
the generation architecture described above and implemented in quintus prolog produces paragraphlength spoken monologues concerning objects in a simple knowledge base
unit classifiers are the prototypical classifiers
a non word error occurs when a word in a source text is interpreted under ocr as a string that does not correspond to any valid word in a given word list or dictionary
examples are given in table NUM
this has no direct english equivalent
sees and the appropriate combination of features e g v naively at least every feature we use must have its own bit position since in the logic we treat features as set variables
NUM when is a classifier a classifier
recall that the system lexicon is based on the words derived from the training corpus some words may be present in the test corpus that are not in the training corpus
for example run on errors e.g. of the ofthe and split word errors training train ng can not be corrected
research in electronic lexicography has been very intense during the last years and many large dictionaries are being built for different languages
little research has been dedicated to the investigation of full text retrieval of short messages such as those used in communication aids
in the paper we present details of the approach describing those data and processing componm ts of the overall ie system which contribute to proper name recognition and classification
since name recognition and classification is achieved through the activity of four successive components in the system we quantitatively ewfluate tile successive contribution of each comt onent in our overall approach
a trigger word indicates that the tokens surrounding it are probably a proper name and may reliably pernfit the class or even subclass NUM of tile proper nmne to be determined
manually collected as well as name phrase matching another technique is applied at this point inside multi word proper names certain words m y flmction as triqget words
the system is a pipelined architecture which processes a text sentence at a time and consists of three principal processing stages lexical preproeessing parsing plus semant ic inter pretation and discourse interpretation
the system was tested oil NUM unseen wall street journal texts and the results were analyzed in terms of inajor system components and ditti rent lasses of referring expression
airlines governmental institutions NUM trigger words for governmental institutions e g ministry for word classes such as days of the week and months
first and foremost in order to generate quantitative evaluation results we have used tile muc NUM data and scoring resources and these restrict us to the above proper name classes
si ecii s classifiers are partitives of quality and an occur with countable or uneo lnt ble llolln phrases
the selection of an appropriate translation is not dependent on this analysis and can be left to the normal machine translation process
as a default it is entered in the dictionary as a gi nei lcb al classifier with the translation piece
whether a noun is a classifier and if so which type is marked in the lexicon for each japanese english noun pair
n english comltal l nouns can t directly m dili d
the fifth major type of countability preference is pluralia tanta nouns that only have a plural form such as scissors
kind of something further when n is an attribute and c measures the same attribute the interpretation is again different
translated as though the classifier were a norreal noun giving the n of x c for examph
these semantic representation are the input to the discourse component
develop effective management mechanisms for multiple site coordination with the government
ovals represent declarative knowledge bases rectangles represent processing modules
NUM NUM extraction NUM NUM NUM porting components of the plum information extraction system
to chinese the plum architecture is presented in figure NUM
in practice the thoroughness of relevance judgments will vary
underlying the processing components is a domain knowledge base which is the main repository of information about the domain
drafter also involves two industrial partners integral solutions limited isl a sofrware company specialising in artificial intelligence products and praetofius ltd a leading translation and technical writing consultancy specialising in software documentation
aset x lcb y i azt x y rcb x s alternatives rset x lcb rcb lcb x rcb u lcb y y NUM aset x y e deiist rcb evoked alternatives cset x lcb rcb lcb rcb
we will denote the substitution of a term n for all free occurrences of x in m with n x m
for first order logic this problem is decidable and the set of solutions can be represented by a single most general unifier
this is not the case for higher order colored unification where variables can range over functions instead of only individuals
another interesting research direction would be the development and implementation of a monostratal grammar for anaphors whose interpretation are determined by colored unification
in order to make this approach work formally we have to extend the supply of colors by allowing boolean combinations of color constants
b like dan golf ar peter c like dan golf r dan d
under these assumptions the equation for 4a will be 6a which has for unique solution 6b
the syntactic pattern includes the syntactic features that are related with the language dependent expressions of speech acts
we proposed a statistical method to decide a speech act of a sentence and to maintain a discourse structure
technology taejon NUM NUM korea lcb j wonl ee gckim rcb ocsone kaist ac
we extracted NUM pairs of speech acts and syntactic patterns from a dialogue corpus automatically using a conventional parser
thus like the scheme proposed in sundheim et al we have an aesthetically pleasing inverse relationship between precision and recall
for the example above we see that the response generates an equivalence class of size NUM namely the set s lcb a b c
the solution to a search branch of a program is a satisfiable constraint represented in solved form as an automaton
from the scheme we get a sound and complete but now only semi decidable operational interpretation of a definite clause based derivation process
parse words tree in more detail words denotes a set of nodes labeled according to the input description
a weak second order theory is one in which the set variables are allowed to range only over finite sets
there exist much smaller formulas equivalent to that definition and indeed some are suggested by the very structure of the automaton
as the string pair is parsed from left to right the stem grade change is observed betbre tile stem end change
recognition algorithm was tested on NUM stein variant pairs of cmi linguistically incorrectly classified pairs were not observed
inductive supervised learning learning from examples is one of the main techniques in machine learning
mary thinks on it mary thlulcs about it
NUM mary arbeitet seit zwei wochen daran
this component ranks the sfs obtained by the previous component of the system
the system acquired a dictionary of NUM unique subcategorization frames
sentences ta b are examples to which this rule applies
the rem inlng NUM sets were considered to contain incorrect sfs solely
a random set of NUM sets was obtained from these unique ambiguous sets
the log likelihood statistic is used to test this hypothesis
this paper presents a method for learning german prepositional subcategorization frames
phenomena of incomplete input or coercion require a withholding or at least a delaying of the closure operation
we now look into the interpretation of examples NUM to NUM which exhibit forms of parallelism
while the readings of a discourse may be only partially known the interpretations of its components are often strongly correlated
we need to apply a closure operation consisting in projecting the remaining free context variables to the indentity context ax x
in addition external factors such as insufficient coverage ported by the sfb NUM at the universits t
the set of solutions of a context constraint represents the set of possible readings of a given discourse
NUM i x speak chinese john a xc john a xs c xcs
this algorithm computes a representation of all solutions of a context constraint in case there are any
scope underspecification for a sentence like NUM is expressed by the equations in NUM
the third constraint results from rule i when the semantics of x is filled in
in the linm step the packed udrs is traversed att t flmetions whet all eontexl s point to a single value are replaced by this vahm
the arguments on this list are not arguments proper as they would be if only pm se trees were considered but fl nctions fl om contexts to arguments proper
in the packed udrs approach it is conceivable to delay actual disambiguation as long as possible apart from the potential representation of referentim ambiguities by functions packed udrss look exactly like udrss
sl refers to the total set of tree readings of the forest since the root vertex figures in all trees derivable from the forest
a dag satisfying the constraints NUM NUM is called shared forest a dag only satisfying NUM is a packed shared forest or parse forest see figure NUM
it should be pointed out immediately that the translation from formulas to automata while effective is just about as complex as it is possible to be
the main reason for this was we noted the limited amount of information that the queries could convey on various aspects of topics they represent
our earlier experiments demonstrated that an improved weighting scheme for compound terms including phrases and proper names leads to an overall gain in retrieval accuracy
in this paper we report on our natural language information retrieval nlir project as related to the recently concluded 5th text retrieval conference trec NUM
the initial evaluations indicate that queries expanded manually following the prescribed guidelines are improving the system s performance precision and recall by as much as NUM
full text expansion can be accomplished manually as we did initially to test feasibility of this approach or automatically as we tried in later with promising results
among the advantages of the stream architecture we may include the following stream organization makes it easier to compare the contributions of different indexing features or representations
for example it is easier to design experiments which allow us to decide if a certain representation adds information which is not contributed by other streams
for example content terms from documents judged relevant are added to the query while weights of all terms are adjusted in order to reflect the relevance information
in order to obtain the final retrieval result ranking lists obtained from each stream have to be combined together by a process known as merging or fusion
this way if a corresponding full name variant can not be found in a document its component words matches can still add to the document score
the entire lexical system is organized as a hierarchy of lexical classes isac denoting the subclass relation among lexical classes with concrete lexical items forming the leave nodes of the corresponding lexicon grammar graph
technically the links are a spanning tree of the set s implicit equivalence graph i e thefullyconnected graph whose nodes are the entities a b c and d
the response partitions this class into a partition p s of size NUM containing lcb a c rcb an d lcb b rcb where the latter element is implicitly defined
once again we proceed by generating a relative partition in this case the partition of the response equivalence class s relative to key equivalence classes k1
in the case of precision we need to do the converse add links to equivalence classes in the key so as to yield equivalence classes in the response
the recall score of NUM NUM aligns with the fact that one equivalence arc is required to complete the response graph yielding one of the following four spanning trees
to extend from a single response to a complete test set t we once again sum over the tes t set this time iterating over response equivalence classes
roughly stated the scorin g mechanism for recall must form the equivalence sets generated by the key and then determine for each such key set how many subsets the response partitions the key set into
although at first blush this seems combinatorially explosive due to references to minimal spanning subsets of th e equivalence relation it turns out it can be accomplished with a very simple countin g scheme
it is intuitive that the precision score for thi s response should be NUM NUM NUM since NUM out of NUM of the response links are correct
note that the equivalence classes defined by the response may include implicit singleton sets these correspond to elements that are mentioned in the key but not in the response
noteworthy is also the behavior of the precision recall curves with our method a high level of recall can be maintained even as the output threshold is increased to augment precision
the part of speech pos tagger brill NUM farwell et
manual analysis of the tree and scored result leads to the discovery of new features
the new features are added to the tokenized training text and the process repeats
the training set used for this example contains NUM negative and NUM positive examples
table NUM shows a summary of various types of features used in system development
on line lists provide lists of cities person names nationalities regitms etc
parts of speech features are predetermined based on the part of speech tagger employed
a system initially optimized for english has been successfully ported to spanish and japanese
for these reasons list based matching schemes do not achieve desired performance levels
al NUM matsumoto et al NUM attaches parts of speech
as in the case of textual ellipsis we have to deal with imths leading fi om the elliptical expression to several altenmtivc antecedents we usually have to compare pairs of path lists cp v and cp where x y z are concepts
a pattern based approach to infereucing inchtding textual ellipsis has also been put forward by norvig NUM
clock mhz pair of cpu of motherboard of central unit oj 316lt is selected as the valid elliptic antecedent
additional criteria have to be supplied in the case of equal strength of cp lists for alternative antecedents
the constraints we posit require a domain knowledge base to consist of concepts and conceptual roles linking these concepts
a quasi anaphoric relation between two lexical items in terms of textual ellipsis is here restricted to pairs of nouns
refers to the instance in the text knowledge base denoted by the linguistic item object and object c
the mode of path evaluation incorporates empirically plausible criteria in order to select the strongests of the ensuing imths
when the clock frequency is reduced suffices the power even for NUM hours
in a power supply independent mode it is for approximately NUM hours with power provided
data preparation of topic descriptions for information retrieval and templates for information extraction is costly but without defined evaluation data how is agreement reached on an acceptable level of performance
we assembled thirty natural language queries modeled on a current set of trec queries a typical query being investment prospects in china for american companies
these characters may appear individually in chinese words but when they are combined to sound out non chinese names they form sequences that are not otherwise part of a chinese lexicon
this information can lead to selection of a different collection or modification of the original query to alter a term that has a domain specific meaning not intended by the user
while the goal of sharable systems across multiple government agencies is admirable all participating agencies must commit to providing support and infrastructure resources to maintain the resulting system within each office
the tipster demonstration system allowed us to test implementation of tipster technology in a new language and gave us a more complete understanding of risks involved in undertaking such an effort
thus manual effort can be further reduced
the list of sorted messages is traversed
there are other interesting problems in aggregations
table NUM tendency of obtained similarities
we evaluate the appropriateness of the obtained similarities
the similarity is usually calculated from a thesaurus
table h result of test of verbal meaning selection
however because these schemes look for similar words in the corpus the number of similarities which we can define is rather small in comparison with the nunlber of similarities for pairs of the whole
since our method construct a thesaurus from the handmade thesaurus by the corpus it can be considered a method to refine the handmade thesaurus such as to be suitable for the domain of the used corpus
thus the obtained similarities are the same in nmuber as the similarities in the thesaurus and they reflect the particularity of the domain to which the used corpus belongs
in the future we will extend and modify bunrui goi hyou by the cooecurrence data and the similarities obtained in this study and will try to classify multiple senses of verbs
an underspecified form representing an utterance is then the representation of a set of meanings all the possible interpretations of the utterance
these are top as described above and main the label of the semantic head of a lu1 reprcsentation
secondly meta variables over drss which we call holes allow for the assignment of underspecified scope to a semantic operator
the parsing component processes the lattice and assigns each well formed path through it one or several syntactic and compositional semantic representations
in this way syntactic derivation and semantic construction are fully interleaved and semantics can further constrain the possible readings of the input
in order to make our formalisation executable we employ the trug system which compiles our rules into an efficient tomita style parser
further we have an operation that combines two lud representations into one q merge for lul representations
the speech recognition component then processes the input and produces a word lattice representing the speech hypotheses and their corresponding prosodic information
we show that a linguistically sound theory and formalism can be properly implemented in a system with near real time requirements
this gives us in some sense the minimal solutions to the original constraint
for example NUM provides no solution to the above constraints
adjectives in germanet are modeled in a taxonomical manner making heavy use of the hyponymy relation which is very different from the satellite approach taken in wordnet
for example the particle verb herauslau en in figure7 is a hyponym to lau en walk as well as to heraus
while the previous step of the algorithm determined which abstract discourse entities and properties stand in contrast the third step uses the contrastive focus algorithm described in section NUM to determine which elements need to be contrastively focused for reference to succeed
for example suppose the system believes that conveying the proposition in NUM moderately supports the intention of making hearer hl want to buy el and further that the rule in NUM is known by hl
we expect that after additional tuning based on further informal user studies an interviewer with eight hours of training should be able to use the diplomat system to successfully interview subjects with no training or previous computer experience
during the delimitation phase proper names are delimited using a set of pos based hand coded tcmplates
one of the major projects at asel the compansion project has been concerned with the application of primarily lexical semantics and sentence generation technology to expand telegraphic input into full sentences
building a speech recognition system for a target domain or language requires models at three levels assuming that a basic processing infrastructure for training and decoding is already in place acoustic lexical and language
one population of aac users that might greatly benefit from expanded telegraphic input are those who are young in age but who suffer from some cognitive impairments which affect their expressive language production
the initial set of lexical features is selected by choosing those that appear most frequently above somc threshold throughout the training data and those that appear most requcntly near the positive instances in the training data
whether a phrase is a proper name and what type of proper name it is company name location name person name date other depends on NUM the internal structure of the phrase and NUM the surrounding context
in order to work with another language the following resources are needed NUM pre tagged training text in the new language using same tags as belore NUM a tokenizer for non token languages NUM a pos tagger plus translation of the tags to a standard pos convention and NUM translation of designators and lexical list based features
these problems coupled with the relatively low processing power and space on devices used for aac led us to question whether or not nlp is possible in viable aac devices
while we do not expect to see the same sorts of complications with the input described above with respect to compansion it is likely that the input will display unusual characteristics
in the following we discuss three stages of increasing restrictions of parallelism at the word level all of which were considered for the design of the algorithm provided in section NUM NUM
for instance any determiner preceding a noun forms a new structure with the det modifying the n usually a contiguity restriction would filter out those structures given perfectly well formed input
it instantiates a lexkcalconizaine actor that encapsulates the potentially ambiguous readings of the first word of the text as accessed from the lexicon and corresponding word classes
the individual behavior of words is generalized in terms of word classes which are primarily motivated by governability or phrasal distribution additional criteria include inflection anaphoric behavior and possible modifiers
the parser is started by an analyze message directed to a parseractor which is responsible for the global administration of the parsing process cf
into the new phraseactor by copyheadfor and copymodfor messages respectively cot ying enables alternative attachments in the concurrent system i.e. no destructive operations are carried out
partial structures were organized such that a message which could be successfully processed at a larger structure was not forwarded to any of its constituent parts
if we were to allow for unrestricted backtracking we would just trade in run time complexity for space complexity for a more detailed discussion cf
norice that several attach messages can be received by a phrase because the searchttead messages are evaluated in parallel by its word actors
a specialized actor type called phrase actor comprises word actors which are connected by dependency relations and encapsulates inh rmation about that phrase
pa is used when the main verb represents a slate and pv for the verbs of type event or action
in this paper we propose a statistical dialogue analysis model based on speech acts for korean english dialogue machine translation
although dialogue corpus are relatively small the experimental results showed that the proposed model is efficient for analyzing dialogues
to differentiate such cases a translation system must analyze the context of a dialogue
however it is more efficient and robust and easy to be scaled up
we believe that this kind of minimal approach is more appropriate for a translation system
in equation NUM one problem is to search all possible uj that ui can be connected to
in utterance NUM according to the ra diagram in figure NUM b may request confirm or requestil formation
in utterance NUM a does n t know the possible room sizes hence asks b to provide such information
each utterance in dialogues was annotated with speech acts sa and with discourse structure information ds
given the focus marked output of the sentence planner the surface generation module consults a ccg grammar which encodes the information structure intonation mapping and dictates the generation of both the syntactic and prosodic constituents
other problems faced by this system have to do with the ability of the user to handle the decisions that are required of them
an implementation of a more suitable rule based classification of derivates and the unlimited number of semantically transparent compounds fails due to the lack of algorithms for their sound semantic classification
assuming the frequency threshold tlr q as NUM the strings which co occur with str more than twice are extracted in the second step
when the final details of the user interface and the other software components are worked out the completed prototype will be implemented on a tablet based pc and will be field tested with current users of the communic easetmmap
second the appropriateness of the grammar can be tested by determining how often the grammar s output matches the interpretation provided by the communication partner in the video sessions containing interpretations by the partner or by a human faced with the same sequence of words for the raw data to which interpretations have been added
in addition it has been used to validate expected sentence structures validate the expectation that the core vocabulary will comprise most of the input allow us to better anticipate the spelled vocabulary and validate input expectations
we have motivated and described a system that is under development via a joint venture between the applied science and engineering laboratories of the university of delaware and the dupont hospital for children and the prentke romich company
our system functionality has been determined by a collection of transcripts from communic easetmusers we have collected both raw keystroke data so that we can establish the range of input we expect from the population and keystroke data from videotaped sessions where interpretations of the keystroke data are provided by a communication partner
suffice it to say that regular abbreviation expansion is not a viable option for the population being considered here
children who use aac and have these kinds of difficulties face additional problems over speaking children with the same impairments both because they have additional obstacles in accessing language elements i.e. language elements must be accessed through a device and because language and literacy acquisition are not well understood in children who use aac
because the icons are rich in meaning and associations a small number of icons keys can be used to represent a large vocabulary where each item can be selected using a memorable sequence of NUM NUM icons
in addition to problems that make speaking difficult aac users often have difficulties in coordinating extremities so typing on a standard keyboard may be impossible and access to large keys is often very slow
c the analysis fails and probably for the reason of the incompleteness of the grammar it can not say anything about the input sentence
the grammar of the system is composed of metarules representing whole sets of rules of the background formalism called robust free order dependency grammar rfodg
the error anticipating rules are marked by a keyword negative at the beginning of the rule and are applied only in phases b and c
this activity is being performed for all sentences in the selection or for all sentences from the position of the cursor till the end of document
fortunately there is the possibility to use a concept of dynamic data exchange dde for the communication between programs in the microsoft windows environment
whenever a metarule describing syntactic inconsistency is used during the parsing process a negative symbol is inserted into the tree created according to the grammar
b the macro creates a message box with a warning each time there is an undesired result of grammar checking either there was no result or the sentence was too complicated
that is the reason why free modifiers at the end of our sample sentence create a great number of variants of syntactic structures and thus make the processing longer and more complicated
this example and also other test data showed that the main source of ineffectivity are clauses with a big number of free modifiers and adjuncts rather than complex sentences with many clauses
if we look more closely at the number of ambiguities present with individual words we notice that the most ambiguous word is the word abbreviation ing
while the assumption is reasonable for some languages such as english it can not be applied to all the languages especially to the languages without word delimiters
through the method various kinds of collocations induced by key strings are retrieved regardless of the number of units or the distance between units in a collocation
most of them are technical jargons related to computers and typical expressions used in manual descriptions although they vary in their constructions
the table indicates that arbitrary length of collocations which are frequently used in computer manuals are retrieved through the method
since this method generates all n character strings appeared in a text the output contains a lot of fragments and useless expressions
let the string be str the adjacent words wl wn and the frequency of str freq str
most of the strings extracted in this stage are meaningful units such as compound words prepositional phrases and idiomatic expressions
although the method described in this paper is applicable to either kinds of languages we have taken english as an example
by the use of each string derived in the previous stage this stage extracts strings which frequently co occur with the string and constructs them as a collocation
fragmental strings such as a local and area network are filtered out with these procedures because their entropy values are expected to be small
while one could conceivably train the interviewer to use a restricted vocabulary the interviewee s responses are much more difficult to control or predict
the present paper demonstrated that evidence on text structure enhanced the performance on the identification of topical words in texts which is based on a probabilistic model of text categorization
two major benefits of using text structure in topic identification are an improvement in effectiveness and a considerable reduction of the text volume necessary for the correct identification of text topics
rather than to use a measure for within document similarity the present approach chose to use a similarity measure between the text and its title to determine the structure of text
since the test sets we used in the later experiments of topic identification ranges from NUM to NUM characters in length the title block similarity is measured only for the relevant sizes
this would mean that title indicating terms are scattered more evenly over the text and thus it becomes all the more difficult to demarcate between relevant and irrelevant parts of the text
though the use of paragraph achieved better results at some points NUM NUM and NUM NUM than other approaches the overall performance is not outstanding compared to either flm or plm
NUM denotes a set of news articles between NUM to NUM character long NUM means news articles between NUM to NUM character long and similarly for others
the distribution of similarity measurements for large texts in fig NUM suggests that the similarity distribution for large texts tend to be less skewed to the left than that for short texts
thus at NUM for instance its performance on NUM NUM character texts drops by NUM compared to the full text approach but gradually improves as the value of j increases
sude ni kiev shi kiev at resident staff office obj open plan disclose did already kiev city tookyoku no kyoka mo eta to iwu
re conc awe ght construct can only occur as part of a text concept description when it also contains a construct an r tel cl ca where conc m subsumed by one of the c s if thin m not the case rcount refers to a concept being related via a relationship rel which m not m the range of this reta
both solved constraints in NUM describe infinite sets of solutions which arise from freely instantiating the remaining context variables by arbitrary contexts
in addition terminology dictionaries are hilly variable in the coverage
there are more than NUM NUM bi gram candidates in cw corpus
figure NUM shows the approximate recall precision curve of terminology phrase extraction
table NUM presents some example words with highest correlation coefficient
unfortunately the identification of terminology is a hard work
tri gram and NUM gram candidates are processed in the same way
these words should be extracted from the new word tables
rminology words are divided into two subsets and treated respectively
one of these mapstm the communic ease map tm was developed for users chronologically NUM or more years of age with a language age of NUM years
so it is important to extend and modify the existing knowledge corresponding to the purpose of use
they are not found in dictionaries are very large in number come and go every day and appear in many alias forms
the second and third spanish experiments s e s s s s require in addition pre tagged spanish training text using the same tags as for english
t ossessivcs when an un assitic l proller ll i ill stands in a possessive r lation to a n rganisation post then th name is classiti xl as all organization e.g.
the second and third japanese experiments j e j j j j require in addition pre taggcd japanese training text using the same tags as for english
st or examtlh b itsstoning ford mol or co has ah eady i cn lassi ie l as a c nnl any nam its scmanti rei r s ntation will be something like company e23 name e23 ford motor co
as defined for muc NUM the first three of these are proper names the fourth contains some expressions that would be classified as proper names by linguists and some that would not while the last two would generally not be thought of as proper names
np 0kgan np 0rgan np list loc np names np cdg np 0rgan np list 0rgan np names np cdg np organ np names np names np names np nnp names np names np nnp punc nnp names np nnp
the first a tivity is orcf rcnc resolution an unclassified name may bc corefcrr d wil h t previously classified one tly virtue of which the lass of the unclassifi d
NUM risks and evaluations in the end however it is well to remember that every project to transfer a new technology to the work place is a risk
in step four potential users of the technology are exposed to some of its possible uses as much as possible seeing their own data flow through the system
null sophisticated grammatical input sophisticated writers are apt to want to use complicated grammatical constructions which may lie outside the processing ability supplied by the compansion technique
that is we must find a method that enables the user to select the lexical items that they wish to communicate
this would allow robotag to actually reconfigure its knowledge base of word lists and propose new features
robotag is a multilingual text extraction system that automatically learns to tag texts by observing its users
robotag uses a machine learning algorithm to discover features that the training examples have in common
in these experiments we only trained and tested on person place and entity tags
to fill in the tuple values robotag calls on the preprocessor as a feature extractor
this paper describes robotag an advanced prototype for a machine learning based multilingual information extraction system
these output target language segments are indexed in the chart based on the positions of the corresponding input source language segments
the conditional probability pr siw reflects the channel processing characteristics of the ocr environment
finally the wordobigram model and viterbi algorithm are used to determine the best scoring word sequence for the sentence
our experiments show that system performance is very sensitive to the value of a especially for real word error correction
for example suppose that source word flag is recognized as flo by an ocr device
seventy pages of ziff data in the test set were printed in NUM point times font
the second and third feedback steps have only slight effect on the error reduction rates
the system created a lexicon and collected word bigram sequences and statistics from the training data
the resulting lexicon contained about NUM NUM words these were indexed using NUM NUM letter n grams
essentially there are two types of word errors non word errors and real word errors
the architecture of the word correction system for ocr post processing is given in figure NUM
l p spf fsv pf f under the indicated color constraints three so lutions are possible fsv pf ax l p
peter does too such a discourse presents us with a case of interaction between ellipsis and focus thereby raising the question of how dsp por for ellipsis should interact with our por for focus
since c substitutions have two parts a term and a color part we need two kinds m t n for term equations and c c d for color equations
things get even more complicated when languages with relatively free word order such as german are taken into account
the distribution strategy of the message incorporates the syntactic restrictions for the appearance of a reflexive pronoun and its possible antecedent
all these predicates form part of the computation process aiming at the resolution of anaphora as described in section NUM
new messages with phase 2a are sent their number depends on how many modifiers of the head exist
in phase NUM the message reaches the finite verb bestiickt where two new instances of the message are created
on the other hand its lack of an equally thorough treatment of complex syntactic constructions makes it inferior to gb
a in phase 3a the sentence delimiter s acquaintances focus and potfoci are tested for the anaphor predicates
in NUM the anaphor precedes its antecedent which is governed by the head that d binds the anaphor
structural constraints are necessary conditions but additional criteria have to be considered when determining the antecedent of an anaphor
these constraints incorporate categorial knowledge about word classes and morphosyntactic knowledge involving complex feature terms as used in unification grammars
then the predicate match2 unifies upperarg with the restriction of the flmction lowerarg to the context s in d2 lcb dl vl d v rcb a subset of lowerarg
instead two other routes have been tmrsued NUM the integration of further disambiguating knowledge and heuristics into the system or NUM the general ion of a single semantic represent alion that summarizes all the interi retations in the hope that the application task will force a distin lion between the int erpretations only in few cases
bj b NUM x x b rcb for each edge j starting in v bl NUM NUM bn x el NUM NUM era if a vertex v h s already been encountered the only action required is to connect the edge information on v s predecessor w with the edge information already present on vertex v
adjunct on ambiguities arising from attachmen of p ps adjectives adverbial sllb clauses and other modifiers coordinatkm ambiguities NUM role assignment ambiguities arising fi om scrambling arnhiguities arising from multi part of speech words a subcase of this type of ambiguity is tit treatment of mlknown input words
a packed shared forest for an input string a obeys the further constraint that there must be at most one vertex for each grammar symbol and substring of a thus if a consists of n words there will be at most k n NUM vertices in the parse forest for it k being constant
if a function lcb a xl b x2 rcb replaces a discourse referent in a packed udrs this intuitively means that the argument slot is filled by xt in reading a and by x2 in reading b as an example for a packed udrs consider the following representation for i saw every man with a telescope
let d2 be the context set lcb dl d rcb at e let upperarg be an argument as provided by the semantic rule corresponding to edge e let lowerarg be an argument as attached to the vertex w
figure NUM lexical entries and a sample derivation in lud
a plugging is a bijective function from holes to labels
the context of the result is the context of the functor
the instance is related to its two arguments by argument roles
the syntax of lud conditions is formally defined as follows NUM
ambiguities introduced by thesc may be resolved by a resolution component
thus our component has to pipe certain kinds of information like prosodic values
as shown in section NUM the lud description language has a well defined interpretation in drt
the deep level syntactic and semalttic german processing of verbmobil is also done along two parallel paths
the verbmobil system is a translation system built by some NUM different groups in three countries
most requirements for other functions such as machine translation or optical character recognition must be met outside the tipster architecture
the architecture has been designed to meet a large number of text handling requirements for cia dia and nsa
items of specific types such as personal names places or organization names for example can be located in the text by appropriate annotators and the text locations and data types can be passed to any other component or part of the application through annotations for fiwther processing or viewing
it meets however only those requirements having to do with document detection and information extraction functions
on the one hand proper names and pronouns do not tolerate determiners though admittedly the definite article occurs in some proper names while mass nouns and count nouns do
the set of aggregates accruing to the formation of aggregates from elements of a background set has the algebraic structure of a complete join semi lattice with a unit and without a zero
my purpose here is to pull together the observations which have been made and expand the empirical base so as to arrive at a preliminary formulation of the regularity or regularities involved
such flexibility accounts for why it is that when different piles of leaves are touching different bundles of wires the following sentences due to lauri carlson are true
on the other hand there is a large class of words which pattern morpho syntactically with count nouns yet their denotations have parts which also fall within the same noun s denotation
as is well known not only can the members of a collective come and go with the collective remaining intact but the very same people may make up two distinct collectives
ostler and atkins NUM p NUM common nouns for plants which humans eat can be used to denote the largest aggregate of those parts considered suitable for human consumption
personal names can be converted into common nouns with the concomitant shift in meaning to denote the set of people who have the proper names in question as a proper name
the former requires the useof a partitive expression a slice of pizza and a piece of cake whereas the latter accrues to the count noun conversions of pizza and cake
i will make use of two values for the when slot immediate and posterior
in reiter s default logic this is expressed with the following normal default rule
class skickade isa verb requires lex skicka
class value nonmon default x immediate x x
note in particular that the above conditions on a nonmonotonic sort implies that NUM may be fail
in particular it is dependent on having a decidable unification operation and subsumption check
class the name of the class isa its parent in the hierarchy requires a structure
the second part of this definition contains some well foundedness conditions for a nonmonotonic sort
in contrast the successive abstraction scheme determined the back off weights automatically from the training corpus alone and the same weight setting was nsed for all test corpora yielding results that were at least on par with those obtained using linear interpolation with a globally optimal setting of contcxt independent back off weights determined a posteriori
the verb kulehsupnita in korean also may be translated differently depending on context
each time a begin end pair is made any begin or end tags between the pair can not be used or the resulting tags would overlap
we plan to do further experiments which address how the use of deg this directed feedback can result in rapidly learned tagging procedures utilizing fewer tagged texts
the system had to process a large number of texts as well as provide the ability to visualize learning results and allow feedback to the learning system
in this paper we described an efficient dialogue analysis model with statistical speech act processing
this model is weaker than the dialogue analysis model which uses many difference source of knowledge
aux verb represents the modality such as want possible must and so on
in phase NUM the message is forwarded from its initiator to the head which d binds the initiator
its distribution strategy incorporates the syntactic restrictions for the appearance of both elements involved anaphor and antecedent
in phase NUM the message reaches the finite verb hat where new instances of the message are created
its message passing mechanisms constitute the foundation for expressing specific linguistic protocols e.g. that for anaphora resolution
NUM vs NUM hence that modifier is the antecedent of the reflexive
in phase NUM it takes the path to the sentence delimiter of the current sentence no effect
usually the antecedent of a reflexive pronoun is the subject of the clause to which the reflexive belongs
we will reconsider these constraints in section NUM where our grammar model is dealt with in more depth
denotes the value of the property attribute at object and the symbol self refers to the current lexical item
NUM s where si is a possible speech act for the utterance ui
such model is weaker than the dialogue analysis model which uses many difference source of knowledge
NUM we also generate a table called count of all unambiguous parses in the corpus along with a count of how many times this parse occurs in the corpus
we refer to the unambiguous tokens in the context as llc left left context lc left context rc right context and rrc right right context
so if the rule right context constraint subsumes the top level feature structure or the stem feature structure then the rule succeeds and is applied if all other constraints are also satisfied
for instance in turkish postpositions have rather strict contextual constraints and if there are tokens remaining with multiple parses one of which is a postposition reading we delete that reading
our system with the structure presented in figure NUM has three main components NUM the preprocessor NUM the learning module and NUM the morphological disambiguation module
among these problems the most crucial is the second one which we believe can be solved to a great extent by using root word preference statistics and word form preference statistics
it is specifically applicable to languages with productive inflectional and derivational morphological processes such as turkish where morphological ambiguity has a rather different nature than that found in languages like english
this module also performs a number of additional functions it groups lexicalized collocations such as idiomatic forms semantically coalesced forms such as proper noun groups certain numeric forms etc
our approach also uses a novel approach to unknown word processing by employing a secondary morphological processor which recovers any relevant inflectional and derivational information from a lexieal item whose root is unknown
our system combines corpus independent hand crafted constraint rules constraint rules that are learned via unsupervised learning from a training corpus and additional statistical information from the corpus to be morphologically disambiguated
this question can only be answered by a lexical approach an approach that pleasingly lends itself to efficient experimentation with very large amounts of data
figure NUM a the corpus frequencies of all binary stress NUM grams based on NUM NUM million syllables with secondary stress mapped to weak w
the question of whether more probable word sequences are also more rhythmic can be approximated by asking whether sentences with lower perplexity have lower stress entropy rate
the NUM gram estimate matches quite closely with the estimate of NUM NUM bits that can be derived from the distribution of word stress patterns excerpted in figure 3b
to say nothing of the psychoacoustic biases this methodology introduces it relies on too little data for anything but a sterile set of means and variances
degrees of stress arise from variations in the amount of energy expended by the speaker to contract these muscles and from other factors such as intonation
monosyllabic function words such as the and of usually receive weak stress while content words get one strong stress and possibly many secondary and weak stresses
the training procedure entails simply counting the number of occurrences of each n gram for the training data and computing the stress entropy rate by the method described
the experiments in this section were repeated with a larger perplexity interval that partitioned the corpus into NUM bins each covering NUM units of perplexity
examining the NUM gram frequencies for the entire corpus figure 3a sharpens this substantially yielding an entropy rate estimate of NUM NUM bits per syllable
cat message admin plandoc message name rda runid r regl class refinement action activation equipment type all dlc csa site NUM
usually the counterexamples objects which do not belong to the class are given too
the six rules correspond to the stem grade changes the last one corresponds to the stem end changes
the initial description hypothesis takes into account only the characters correponding to the changing sound
because of that the technique of inductive supervised learning is the most suitable for the current task
the main specifying operation in case of string data is the adding an attribute extending the context
because the final sorting operation dominates the order of the resulting messages plandoc sorts the message list from the lowest rank attribute to the highest
the result after applying immediateexplanation to the two structures above is shown below
the resources are typically put to use for the creation of consistent and large lexical databases for parsing and machine translation as well as for the treatment of lexical syntactic and semantic ambiguity
this is necessary because they often really are two separate concepts as in pork pig and each sense may have different synonyms pork meat is only synonym to pork
as compatibility with the princeton wordnet and eurowordnet is a major construction criteria of germanet german can now finally be integrated into multilingual large scale projects based on ontological and conceptual information
another additional pointer is created to account for regular polysemy in an elegant and efficient way marking potential regular polysemy at a very high level and thus avoiding duplication of entries and time consuming work c f
the amount of polysemy is kept to a minimum in germanet an additional sense of a word is only introduced if it conflicts with the coordinates of other senses of the word in the network
a wordnet for german has been described which compared with the princeton wordnet integrates principle based modifications and extensions on the constructional and organizational level as well as on the level of lexical and conceptual relations
meronymy has a the part whole relation holds only for nouns and is subdivided into three relations in wordnet componentrelation member relation stuff relation
hopefully there are decidable fragments of the context unification problem that are empirically adequate for the phenomena we wish to model
finally within six months to a year from the integration of the technology the maintenance and support of the system should be transitioned to the user s organization
at the same time the project leader must be aware that most of these people have other jobs to do and their time should be asked for only when necessary
at the same time even an apparently failed project can have good results if the causes of the failure are understood and the knowledge incorporated into later planning
given this state of affairs cots products do not seem even yet to be an assured answer to the need for a well supported suite of tools employing tipster technology
for this reason at this stage the interface should be easy to develop easy to change and familiar in some aspects to what users know already
the best solution seems to be for the technologist to offer as many reasonable choices as possible for the application of the technology allowing the user to choose or fine tune the actual work flow
the existence of an established set of interfaces for this group of technologies allows applications to be designed more quickly and with some accumulation of knowledge across vendors of the best ways to use them
it is based on the format of a slot grammar sg a slight theory variant of dg
upon instantiation of the corresponding word actor a searchpronanlecedent message will be sent
these results prove to be better than those reported earlier using different approaches
high performance segmentation of spontaneous speech using part of speech and trigger word information
within this region therefore several neural nets yield extremely good performance
a indicates the location of the error
NUM of the text there is a segment boundary
in figure NUM we plot the f score against the threshold
the number of hidden units h ranged in our experiments from NUM to NUM
two scores were assigned to each word w in the transcript according to the following formulae
our method applies an artificial neural network to information about part of speech and trigger words
we describe and experimentally evaluate an efficient method for automatically determining small clause boundaries in spontaneous speech
increasing the sampling ratio gives the learning system more examples of things that should not be tagged reducing the number of false positives which increases precision
we are planning to explore additional tagging tasks besides names in multiple languages such as chinese thai spanish as well as english and japanese
a number of demonstration projects use web browsers for this purpose
rohotag is flexible in its ability to work with multiple languages
finally the following collocation is produced refer to the manual for specific instructions on the broken lines in the collocation indicates the gaps where any substitutable words or phrases can be filled in
the ynl act ic d nition of prioritized circumscription i s as follow s
in circumscription we give a prefcrclme of der over logical interpretations and consid r
in addition some variations in the transformation based learning algorithm are suggested by this application that may also be useful in other settings
therefore what affects the final result of a computation is when one chooses to explain default rules and not the order of the unification operations occurring between such explanations
the subsumption order is assumed to be a semi null lattice and permits computing a unifier denoted a n b corresponding to the greatest lower bound for every pair of elements within it
this alternate definition is useful for applications where the simplification of nonmonotonic sorts by each unification is expected to be more expensive than the extra work needed to explain a sort whose nonmonotonic part is not simplified
one could for example imagine cases where one would want different nonmonotonic rules to be explained after a completed parse a generation or after resolving discourse referents
examples of other subsumption orders that might be useful are typed feature structures feature structures extended with disjunction or simply an order based on sets and set inclusion
note that although the when slot in the definition of a nonmonotonic rule allows the user to define when his rule is going to be applied we will still have an order independent nonmonotonic unification operator
this system first uses heuristics to find maximal length noun phrases and then uses a grammar to extract terminological units
thus lexical rules appear to be making a limited contribution in determining basenp chunks but a more significant one for the partitioning chunks
it is worth glossing the rules since one of the advantages of transformation based learning is exactly that the resulting model is easily interpretable
the context is added initially words then possibly some non terminals one element at a time on either side of l the revised rules are re validated and the cycle is repeated until no further progress is possible
in effect we are labeling every node with a list of the sets to which it belongs
finally our experiments have focused on proper name tagging but robotag is not limited to this
figure NUM shows a screen shot of a portion of a decision tree trained to produce begin tags
robotag can thus suggest what should be tagged after having received some training through observation of the user
both kind of links are relevant for message retrieval
they can be exchanged for lexicons in other languages
the robotag system is particularly well instrumented for exploration of different learning system parameters and inspection of the induced tagging procedures
NUM shall we go for a dip
wordnet was converted to a format suitable for the wordkeys software
among the systems using natural language we distinguish two different approaches
table NUM value determination for message ranking
these messages are automatically indexed and integrated in the system s database
evaluation equipment has been purchased with a donation from the anonymous charitable trust
therefore the first step in the matching process seeks to divide the text up into a set of non interacting sections
consider however the case in which a noun is preceded by a determiner and an adjective
thus different knowledge sources have to be integrated in the course of an incremental text understanding process
further abstraction is achieved by organizing word classes at different levels of speciticity in terms of inheritance hierarchies
after forwarding a each searehhead message evokes the check of syntactic and semantic restrictions by the corresponding methods
before the new composite phrase can be built the address of the next container actor must be determined
the newin message subsequently creates a new phraseactor that will encapsulate the word actors of the new phrase
major drawbacks concerned an overstatement of the role of lexical idiosyncrasies and the lack of grammatical abstraction and formalization
for illustration purposes we here introduce the protocol in a diagrammatic form figs NUM to NUM
null the analysis of texts as opposed to sentences in isolation requires the consideration of discourse phenomena
as mentioned in section NUM NUM each of these defaults will have one of threepriority values strong weak or very weak
the default is normally piece but this can be over ridden by an explicit entry for n s default classifier in the lexicon NUM piece of furniture
the final type of classifier spe cial is rare classifiers which force an uncountable interpretation of even countable nouns for example kire slice
this allows us to give a uniform treatment of noun phrases such as NUM and NUM during english generation even though their japanese structure is very different
given a sentence to be corrected the system decomposes each string in the sentence into letter n grams and retrieves word candidates from the lexicon by comparing string n grams with lexicon entry n grams
n r exainple if c measures n s attribute then the resulting noun phrase will be indefinite by default a height of lore or a price of NUM yen
as there is no direct fit between english and japanese it is necessary to categorize the japanese and english classifiers and to define rules which will enable effective machine translation
results are given in tables NUM NUM and NUM in all cases we considered only those strings whose correct forms are literal words not alpha numerics
the first oi neital classifiers are those that have no special meaning of their own but are used only to quantify the denotation of a noun
however we believe they will prove to be useful not only in improving the quality of ocr processing but also in enhancing a variety of information retrieval applications
in particular all words or ocr strings are indexed by their letter trigrams including the beginning and end spaces surrounding the string
in these respects the basic ideas of the qlf formalism are quite similar to lud
another property of the verb s semantics is that it introduces the top hole of the sentence
in this paper we presented a first step towards the realization of a system using automata based theorem proving techniques to implement linguistic processing and theory verification
this at least separates the actual processing issues e.g. parse from the linguistically motivated modules e.g. ecp
in fact since we want the lexicon to be represented as several definite clauses we can not have xbar as a simple constraint
note that top down tree automata do not have this property deterministic top down tree automata recognize a strictly narrower family of tree sets
in fact the above automaton was constructed by a simple implementation of such a compiler which we have running at the university of tiibingen
this procedure is called new word extraction
another relevant research is statistical collocation extraction
second the computation is quite simple
they are extensively used in scientific articles
figure NUM recall precision curve of phrase table
these words were further categorized manually
current research only concerns word pairs
the interface has been augmented with several displays that allow for a thorough investigation of the learned tagging procedure
the matching algorithm must decide the best possible pairing of the begin and end tags for each tag type
all phrases in the active container send a search message to the current context container that forwards them to its encapsulated phrases
if none of the receipts signals success to the handler the search head protocol will be followed by moditier search or backtracking NUM protocols not shown here cf
primarily this change in perspective was due to large amounts of artificial ambiguities that could be traced to blind parallel computations with excessive resource allocation requirements
the performance of a revised version of the chart parser which also handles these cases is given as cp disc syn con in the figures
there are several arguments why computational linguists feel attracted by the appeal of parallelism for natural language understanding for a survey cf
p neuhaus is supported by a grant from dfg within the freiburg university graduate program on lluman and artificial intelligence
6this can only serve as a rough estimate since it does not take into account the exploitation of parsetalk s concurrency
this led us to restrain from unbounded parallelism and rather guarantee contluent behavior by the design of the parsing algorithm
there is a great deal of scope for tailoring descriptions to a user s knowledge here for example illustrating the size of the aye aye with the fox might be appropriate for a user who is familiar with the fox however this illustration might not be appropriate for someone located in australia since the fox is not found in australia
peba e text generation system il l idocument done i figure NUM a direct comparison as generated by peba ii rant and the purpose of the text is to determine their similarities and differences based on both their relationship within a taxonomy of animals their lowest common ancestor and their attributes
the difference between an illustrative comparison and a clarificatory comparison is that in an illustrative comparison the comparator entity although usually of a similar type in this case an animal may only share one attribute with the focused entity and is not necessarily similar in any other way to the focused entity
the geographical location of the user can also play an important role for example in the texts that we have examined squirrels are often used as comparators but australians are not necessarily familiar with the features of squirrels and some north americans might only know of the existence of black squirrels
for example if a user who is not familiar with sheep requests a description of the sheep and the system describes the sheep by informing the user of its similarities with the goat and not their differences then the user could be led to believe that the two animals are more similar than they are in reality
we could try to generate such clarificatory comparisons from first principles when we have to describe some entity e we could search the knowledge base for entities which share properties with e and then use some mechanism to determine whether there is any chance that the two entities might be confused
knowledge base by means of a clause of the following form hasprop sheep potential confusor goat then whenever we have to describe the sheep we know immediately that it has a potential confusor in the goat and invoke a discourse strategy that makes an explicit comparison between the two entities
gies we are faced with two interdependent questions when do we decide to describe an entity by comparing it to another entity and how do we decide which type of comparison to use recall from earlier that peba ii can address two different discourse goals requests to describe some specified entity and requests to compare two specified entities
the aim of the corpus analysis was to reverse engineer the comparisons found in animal descriptions in order to answer the following questions what entities are compared in descriptive texts and how do they relate to each other
main verb is the type of the main verb in the utterance
f denotes the syntactic pattern and freq denotes the frequency count of its argument
p uilut u2 u i l means the probability that
nole lhal although t he t reference NUM seems lo l e alq li able il is nol aclually used since the stronger prefcre nce NUM overrides lhe preferen c NUM
we responded to this requirement by developing an asymmetric interface where any necessary complex operations were moved to the interviewer s side
this allows us to rapidly put a recognition capability in place and was the strategy used for our serbo croatian english system
the selective collection approach presupposes a preparation interval prior to deployment and can be a follow on to a system based on assimilation
the percolation of slash across head domains is lexically determined
there is a danger in raising more than the sfs
we now give a general description of the compilation process
we assume that a reduction takes place along with selection
we also obtain from this criterion a notion of local completeness
it merely constrains the modified head to have an unsaturated subs
in the first phase we raise all the sfs
initial trees are those that have no such frontier nodes
class value nonmon coherence a immediate a none a none nonmon completeness a posterior a any no value fail
there are three parts to the scoring function for a pair
specification and can be avoided with further development of the detection component
there was a total of NUM verbal prepositional frames listed in either dictionary
the restriction in our nonmonotonic rules that NUM c fl is similar to the restriction into normal default rules and captures the important property that the application of one nonmonotonic rule should not affect the applicability of previously applied rules
i will start with two observations regarding the definitions given in section NUM first it is possible to generalize these definitions to allow the first component of a nonmonotonic sort to contain substructures that are also nonmonotonic sorts
the reason for only checking that a s instead of s c a is that future unifications can restrict the value of s into something more specific than a and thus may make the default rule applicable
the actual nonmonotonic rule occurring within the sort is a pair consisting of the when slot and the last part of the nonmonotonic definition with the parameter variables instantiated according to the call made by the user
in other words source semantics and target semantics must be identical up to the positions of the contrasting elements
we illustrate them here by some examples focus bearing phrases are underlined NUM john speaks chinese
valency constraints are attached to each lexical item on which the local computation of concrete dependency relations between a head and its associated modifier is based
speaking in terms of dependency structure representations the head always precedes and thus transitively governs its associated modifiers in the dependency tree
we provide here a definition of d binding and two constraints which describe the use of reflexive pronouns and anaphors personal pronouns and definite noun phrases
if the search for an antecedent is successful a ref antecedentfound message is sent directly to the initiator of the search message which changes its concept identifier accordingly
l a pro features self agr pers ll ante features self agr pers
box NUM determines the candidate set of possible antecedents for pro nominal anaphors and thus characterizes the notion of teachability in formal terms
note that only nouns or personal pronouns are capable of responding to searchan ecedent messages and test whether they fulfill the required criteria for an anaphoric relation
it is also a striking fact that given the same linguistic phenomena structural dependency configurations are considerably simpler than their gb counterparts though suitably expressive
a in phase la the modifiers of the initiator s direct head are tested in order to determine if any of these modifiers have modifiers themselves
c l a pro features self agr num u ante features self agr nurn
the resolution of text level nominal and pronominal anaphora contributes to the construction of referentially valid text knowledge bases while the resolution of textual ellipsis yields referentially coherent text knowledge bases
a function pathmarker cpi j yields either plausible metonymic or imphmsible depending on the type of lmths the list contains
fig NUM referring to the already introduced text fragment NUM which is repeated at the bottom line of fig NUM
in sentence lc the information is missing that ladezeit charge time links up with akku accumulator
among NUM NUM NUM false negatives no resolution triggered though textual ellipsis occurs the ellipsis handler encountered NUM NUM NUM
as this can be excluded the remaining concepts associated with the current forward looking centers namely time unit pair and power need no longer be considered
textual ellipsis has only been given insufficient treatment within the centering model in terms of rather sketchy realization conditions as opposed to the more elaborated constraints for pro nominal anaphora
the higher error reduction for the former is partly due to the fact that the part of speech basehne for that task is much lower
few of those dictionaries however are publicly available and few of those available are suitable for retrieval of unrestricted text
it compactly represents the infinite number of possible satisfying assignments
unification of nonmonotonic sorts would then correspond to merging two default theories into one single theory and our notion of explaining a nonmonotonic sort corresponds to computing the extension of a default theory in default logic
when several key words are typed in the message retrieval algorithms is working with an or link between search words
for example messages containing synonyms of key words receive a high rating those containing hypernyms are assigned a lower rating
a path is the concatenation of semantic links that are used to get from the input key word to the index word
in turkish even though there are ambiguities of such sort the agglutinative nature of the language usually helps resolution of such ambiguities due to restrictions on lnorphotactics
currently the left and right contexts can be at most NUM tokens hence we look at a window of at most NUM tokens of which one is ambiguous
we have also attempted to learn rules directly without applying any hand crafted rules but this has resulted in a failure with the learning process getting stuck fairly early
on the other hand a text where each token is annotated with all possible parses NUM the recall will be NUM NUM but the precision will be low
NUM if the threshold for the most specific context falls below a given lower limit the learning process is terminated
our approach starts with a set of corpus independent hand crafted rules that reduce morphological ambiguity hence improve precision without sacrificing recall
this usually happens when the root of one of the forms is a proper prefix of the root of the other one
we consider as token as correctly disambiguated if one of the parses remaining for that token is the correct intended parse
pruning helps reduce over fitting of training data and improves classification accuracy on unseen examples
in addition most of these words are idioms or terminologies then can be extracted in the phrase generation phase
note that in the description below tel and rel denote the transitive and transitive reflexive closure of a relation rel respectively
these data structures are realized as acquaintances of sentence delimiters to restrict the search space beyond the sentence to the relevant word actors
al NUM church NUM chen NUM and we draw on these here
the various back off weights are combined in the process
thus this represented the best possible setting of back off weights obtainable by linear interpolation and in particular by linear deleted interpolation when these are not allowed to depend on the context
this will allow us to calculate an estimate of the probability distribution in context c2 which in turn will allow ns to calculate an estimate of the probability distribution in context ca etc
we have thus established a recurrence formula for the estimate of the probability distribution in context ck given the estimate of the probability distribution in context ck NUM and the relative frequencies in context ck
this is usually referred to as deleted interpolation
the experimental results are shown in figure NUM
this can be done in several different ways
another common difficulty is failure to work out the best uses of the technology before an application is chosen and a user organization signed up
despite all the careful testing it is inevitable that there will be some bugs in the software and these must be fixed immediately
however it also is being promoted to meet a number of other problems associated with the delivery of software to the end user
it was problems some of them not solved the first time which taught us what i have recorded in this short discussion
however users can take this stage as an opportunity to change the actual functionality of the software in effect to change the requirements
the tipster program freely advertises its involvement in document detection and information extraction through many informal contacts and a number of formal reviews and publications
the divorcing of the tipster technologies from the human interface will make it easier to insert them seamlessly into a variety of user desktop environments
at the same time the technologist being an outsider can not prescribe how the task should be done with the new technology
these have proven excellent opportunities to demonstrate the new technology without taking on all the additional problems of a very large and complicated development
the technologist is neither a servant nor a savior that is neither the user nor the technologist has all the answers
between these two extremes are nouns such as cake which can be used in both countable and uncountable noun phrases
there is a default classifier tsu piece which can be used to count almost anything
if n is parted or defaulted there axe two possibilities either if the dictionary entry for n has the default classifier pair then it will be used as the classifier or if n has no default classifier then a different translation is searched for in the dictionary and used instead
if n is fully strongly or weakly countable then the classifter is not translated individuate
in the rest of this paper we describe the overall organization of our trec NUM system and then discuss some experiments that we performed and their results as well as our future research plans
the question is are there uses for techniques such as compansion that avoid some of the above mentioned problems
the final results are produced by merging ranked lists of documents obtained from searching all stream indexes with appropriately preprocessed queries i.e. phrases for phrase stream names for names stream etc
the fundamental problem however remained to be the system s inability to recognize in the documents searched the presence or absence of the concepts or topics that the query is asking for
the head in such a pair is a central element of a phrase main verb main noun etc while the modifier is one of the adjunct arguments of the head
finally the system will not face the same sorts of input problems described in conjunction with the compansion system
the project can best be thought of as a rate enhancement technique used in the context of a writing tool
thus there is a limit to the sophistication of grammatical constructions that are possible to disambiguate in telegraphic input
specifically we are interested in query construction that uses words sentences and entire passages to expand initial topic specifications in an attempt to cover their various angles aspects and contexts
the protocol level of text analysis encompasses the procedural interpretation of the grammatical constraints from section NUM we will illustrate the protocol for text ellipsis resolution of
at the top sbo o denotes the basic relation for the overall ranking of information structure is patterns
morphological analysers are available in the public domain
we discuss techniques based on constraint logic programming that are applicable to that problem in the next section
in the base case relations defined by atomic formulas are shown to be recognizable by brute force
in this paper we describe an approach to constraint based syntactic theories in terms of finite tree automata
it is one of our goals to show that this is the case in linguistic applications as well
in general all the techniques which apply to tree automata are straightforward generalizations of techniques for fsas
the translation is provided via the weak monadic second order theory of two successor functions ws2s
we can avoid having to compile the whole lexicon by having separate clauses for each lexical entry in the clp extension
to demonstrate how we now split the work between the compiler and the clp interpreter we present a simple example
the first two tools operate in sequence planning respectively the high level rhetorical structure of the text and the low level grammatical details of the sentences
drafter then has facilities for reading the interface definition produced by visualworks in smalltalk and finding all the objects relevant for the generation of the instructions
it is easiest for the technical writer if the process starts by defining the interface to be documented with some interface building tool
it uses this information to define a set of object and action entities in the drafter knowledge base for use in text generation
the text planner determines the content and structure of the text and the tactical generator performs the realization of the sentences
drafter is an interactive tool designed to assist technical authors in the production of english and french end user manuals for software systems
figure NUM sentences containing refer to
despite the fact that spite is frequently used with in mutual information between in and spite is small because in is used in various ways
this is based on the idea that adjacent words will be widely distributed if the string is meaningful and they will be localized if the string is a substring of a meaningful string
most of the learning algorithms assume the attribute value pairs as input with fixed number of attributes and known set of values lbr every attribute
our future work will involve bringing these various factors together into one integrated formalism
wl and w represent respectively the frequencies of their unconditional occurrence
these functions are piecewise linear mappings of the normalised co occurrence frequency and are used as scaling factors
table NUM shows some pairs of words that exhibit differing degrees of influence on each other
in particular the first paragraph strategy is outperformed by the full text method on texts with more than NUM characters
a problem with the plm approach is that a segment from which topical words are chosen is too small for short texts
in rst the article would be analyzed as having an issue structure which consists of one nucleus and some adjuncts
our approach to the reduction problem above is to use a text structure to demarcate between relevant and irrelevant parts of text
the test set NUM on the other hand consists of larger articles which are between NUM and NUM character long
contrary to our expectation the results of the experiments cast some doubt as to the usefulness of paragraphs for topic identification
fig NUM shows a particular implementation of the present method full text model which operates in the emacs environment
the one we will use is to construct a numerical random variable with a uniform distribution that has the same entropy as the non numerical one
without the at t bell laboratories tts system and the patient advice on its use from julia hirschberg and richard sproat this work would not have been possible
that is information structure refers to how the semantic content of an utterance is packaged and amounts to instructions for updating the models of the discourse participants
the problem of context based ocr word error correction can be stated as follows let l lcb wl w win rcb be the set of all the words in a given lexicon
formula NUM in each feedback step the system first generates a character confusion probability table by comparing the ocr text to the corrected ocr text from the last pass
ideally each word w in the lexicon should be compared to a given ocr string s to compute the conditional probability pr wls
the system we have created uses information from a variety of sources qetter n grams character confusion probabilities and word bigram probabilities to realize context based automatic word error correction
finally if a noun phrase of this type is used to modify at other noun then it line is to lm converted to an adjective a lore
moreover using h al rules to tackle unattached pps by statistical model is also hellfful in improving the overall su cess rat since loom rules in l hase NUM work nmch b tter than default h ision in phase NUM
since local rules emph y the senses of head words termed as concepts we shouhl NUM roject each of v ul and n2 used by rules into one or several coi cepts which denote s correct word senses before apl lying local rules
features of v or nl n2 sometimes in licate the corre t attachment
instead of using pure statistical approaches stated above wc propose a hybrid approach to attack pp attachment problem
for exami le we classify verbs into active and passive and ontologicm cbusses of mental movement etc
icate oil the left hand presents tt subclass of concept ill the eon ept hierarchy e.g.
that is each pp in the corl ora has NUM een attached to an unique l hrase
in NUM the symbol f denotes frequency of a parti ular tuple in the training data
personal NUM ronouns in the n2 field are r lfiaced with perso t
all verbs are rel l u ed with their stems ill lowor as s
the group opinion and dehvers the result to the next blackboard knowledge base the slmsum knowledge base ts a common knowledge store compnsmg a text representation winch holds all texts in the system and an ontology of the concepts which are needed to deal with them
this gives rise to the support set for a given rule which we will denote sp z r
natural language processing techniques have been used before to assist automated speech recognition either in constructing better language models or as post processors
second the reported improvement cover corrections of vocabulary deficiencies not only true transcription mistakes due to weaknesses of the language model
note that only context free rules are used a context sensitive correction indication colon indication would fix the problem in the first line
we also consider substrings of l for validation purposes e.g. sl at the track of tumor on change in
this again is repeated until no further progress is possible that is no further changes to the rule set result
the correction box consists of a text alignment program a correction rule generator and a series of rule application and verification steps
at the time this report is written we collected nearly NUM transcribed dictations all in the area of chest x ray
in this paper the differences between traditional information retrieval and the requirements for text retrieval in a communication aid are highlighted
so if only referentially unambiguous conditions must be consulted in a proof a udrs theorem prover may be used
figure NUM a parse forest with a tree reading all edges used in dl are shown as broken lines
v is the set of vertices and d is the set of tree readings
the order relation forms a semilattice ow r l with one element lq
in the packed udrs approach the problem is handled by explicitly enumerating all possible readings
if lexarg does arg is unified with the fimction lcb dl NUM lexarg
ill general this notion of focus must be feint vised to individual argmnents
let us begin wil h a rough sketch of the arctfiteclure of the systmn
the paper describes a system which uses packed parser output directly to build semantic representations
a common such mapping is to let the interpretation of the phrase be the interpretation of its semantic head modified by the interpretations of the adjuncts
the description language and its formal interpretation in drt are described as well as its implementation together with the architecture of the system s entire syntactic semantic processing module
the key part of the paper section NUM showed how the linguistically sound lud formalism has been properly implemented in a near real time system
instead of trying to resolve ambiguities for example the ones introduced by different possible scopings of quantifiers the interpretation of the ambiguous part is left unresolved
an example is the linguistic observation that a predicate that encodes sentence mood in many cases modifies all of the remainder of the proposition for a sentence
compositionality may be defined rather strictly so that the interpretation of a phrase always should be the logical sum of the interpretations of its subphrases
differently from reyle s udrss however lud assigns labels to the minimal semantic element and may also be interpreted in other object languages than drt
it offers no easy solution because to provide software in a truly off the shelf condition to government users would in fact mean setting up a small business like unit to do software testing and upgrades promotion of products distribution integration support and maintenance all of which cost money over and above the initial investment in the software development
the free text management ftm project NUM for the national drug intelligence center ndic at the federal intelligent document understanding laboratory fidul is an example of a phase ii project devoted entirely to the experimental integration of tipster technologies into a near user environment for the purpose of engineering and trade off studies
the implementation in siemens tug grammar formalism was described together with the architecture of the entire semantic processing module of verbmobil and its current coverage
thirdly a subordination relation on the set of holes and labels constrains the number of interpretations of the lud representation in the object language drss
generally we have found that unless users are sufficiently concerned about the improvements in their task to invest time in learning about and supporting the application development they are a high risk user that is one who is likely to back out before a project is completed or who will not use and support the software once it is completed
there are considerable potential rewards which serve as important motivations to people involved in the process material incentives but also the satisfaction of building something that works and does useful tasks for people who need it
the mission of educating the user about the technology means being as honest as possible about what it can and can not do explaining clearly how the technology will fit into the user s environment discussing requirements on their time for development evaluation and training and making good faith estimates of maintenance efforts should a system become operational
government sponsors and initiators of the program in NUM could see clearly the inadequacies of the tools that analysts were working with at the time and could also see that the overload of text which analysts dealt with was only going to get worse given the proliferation of information sources and the increasing push in the intelligence community to tap those sources
the view advocated here is that of non specification
another common conversion is to that of units
NUM NUM this oil are viscous
some nouns have their grammatical number specified lexically
NUM NUM john and mary are leaving
NUM NUM those earnings is insignificant
those which do are also known as pluralities
let us begin with mass nouns denoting emotions
NUM NUM how many loyalties does dan have
the contextual probability p sils1 NUM si NUM is the probability that n utterances with speech act si is uttered given that utterances with speech act NUM NUM si NUM were previously uttered
assuming that the threshold tra io is NUM NUM first a string manual for specific instructions is produced as the inequation NUM is satisfied
this is just one such spanning tree for the overall equivalence class there are of course i coreference task defmition version NUM NUM and earlier
the scores themselves are obtained by determining the minima l perturbations to the response that are required to transform its corresponding equivalenc e classes into those of the key
when the response overlaps the key in such a way as to produce a non trivial set difference as in the following figure the response contains precision errors
the principal conceptual differenc e is that we have moved from a syntactic scoring model based on following coreferenc e links to an approach defined by the model theory of those links
note that the problem with the syntactic link wise scorer is that there are combinatonally many such spanning trees for a given equivalence class while keys only list one
the nodes that are siblings of nodes on the trunk the selected daughters are not elaborated further and serve either as foot nodes or substitution nodes
global completeness is guaranteed because the notion of elsewhere is only and always defined for auxiliary trees which have to adjoin into an initial tree
we do not assume atomic labeling of nodes unlike traditional tag where the root and foot nodes of an auxiliary tree are assumed to be labeled identically
without such atomic labels in hpsg we are forced to address this issue and present a solution that is still consistent with the notion of factoring recursion
on the other hand these constructions would have to be treated by multicomponent tags which axe not covered by the intended interpretation of the compilation algorithm anyway
repeat this step each time with n as the root node of the tree until no further reduction is possible
it is determined which trees are auxiliary trees and then the relationships between the sfs associated with the root and foot in these auxiliary trees are recorded
by first adjoining the tree t6 at the topmost domination link of t5 we obtain a structure t7 corresponding to the substring what to give to sandy
applying a reduction to an unspecified sf is also linguistically unmotivated as it would imply that a functor could be applied to an argument that it never explicitly selected
traditionally in tag auxiliary trees are said to be minimal recursive structures that have a foot node at the frontier labeled identical to the root
figure NUM a dilf0renl t j form
a possible justilication for the rule
figure NUM the translbrmation between s forms and b
ltow do we compose two such functions
we can produce six differenl scopings
extended dependency structures and their formal interpretation
t their types are indicated in brackets
finally the method low level parsing together with a procedure to ranlc alternatives obtained should be extended to other frames as well
evaluating the acqui d dictionary is not straightfoward linguists often disagree on the criteria for the complement adjunct distinction
mary is on it reminded that mary is reminded of the fact that
these parameters are used to rh racterize verbal nominal and adjectival sfs
note that the usage of the pronoun 4b is ungrammatical
unlike prepositional phrases pronominal adverb correlative constructs provide reliable cues for prepositional subcategorization
alternative sfs usually stem from an ambiguity in the attachment of the pronominal adverb pp
this paper presents a procedure to automaticafly learn german prepositional subcategofization frames fzom text corpora
NUM NUM two languages are spoken by many linguists
this constraint language can express equality and subtree relations between finite trees
our approach methodologically differs in three major aspects from that study first unlike the sg proposal which is based on a second pass algorithm operating on fully parsed clauses to determine anaphoric relationships our proposal is basically an incremental single pass parsing model
accordingly we distinguish each sentence s unique focus a complementary list of alternate potential loci and a history list composed of discourse elements not in the list of potential loci but occurring in previous sentences of the current discourse segment
the anaphora resolution module for reflexives intra and inter sentential anaphora has been realized as part of parsetalk a dependency parser which forms part of a larger text understanding system for the german language currently under development at our laboratory
the concept hierarchy consists of a set of concept names lcb computersystem note book motherboard rcb and a subclass relation isaf lcb notebook computersystem pci motherboard motherboard rcb c x NUM r
the protocol level of analysis encompasses the procedural interpretation of the declarative constraints given in section NUM at that level in the case of reflexive pronouns the search for the antecedent is triggered by the occurrence of a reflexive pronoun in the text
the relation of dependency holds between a lexical head and one or several modifiers of that head such that the occurrence of a head allows for the occurrence of one or several modifiers in some pre specified linear ordering but not vice versa
this shortcoming is simply due to the fact that drt is basically a semantic theory not a comprehensive model for text understanding it lacks any systematic connec null tion to comprehensive reasoning systems covering the conceptual knowledge and specific problemsolving models underlying the chosen domain
adequate grammars should therefore also be easily extensible to cover non anaphoric text phenomena e.g. coherence relations rhetorical predicates which provide for additional levels of text macro structure with descriptions stated at the same level of theoretical sophistication as for anaphora
interactive chart editing as mentioned above the memt technology produces as output a chart structure similar to the word hypothesis lattices in speech systems
while this project has shown some very promising results its direct application to a communication device is somewhat questionable primarily because of the computational power necessary to make the technique fast
but once these interface issues are resolved the retrieval model and enhancement techniques operate equally effectively in all the languages we have worked with
this is an expensive and time consuming procedure when done properly requinng many months of work assembling queries and judging retrieved documents by domain experts
if the critical path of the system schedule depends on the timely acquisition or development of new resources the schedule must allow for this
clearly much of this process is governed by the specific requirements of the application considerations which have little to do with linguistic processing
a future modification will be to combine the segmented query with the raw character query and possibly to break long words into their bigram subcomponents
simply porting the components of tipster advanced text processing technology is insufficient proof that a technology will actually perform as expected in a given language
output entities and relationships figure NUM ultimate chinese plum system rectangles represent domain independent language independent algorithms ovals represent knowledge bases
the relative partition p s is then lcb a b rcb lcb c rcb and lcb d rcb
we note that this is simply one fewer than the number of element s in the partition that is
elements of the response that are not found in the key once again generate implicit subsets one per element
the following figure shows the spanning tree in dark lines and the rest of the graph in gray lines
each subset of s in the partition is formed by intersecting s and those response sets ri that overlap s
thinking model theoretically we note that the response corresponds to a subgraph of the fully connected equivalence graph
how may we use our model theoretic notions to provide a scoring mechanism for precision
lastly related work and future directions are discussed
a principal deficiency of the current generation of communication aids is the low rate of communication which can be achieved by users
below are descriptions of some of the parameters
with respect to nouns the treatment of regular polysemy in germanet deserves special attention
in figure NUM all concepts except for the leaves are proper artificial concepts
as opposed to wordnet connectivity between word classes is a strong point of germanet
however they are neither marked nor put to systematic use nor even exactly defined
some of this knowledge is constant across languages
these questions are addressed in the following sections
figure NUM p r scores lbr spanish versus english
proper names which are choosing the highest priority class
p r scores for japanese versus english
these features are obtained through automated and manual techniques
we also gratefully acknowledge tim provision of tim loom system f mm usc isi
in addition futtctional information structttre constraints contribute further restrictkms on proper elliptical antecedents
in NUM of the NUM cases the conceptual model was adequate but the triggering conditions were inappropriate
concepts and roles are hierarchically ordered by subsumption a terminological knowledge representation framework is assumed cf
this section provides a highly condensed exposition of the conceptual constraints underlying the resolutkm of textual ellipses
wenn die taktfrequenz herabgesetzt wird reicht die energie sogar r NUM stunden
also is the charge time of NUM NUM hours quite short
the ellipsis handler has been implemented in smalltalk as part of a comprehensive text parser lot german
in particular it lacks any systematic connection to welldeveloped reasoning systems accounting for conceptual domain knowledge
idioms d carry some individual meaning
extending this idea to the decomposable idiom jmdm
with the help of the rule connecting a ljeetives and nouns not especially written for idioms the predicates incredible z and tall tale z are inserted in the drs
NUM br the extension of active edges according to the flmdamental rule of active chart parsing all synta tic and semantic constraints of the resi ective granunar rule must be satisfied
we establish the following convention for translation literal literal english word by word translation of the german idiom figurative english paraphrase of the figurative meaning fig
tells tom an incredible tall tale
using the stone fl rmalism for syntacl ic and scman i construction is called the integrated approach in tit descriptive approach they are build up sequentially
as example we consider the syntactic behavior of the german verbal idioms cinch bock schieflcn lit shoot a buck fig make a mistake fig
it ix evident f hat a component like bucket of a non coml ositional idioln as kick the bucket annot undergo such kind of synta ti operations
it would also be possible to automatically segment the relevant documents for feedback analysis but it is not clear that this method would produce a measurable difference within the parameters of this experiment
it was often the case that collections were in the simplified character set while the client users might be more familiar with the stc input method and or the traditional character encoding and display
for example the chinese expression for beijing institute of physics may be legitimately represented in a chinese lexicon as a single word and a chinese speaking user may also perceive it as a word
chinese and japanese on the other hand have no explicit indication of word boundaries a reader must determine the writer s intended sequence of words a process called word segmentation
developers must identify what types of resources are reqtfired for successful development whether they are currently available or must be developed and how soon in the development cycle they must be available
for technologies being ported to a new language with a heavy dependence on creation of new resources developers should track incremental progress to more readily identify problem areas and potential schedule slippage
life cycle support of advanced natural language technology is still beyond the ability of most software centers which creates an unrealistic support requirement on contractors that focus primarily on research and technology development
the discourse processor then tries to merge the new ddo with a previous ddo in order to account for the possibility that the new ddo might be a repeated reference to an earlier one
the general purpose thesaurus falls by bringing in terms which are unrelated to the usage or the context at hand and by neglecting other terms which are germane to a query term in context
taking any word or phrase as a concept the infinder program collects and filters frequency information on the words that are most frequently found within two or three sentences of the concept of interest
our system uses two hand crafted sets of rules in combination with the rules that are learned by unsupervised learning NUM
through the method various range of collocations especially domain specific collocations are retrieved
the second step and the third step narrow down the strings to the units of collocation
precision is NUM NUM in the first stage and NUM NUM in the second stage
table NUM shows the collocations extracted with the underlined key strings
but the mutual information is estimated inadequately lower when the cohesiveness between two strings is greatly different
the third column in the table shows the kinds of adjacent words which follow the strings
in this paper we describe a method for automatically retrieving collocations from large text corpora
they contain unnecessary strings such as to a and the in them
therefore by filtering out these strings invalid collocations produced by the method should be reduced
modex operates as a web server which generates html files that can be viewed by any web browser
mobex has been fielded at a software engineering lab at raytheon inc with interesting and encouraging initial feedback
description of the relation is section of general observations a section must belong to exactly one course
several other types of text can be generated such as path descriptions and comparisons and texts about several classes
then the requirements model undergoes subsequent evolution modification or adjustment by a perhaps different analyst
current work on modex is supported by the trp road cooperative agreement f30602 NUM NUM with the sponsorship of darpa and rome laboratory
a requirements analyst builds a formal object oriented oo data model modeling
however suppose the analyst introduces a relation called top between classes gulfinkel and worrow
the lower layer consists of a set of general tools which are structured into several integrated components four in our case
using the completive construction this example is easily translated in nkrl using the i onr occmtences of fig NUM
this pure conceptual parser however is not suitable per se for dealing directly with huge quantifies of unrestricted data
this occurrence is used to express the acting component i.e. it identifies the subj ect of the action the temporal co ordinates etc
nkrl is a fully implemented language the most recent versions have been rcalised in the frmnework of two european projects nomos esprit p5330 and cobalt
errors in model i occurred because the hierarchical structure of dialogues was not considered
for this purpose it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word
note that this heuristic technique introduces some risk of missing the actual best rule in a pass due to its being incorrectly disabled at the time
finally rule NUM picks up cases hke including about four million shares where about is used as a quantifier rather than preposition
this entire learning process is then repeated on the transformed corpus deriving candidate rules scoring them and selecting one with the maximal positive effect
the same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text including non recursive basenp chunks
to give a sense of the kinds of rules being learned the first NUM rules from the 200k basenp run are shown in table NUM
to explore how much difference in performance those lexical rule templates make we repeated the above test runs omitting templates that refer to specific words
in alternate we just look a tri gram as the combination ofa bi gram and a character then calculate their correlation coefficient
terminology phrases were later extracted from the combination of NUM NUM terminology words and their neighboring words within a distance of NUM
previous research had shown that about NUM of the words in science abstract were technical words NUM
uni grams only consist of one character and most of them are common words and then can be found in universal dictionaries
for example two chinese character input methods
a little more differently the proportion of available words in tri grarn and NUM gram candidate tables is much smaller than in hi gram table
these candidates weights were computed in the method introduced in section NUM then they were sorted in descending weight order
from figure NUM we can find that the performance of phrase extraction was n t as good as that of word extraction
to evaluate the computing methods we may consider the distribution in the candidate table of those words available in the dictionary
to gather all word frequency information in a specific domain the domain corpus should be first segmented with the augmented dictionary
step five is to respond to a real application opportunity
minspeak r is ultimately an abbreviation expansion system but it is designed to eliminate much of the cognitive load associated with abbreviation expansion
in addition to the confidence ratings for the tags the scoring function makes use of statistical measures like the mean and standard deviation of the tag length in the training examples
thus our results provide strong support for models that support continuous interpretation
the third step is to integrate into an experimental environment so that complete system flows can be demonstrated and important processing threads can be carried out from start to finish
this means placing an appropriate sgml begin tag like person prior to a person s name in the text and following the person s name with an sgml end tag like person
the semantics are then extracted fl om this analysis trod passed on to the discourse interpreter
2company and governmental institution are subclasses of the class organization airline is a subclass of company
in addition the system can generate a brief natural language summary of the scenario it
subsequently these tags are used by the proper nmne parser to build complex proper name constituents
the third section explains in detail how proper names are reeognised and classified in the system
alld lilltll rcb of fcatllt s are translat d
again this primarily follows from the language sophistication of the chosen population
it provides access to approximately NUM single words divided into NUM general categories
however note that to accomplish this additional keystrokes are required
consider the following input mary think NUM watch give john andrew
we are able to deliver libraries for these tools and their data for french on mac pc and unix
in all three experiments the system introduced NUM new errors due to false corrections of words that were not in the lexicon
certain types of errors in the source or ocr output text present systematic problems for our approach highlighting the limitations of the system
on the other hand if a is very low some correct words may be detected as real word errors and will be changed
the system can correct non word errors as well as real word errors and achieves a NUM NUM error reduction rate for real ocr text
this indicates that even a modest e.g. bigram based representation of context is useful in selecting the best candidates for word error correction
in the second feedback pass for example the system introduced NUM new errors by changing correct lexicon words into other lexicon words
performance also improves significantly in the first feedback process as the system learns the character confusion probabilities by correcting the ocr text
indeed in experiment NUM the result from the third feedback pass is actually worse than that from the second feedback pass
word errors present problems for various text or speech based applications such as optical character recognition ocr and voice input computer interfaces
note that errors can be introduced by the system when it incorrectly changes a correct word in the ocr text into another word
for example assume that we have an automaton gram x such that x is a well formed tree and suppose we want to recognize the input john sees mary
the categorization results are demonstrated in table NUM
in section NUM we will describe context unification and present some results about its formal properties and its relation to other formalisms
note that this gives us another way of determining satisfiability since the minimal automaton recognizing the empty language is readily detectable its only state is the initial state and it is not final
the automaton for a grammar formula is presumably quite a lot larger than the parse forest automaton that is the automaton for the grammar conjoined with the input description
note that automata do make appropriate solved forms for systems of constraints minimized automata are normal forms and they allow for the direct and efficient recovery of particular solutions
note that given an automaton with k states the algorithm must terminate after at most k passes through the loop so the algorithm terminates after at most k NUM searches through the transition table
since it allows the control of the generation process the addition of information to the constraint base is dependent on the input which keeps the number of variables smaller and by this the automata more compact
for a discussion on decidable fragments of context constraints we also refer to this paper
successful research does thus have direct implications for operational applications
the usefulness of hyponymic links has also been evaluated for wordkeys langer and hickey in preparation
the proposed model can be integrated with other approaches for an efficient and robust analysis of dialogues
users of these devices typically have a very low typing rate and it is desirable that any message from the message database can be retrieved by only one key word without the need for query refinement
NUM message retrieval for an aac system currently there exist different types of communication aids for non speaking people
the morphological module uses an affix list in combination with an exception list and the information about syntactic categories from wordnet
a novel approach to reach this is the use of full text retrieval to access a message database
we have also taken care to provide the possibility of porting the system to languages other than english
these include organization person and location names time expressions percentage expressions and monetary amount expressions
the tagset contains two tags fin proper nouns nnp for singular proper nouns and i nps for plurals
the lists of trigger words are amine company NUM trigger words for finding airline company names e.g.
gulf organisation NUM trigger words for organisa null tion names e g association
proper name grammar the grammar rules for proper names constitute a subset of the system s noun t hrase
the f measure also called p r allows the differential weighting of precision and recall
nollll itolllt qllalificati m when an un assi
vahm being the sllrbtc string form of th name
of course the four name classes mentioned are not the only classes of proper names
they employ early stage user interfaces for the purpose of investigating the way users would actually like to use the technology
their reactions and suggestions are collected as important sources of ideas about how to actually configure the technology against their task
tipster phase ii has made a number of strides forward in transferring the research advances of phase i into operational use
collectively the participants in the program have learned a great deal about what works and does not work in transferring technology
prior to installation and use testing and evaluation in an exact replica of the user s environment is usually necessary
i the only real authority we have on language the only measure of whether or not meaning has been conveyed
a record of what was learned in phase ii efforts will be valuable for continued tech transfer in the future
the tipster program has made a number of different efforts aimed at facilitating the use of its advances by commercial entities
so the distribution of tipster gots would require the establishment of a small business center to test and maintain the software
it has been important for tipster to have demonstration projects which showed that tipster technologies could do something useful and important
this means that each pair could preclude other possible matches
features from the surrounding tokens must be used as well
a second order term t is either a first order variable x a construction f tl tn where the arity off is n or an application c t
we hope that while sticking to linear solutions only one may be able to introduce such ambiguities in a very controlled way thus avoiding the overgeneration problems that come from freely abstracting multiple variable occurrences
as an example consider the ellipsis construction of sentence NUM where for simplicity we assume that proper names are interpreted by constants and not as quantifiers
semantically we interpret first order variables x as finite constructor trees which are first order terms without variables and second order variables c as context functions that we define next
the constraint set of the whole construction is the union of the constraint sets obtained by interpreting source and target clause independent of each other plus the pair of constraints given in
ii xs c2 many linguist lam c6 two language lam c3 spoken by var varx
if we are learning a tree to predict begin tags the label is true if the token is the first token inside an sgml tag we are trying to learn and false otherwise
by separating the client interface from the server which performs the learning functionality it was possible to use fast machines for the cpu intensive learning operations rather than relying on the user s desktop machine
each of the three length preferences longest shortest or closest to mean distance uses an appropriate bias to the way in which these inputs are combined
when tagging a text robotag evaluates the learned decision tree classifiers on the new text to produce a list of potential begin and end tags for each tag type
it was important for the confidence of our users that the tagging procedure induced by the system be easily explained in terms of how it makes its decisions
what distinguishes this tagging tool is that the manually tagged documents are passed back through the robotag server to build a tagging procedure in line with what the user is tagging
that means that the concept of the underlying referent which often may be an abstract entity lacking a physical extension should be verbalized and included into the paraphrase
the trees in 12a b will be considered equivalent because they specify the same recipe shown in 12c
for each leaf i.e. lexeme of a given syntax tree the lexicon specifies a lexical interpretation from the model
mary tells peter s story about himself
hence a is not d bound by the head h1 which d binds 7r
NUM dab er e hen brief bekommt erwartet peter
NUM NUM peter erwartet daft eri einen brief bekommt
this can be described in terms of three main phases NUM
this work has been funded by lgfg baden wiirttemberg NUM NUM NUM NUM NUM
note the equivalence of grammatical and conceptual conditions within a single constraint
only nouns or personal pronouns are capable of responding to searchrefantecedenl messages
NUM maria erzpshlt peters geschichte fiber sich
at this level of description drt is clearly superior to gb
NUM each encyclopedia yielded around NUM animal entries and from these we collated a subcorpus of sentences involving comparison
the echidna and the african porcupine i the echidna also known as the spiny anteater is a type of monotreme
if on the other hand reproduction is considered a more important feature then we might compare the echidna to the platypus
the purpose of a clarificatory comparison is to ensure that the reader does not confuse the entity being described with some other entity
we are more interested here in how peba ii decides when it is appropriate to use either a clarificatory comparison or an illustrative comparison
in such cases we will refer to the first entity as the focused entity and to the second entity as the potential confusor
a reader who is familiar with the comparator entity will also more easily form a mental picture of what the focused entity is like
it would be a mistake to think that units or servings and kinds are the only atoms available to mass nouns undergoing conversion to count nouns
at the same time however i have postulated lexical rules whereby mass nouns are converted to count nouns and count nouns are converted to mass nouns
when a noun has the feature ct its denotation is the set whose members are all and only those minimal aggregates of which the noun is true
this characterization while apt does not however distinguish mass nouns from count nouns for cumulativity of reference also holds of plural count nouns
NUM NUM has pointed out a plurality can be seen as the limiting case of a collective a plurality is a collective without conditions governing its constitution
NUM NUM lcb m mlm2m3 mlm2m4 m4msrn6 rcb NUM NUM lcb wlw2 w4ws wlw3 rcb
but the quantifier is restricted not to the count noun s denotation but to an aggregation built from that denotation
there are parts of water sugar and furniture too small to count as water sugar furniture
the account presented postulates the pair of morphological features ct which are assumed to be assigned uniquely to the lexical entries for english common nouns
and the reason for this answer can be seen by considering the need for this redundancy in the case of conversion of proper names to common nouns
the following dialogue examples show such cases
an edge is used in a tree reading if it is one of the tree s edges
vertices that do not begin edges are called leaves such that do are called inner nodes
as it would be done in the underlying trees tile contexts clearly separate the information flow
a parse forest as input structure but an arbitrary parse tree i.e. one speeitic syntactic reading
NUM for every vertex there is at most one edge starting at the vette
the syntactic ambiguities are ol rained by re ambiguat ion in the semanl ic eohlponent
this t aper opts for the second approach for motivation see chapter NUM
a parse tree is an ordered directed acyclic graph dag satisfying the following constraints
unfortunately advances made in this area lid not have impact on semantic construction
languages contemporary speech recognition systems derive their power from corpus based statistical modeling both at the acoustic and language levels
the c box approach does not preclude n best techniques in fact we consider this as a natural extension of the present method one of the obvious limitations of the present approach is the need for parallel training texts which may be replaced by multiple alternatives
for example the radiology reports used in these experiments were read by an amc resident who while obviously familiar with the subject matter also pointed out some fine vocabulary and style differences between amc and ummc baltimore where the reports were produced
i NUM conclusions limitations and future directions while we are generally pleased with the initial progress of this work it is still quite early to draw any definite conclusions whether the c box method will prove sufficiently robust and effective in practical applications
as shown in figure NUM memt feeds an input text to several mt engines in parallel with each engine employing a different mt technology NUM
the goal is to carry out a limited acoustic data collection effort using materials that have been explicitly constructed to yield a rich phonetic sampling for the target language
lexical modeling is based on creating pronunciations from orthography and involves a variety of techniques familiar from speech synthesis including letter to sound rules phonological rules and exception lists
in combination these techniques allow us to create working recognition systems in very short periods of time and provide a path for evolutionary improvement of recognition capability
we have presented here the diplomat speech translation system with particular emphasis on the user interaction mechanisms employed to cope with error prone speech and mt processes
in statistical tagging the relevant information is extracted from a training text and fitted into a statistical language model which is then used to assign the most likely tag to each word in the input text
statistical taggers usually work as follows first each word in the input word string NUM w is assigned all possible tags according to the lexicon thereby creating a lattice
part of speech pos tagging consists in assigning to each word of an input text a set of tag s from a finite set of possible tags a tag palette or a tag set
we will here derive from first principles a practical method for handling sparse data that does not need separate training data for determining the back off weights and which lends itself to direct calculation thus avoiding time consuming reestimation procedures
an enhancement is to partition the training set into n parts and in turn perform linear interpolation with each of the n parts held out to determine the back off weights and use the remaining n NUM parts for parameter estimation
let us generalize this scenario slightly to the situation were wc have a sequence of increasingly more general contexts cm c urn NUM c c c1 i.e. where there is a linear order of the various contexts ck
for instance in processing japanese robotag may use features which are uniquely japanese but may not be present in english or vice versa
although for comparison with other systems we have presented traditional batch mode learning results here one of robotag s strengths is in its interactivity
v fe use co occurrence informatioi in corl ust ased lis mfl iguation and other information in rule b sed disambiguation
first we prcl are l test data of NUM ambiguous pps in texts randomly taken from a olnl uter manual a
rcb phase NUM concept based disalnl iguation NUM project each of v nl n2 into its coiteel t sets
it includes presuppositions syntactic and lexical cues collocations syntactic and semantic restrictions features of head words conceptual relationships and world knowledge
thus we could choose a attaefiment actor ling to ra score if ra NUM NUM choose vp attachment otherwise choose np attachment
we introduce NUM reference rules to encode syntactic and lexical clues as well a s clues from conceptual information to determine pp attachments
the o o llrrences of triples and pairs in v nl p n2 colne frmn annotated eorl ora se tion NUM
for instance between stem variant pair sepp smith sepa hold two rules stem end change NUM a stem grade change pp p e a therelbre the decision list compiled by stlearn should be used in cycle until all stem changes arc identified
decision list is the sequence of the then else clauses arranged according to the generality level of the conditions while the last condition class description tbr the stem end changes in current case is the constant true
stem can occur either in a strong or a weak grade the grades are differentiated first of all by phonetic quantity 2nd or 3rd degree of quantity marked by that may be accompanied by various sound changes enfblding the medial sounds
for instance members of the stem pair h6ive h ive are distinguished only by the different phonetic quantity in case of couple aat2e aal2e the rewriting rule b p is concurrent with the phonetic quantity change
the test results of the algorithms described m the current work show that it is possible to classify the stem changes according to lbrmal features available tiom the text and at the same time to do it correctly in linguistic sense
the number of possible stem variants can strongly vary in estonian in some inflection types there are no stem variants at all in some of them a word can have even five different regular stem variants
determining is the main attribute in our case character corresponding to the changing sound and its direct environs context that consists of the complement attributes in most cases the width of the determining context is unknown at first the earning system has to deal with undefined nmnber of the attributes
the method provides a procedure to dynamically compute restrictor a minimum set of features involved in an infinite loop for every propagation path thus top down constraints are maximally propagated
the symbol is used to represent the equality relation in the unification equations and the symbol used in the form of pl p2 represents the path concatenation of pl and p2
null the method proposed in this paper is more general than the above approaches if the selection ordering is imposed in the detection function features in restrictor
the owner arc keeps extending in the subsequent recursive applications as in d NUM thus the propagation goes into an infinite loop
after the propagation is re done from d NUM the resulting dad d NUM becomes more general than o NUM
hedeg rz sere owfler d NUM rl npo np1 pos np2 npo head np2 head
the extraction function d pl extracts the subdag under path pl for a given d and the embedding function d pl injects d into the enclosing dag d such that d pl d
in the preliminary analysis the number of edges entered into the chart has decreased by NUM compared to when only the category feature i.e. context free backbone was used in propagation
for example if an utterance with ask ref speech act uttered then the next speech act would be one of response request conf and reject
p u is p lis freq fi si fteq si NUM
this model uses the surface syntactic patterns of the sentence and n gram of speech acts of the sentences which are discourse structurally recent to tile sentence
consequently we partition the history space into equivalence classes and the stochastic n gram approach that has served lexicai language modeling so well treats two histories as equivalent if they end in the same n NUM symbols
we base our experiments on a binary valued symbol set e1 lcb w s rcb and on a ternary valued symbol set e2 lcb w s p rcb where w indicates weak stress s indicates strong stress NUM we use the ll6 NUM entry cmu pronouncing dictionary version NUM NUM for all experiments in this paper
how much do their selection diction and their arrangement syntax act to enhance rhythm
if lcb xi rcb is a markov chain with stationary distribution tt and transition matrix p then its entropy rate is
since we had several thousand times more data than is needed to make reliable estimates of stress entropy rate for values of n less than NUM it was practical to subdivide the corpus according to some criterion and calculate the stress entropy rate for each subset as well as for the whole
figure NUM a song lyric exemplifies a highly regular stress stream from the musical pippin by stephen
it would be straightforward to divide among all alternatives the count for each n gram that includes a word with multiple stress patterns but in the absence of reliable frequency information to weight each pattern we chose simply to use the pronunciation listed first in the dictionary which is judged by the lexicographer to be the most popular
coverage of the n gram set is complete for our prose training texts for n as high as eight nor do singleton states counts that occur only once which are the bases of turing s estimate of the frequency of untrained states in new data occur until n NUM
the probabilities in p can be trained by accumulating for each sx s2 sk e e k the k gram count in c sl sz sk in the training data and normalizing by the k NUM gram count c sl s2
abstractly the dictionary maps words to sequences of symbols from lcb primary secondary unstressed rcb which we interpret by downsampling to our binary system primary stress is strong non stress is weak and secondary stress NUM we allow to be either weak or strong depending on the experiment we are conducting
in this equation ui is adjacent to uj and uj is adjacent to uk in the discourse structure where NUM j k i NUM
instead these techniques are based on considerations of how population frequencies in general tend to behave
we can thus calculate estimates of the probability distributions in all contexts c1 cm
there were three separate test corpora b c and d consisting of approximately NUM NUM words each
expected likelihood estimation ele consists in assigning an extra half a count to all outcomes
as it is the latter outcomes are assigned a count of NUM 4i5
this in turn means that the number of observations tends to be quite small for such contexts
further more determining the back off weights usually requires resorting to a time consuming iterative reestimation procedure
if there is only a partial order of the various generalizations the scheme is still viable
their most pressing concern will be speed and getting one or two good answers to their questions in the top of their return document list
note that collocations are not generated from all the key strings because some of them are uninterrupted collocations in themselves like no
these strings are uninterrupted collocations of themselves while they are used in the next stage to construct collocations
f vl i share apartment with friend is the numl er of imes the quadruple share apartlnent with friend is seelt with a vp attachment
second including semantic considerations even if we assume efficient syntactic processing for the sake of argument the question arises how semantic interpretations can be processed in an incremental comparably efficient way
accordingly the getnextcontainer message either returns this address directly el if it is not available yet it will create the next container actor first the actual creation protocol is not shown
in any of these cases the parser should guarantee a robust graceful degradation performance i.e. produce fragmentary parses and interpretations corresponding to the degree of violation or lack of grammar constraints
thus the new container composed at this stage will contain only those phrases that were encapsulated in the context container and that could be enlarged by attaching a phrase from the active container
tile parsetalk parser for a survey cf
figure NUM protocol for the search for a head
information technology product reviews and medical findings reports
we wish especially to thank uwe msnnich and jim rogers for discussions and advice
the number of variables our current prototype can handle lies between eight and eleven
nonetheless there are a number of ways in which these compilation techniques remain useful
it is made even more difficult by the lack of high quality software tools
that is we actually use the compiler on line as the constraint solver
the hshfeld and smolka scheme allows the inclusion of existential quantification into the relational extension
1deg this is not enough to compile or test all interesting aspects of a formalization
so the formula is satisfiable just in case the corresponding automaton recognizes a nonempty language
now every assignment function we might need corresponds uniquely to a labeling function over n2
translation of dialogues often requires the analysis of contexts
we present the lexical semantic net for german germanet which integrates conceptual ontological information with lexical semantics within and across word classes
required conslrmnt s and lhe lnaximal onsisl ent subset for l h slrongest level and so on until a maximm consisl ent subset of coltslraints in the k th strongest level is added
c na c c we can prove thal if NUM is a solution t hen there is no assignment r which satisfies the required onstrmnls and is locally predicate better than NUM
NUM if he appears in a sentence as the subject tnd someone in the previous sentence is male then it is preferable that he refers to the one in the t revious sentence
we can regard a logical interpr tation as a possible remling and disaml igua tion as a task to get the most prcf wal h reading among possil le readlags
since only top of the table should be further examined manually chi method was chosen
cw corpus contains many computer terminologies most of which just appeared in last two decades
later the recall and precision were recalculated using the augmented dictionary
those word pair s with too low frequencies are filtered out
the whole corpus is segmented with the augmented dictionary in advance
at the first step all the candidate phrases are extracted
rule NUM if a word pair contains function words it is also filtered out
second terminology words are extracted from these new words as well as the universal dictionary
first those domain specific words which have no entries in the universal dictionary are identified
therefore in this section only bi grams tri grams and NUM grams are taken imo consideration
for a word based named entity system like the one used by the tipster demonstration system this necessitates the use of a segmenter to preprocess the text
however we take a strongly corpus based approach by determining the base vocabulary modeled in germanet by lemmatized frequency lists from text corpora x
it turns out that all spurious ambiguity arises from associative chains such as a b b c c or a b c c d d e f g g h
the frames used in germanet differ from those in wordnet and particle verbs as such are treated in wordnet at all
explicitly marked for projection in the program so that the constraint store can be kept as small as possible
in addition to the difference in length of the messages to be accessed there is another constraint that affects communication aids to a much higher degree than standard text retrieval systems the minimal input requirement
these results show that in order to have high recall the system needs to make use not only of information internal to the naming expression but also information from outside the nmne
we describe an information extraction system in which four classes of naming expressions organization person location and tinm names are recognized and classified with nearly NUM combined precision and recall
all of these tasks are carried out by building a single rich inodel of the text the discourse model from which the various results are read oil
so for example words were ollected which come inmmdiately before of in those organization names which eolltain of e.g.
when the parser can generate no further edges a best parse selection algorithm is rml on the final chart to chose single analysis
these lists of trigger words were produced by hand though the organization trigger word lists were generated semi automatically by looking at organization names in tile muc NUM training texts and applying certain heuristics
however to achieve higher recall we need coreference resolution for proper names setting NUM and other context information setting NUM
inl o tll s muult i l ellrcs rotation where ai l ropriat
tled t rot r nanle qualifies ttlt organisationrelated thing then the name is c lassifie l its an orga nisation
a tug grammar basically consists of patr ii style context free rules with feature annotations
the learning and use of icon sequences is facilitated by the incorporation of icon prediction
in some cases to make a proper translation of an utterance in a dialogue the system needs various information about context
since it is very difficult to have a complete knowledge it is not easy to find a correct analysis using such knowledge bases
model i is the proposed model based on linear recency where an utterance u is always connected to the previous utterance ui NUM
we believe that this kind of statistical approach can be integrated with other approaches for an efficient and robust analysis of dialogues
in the closed experiments modelq achieved NUM NUM accuracy for the top candidate and NUM NUM for the top four candidates
on the other hand when the speech act of the utterance is accept it must be translated as o k
promise request etc lexical items are used as a main verb because these are closely tied with specific speech acts
accuracy figures shown in table NUM are computed by counting utterances that have a correct speech act and a correct discourse relation
this table shows that response is the most likely candidate speech act of the following utterance of the utterances with ask refspeech act
fortunately linguistic constraints allow us to reduce the effort that has to be put into the computation of pluggings
the ultimate information extraction system for chinese would include a grammar
as a result names were interpreted as a series of characters
the template generator uses a combination of data driven and expectation driven strategies
this dependence means that segmenter errors will greatly lower extraction accuracy
because of this many foreign languages have alternative character encodings
automatic segmentation in chinese raises the special problem of name recognition
as recall increases to NUM precision will decrease correspondingly
only an extremely small collection of documents can be judged completely
if a word is a terminology then it probably occurs more often in related domain corpus than normal
NUM other representation languages such as one based on case semantics would also be compatible with the approach and would permit greater flexibility
theorist typifies what is known as a proof12 non understanding which entails non acceptance or deferred acceptance is signaled by second turn repair
the supposition of an intention to knowif q if either q or not q is already true in the agent s interpretation of the discourse
however for any goal that would explain an utterance the reasons for having that goal would also be potential interpretations of the utterance
for this an agent needs linguistic knowledge linking the features of an utterance to a range of speech acts that can form adjacency pairs
this process involves not only looking at the surface form of an utterance for example was it stated as a declarative but
for example a speaker who wants someone to know that she lacks a pencil might say i do n t have a pencil
our account of interpretation avoids the extended inference required by plan based models by reversing the standard dependency between an agent s expectations and task related goals
typically models rely on mutual beliefs without accounting for how speakers achieve them or for why speakers should believe that they have achieved them
in addition trug incorporates sortal information which is used to rank parsing results
radius this controls the number of tokens used to make each training tuple
the top level propositions shown in NUM were selected by the program because the hearer hl is believed to be interested in the design of the amplifier and the reviews the amplifier has received
the selection and organization of propositions and their divisions into theme and rheme are determined by the content planner which maintains discourse coherence by stipulating that semantic information must be shared between consecutive utterances whenever possible
for each such entity x the structures defined above are initialized as follows NUM props x p i p x is true in kb
this phenomenon is clearly illustrated by the clause praised by stereofool in NUM which is contrastively stressed before reviled by audiofad is uttered
the input to the program is a goal to describe an object from the knowledge base which in this case contains a variety of facts about hypothetical stereo components
figure NUM block diagram of the architecture
this text is essentially bi focal the echidna and the porcupine are equally imporain the terminology we adopt here a property is a tuple consisting of an attribute and a value or example color red
null alternatively an entity sharing a number of salient features with the focused en null tity might already be known to the user in such a case a clarificatory comparison between these entities may aid the user s understanding of the focused entity
hard coding potential confusors might be considered an easy way out although it is our view that this is one of many places in nlg where there is benefit in adopting solutions that make use of precomputed information in preference to working things out from first principles
note that the use of a common comparator set in conjunction with the algorithm specified here means that we can separate the domain specific aspects of the computation from the domain independent aspects in principle the aim is that the comparator set specifies domain specific information but the algorithm itself is domain independent
NUM there are problems with such an approach searching the knowledge base in this way would be a very costly process it assumes a rather more complete knowledge base than we may be able to rely on and most important of all it assumes that we can determine likelihood of confusability on the basis of some metric but it is not at all clear what such a metric might be
work in this paper we have described the peba ii system as an example of a system which integrates natural language generation and hypertext in the provision of user tailored information null defined some notions relevant to the study of comparison and looked at the concept of illustrative comparison in detail with the aim of defining a mechanism for generating such comparisons that embodies a clear distinction between domain dependent and domain independent information
when robotag processes a tagged training text it creates labeled feature vectors called tuples from the preprocessor data
a vertebrate and not a pet figure NUM cross classification
the following table shows an example of the speech act bigrams
in this case response would be the most likely candidate
the value of r pe jpe is now l ppe 8pf so that the second equation is4
if an is the semantics shared by target and source clause then the only possible value for an is z always
in particular we show that it is necessary for an adequate analysis of focus second occurrence expressions and adverbial quantification
let us consider the uncoloured equation x a t a which has the solutions az a and az z for x
colours are tags which decorate a semantic representation thereby constraining the unification process on the other hand there are also the reflex of linguistic non semantic e.g.
in this paper we will briefly describe the machine translation architecture used in diplomat showing how it is well suited for interactive user correction describe our approach to rapid deployment speech recognition and then discuss our approach to interactive user correction of errors in the overall system
while beginning our investigations into new semi automatic techniques for both speech and mt knowledge base development we have already produced an initial bidirectional system for serbo croatian english speech translation in less than a month and are currently developing haitian creole english and korean english systems
though this heuristic disallows the combination of messages with different actions the messages in each action group already contain enough information to produce quite complex sentences
nonmon not x immediate x fail however if this definition is to be really useful we must also allow one definition of a nonmonotonic rule to make use of other nonmonotonic rules
definition NUM the nonmonotonic rule w c fl c NUM is w applicable to s e s if si ot s flors fail slqtl sors fail the result of the w application is NUM i NUM s note that the w in w application should be considered as a variable
this can be generalized to the case where x is a nonmonotonic rule if we extend the definition of to also mean that the application or explanation of the not rule at this node does not yield failure
a2 a s s andttqst s rcb the nonmonotonic unification is computed by computing the unification of the monotonic parts of the two sorts and then taking the union of their nonmonotonic parts
he design of our l arsing system was governed i y two main goals paralh lism and incrcmcntality
NUM lbln hat in seinei l leben sehon tom has in his liti already tom already made several mistakes in his life
in this as the whole literal meaning is bound to the main part of the idiom in inost eases the verb
the connection of the resumed constituent cincn unglaublichcn biiren and the resuxning definite description dic unglaubliche liigcngeschichtc which definitively exists can not be mapped into the drs
while parsing idioms the necessary idiomatic knowledge of the idioms syntax and semantics is extracted from a special idiomatic knowledge base called piiraseo li x
the first german exmnple has a dutch equivalent een bok schicten where interna inodifications mul quantific tion are NUM ossible
a grammar rule consists of three parts context free rule s over category symbols onstitutc the l aekt onc of every grammar
figure NUM and NUM show the NUM most and NUM least plausible frames according to the system
constituents as defined by the grammar and could be eliminated with further developed of the detection component
these stem from erroneous alternatives in the segmentation of nornin
NUM a mary erinnert ihren freund daran dab
a total of NUM sentences matched the pronominal adverb correlative construct grammar described in section NUM NUM
so the precision of the system can be significantly improved with further development of the detection component
proud of adjective with pp figure NUM subcategorization flames learned by the system b
we demonstrate the use of our framework at the examples of quantifier scope ellipsis and their interaction
in addition our constraint language can express the equality up to relation over trees which captures parallelism between them
we assume an infinite set of first order variables ranged over by x y z an infinite set of second order variables ranged over by c and a set of function symbols ranged over by f that are equipped with an arity n NUM nullary function symbols are called constants
let a be a variable assignment that maps first order variables to finite trees and second order variables to context functions
twoqe language lam many linguist qlamy spoken by vary varx xt t NUM
the aim of semantic underspecification is to produce compact representations of the set of possible readings of a discourse
in this paper we are concerned with a uniform treatment of underspecification and of phenomena of discourse semantic parallelism
consider the run of our algorithm in figure NUM in the first step xs s c j is removed from the constraint and the variable binding x8 s c j is added
the resulting constraint s c j c j presents an equation between a term with a constant as its rigid head symbol and a term with a context variable c as its flexible head symbol
let c k t be the set of tree readings determined for edge k at vertex w and ek the set of tree readings determined for the edge at vertex v
a udrs j is a quadruple l r c where l and r are disjoint finite sets of labels and discourse referents respectively
this domain differs for indefinite nps and quantifier nps since these types of nps are subject to different island constraints only indefinites can be raised over clause boundaries
in this way disambiguation is ensured to be monotonic1 a context d can be canceled by grounding the prolog variable representing d to a specific atom no
table NUM compares the time behavior of constructing one underspccified structure u time with the time needed for constructing of the whole bunch of specified structures s time
then the arguments already presenl a at w must be matched with the arguments predicted for w by the semantic rule corresponding to e predicate match2
the semantic cons rllcl ion module works on parse forests and presut t oses a semantic grammar of a certain kind see chapter NUM
it was still necessary to first unpack the compacl parsing ret resentation and derive the individual parse trees from it before going about generating semant ic representations
thus this is a case where the additional expressive power of context constraints is crucial
the framework employs a constraint language that can express equality and subtree relations between finite trees
projection produces a clash of two rigid head symbols and j
the quantifiers themselves are put together by rule ii
context unification allows to treat the interaction of scope and ellipsis
text alignment methods have been discussed primarily in context of machine translation e.g. brown et
the sanitized reports were subsequently re dictated through the automated speech recognition system in order to obtain parallel samples of automated transcription
below is a sample radiology report its automated transcription version and the effect of a partial correction
in practice however users with either slow access methods or poor language ability tend to produce telegraphic messages consisting of sequences of core vocabulary items without embellishing morphology
it is widely accepted that context plays a significant role in shaping all aspects of language
keywords contextual post processing defining context lexical influence directionality of context
however mis is deficient in its ability to detect one sided correlations cf
assume that we want to estimate the conditional probability p x i c of tile outcome x given a context c from the number of times n it occurs in n ici trials but that this data is sparse
the number of missing elements is once again NUM less than the size of the partition
e isii ip si i scoring precision
others including the non problematic case of a b b c c d
table NUM recall and precision scores for coreference example s
preprocessing the data involved i expansion of some contracted forms e.g.
the problem is thus how to correctly segment an utterance into clauses
varying the threshold leads to a tradeoff of precision vs recall
figure NUM precision vs recall tradeoff
which corresponds to an f score of NUM
figure NUM training the neural network
to extract terminology words from new words all new words were manually examined and put to any of three categories terminology words proper names and other domain specific words or to say those words which are related to this domain to some degree but can not be considered as terminology of this domain for example eg cable iv and computer domain
using this terminology dictionary encouraging results has been achieved about the coverage of terminologies
for example many of the statistically derived lexical features may fall under common subject categories
the starting point is training text which has been pre tagged with the locations of all proper names
hand coded heuristics can be developed to achieve high accuracy however this approach lacks portability
segmenting and tagging proper names is very important for natural language processing particularly ir and mt
p log2q NUM p log2 i p i
where p represents the proportion of names behmging to the class for which the tree is built
classify person john smith person chairman of company safetek company announced his resignation yesterday
delimit pn john smith pn chairman of pn safetek pn announced his resignation yesterday
the system was first built for english and then ported to spanish and japanese
also worth noting are the parts of development system that are executed by hand
consider spanish which has a small phonetic alphabet typical endings representing syllables can provide additional evidence as to the part of speech of an unknown word
almost all data detection algorithms for document storage and retrieval and information extraction algorithms are word based i.e. they assume words for higher level processing
looking forward an interesting project would be to combine the segmentation and extraction steps into one process since many of the tasks of a segmenter e.g.
furthermore a segmentation for a given text that is considered correct by one set of criteria may not be the segmentation most useful for named entity extraction
languages that do not use the roman alphabet may have any number of competing encodings in use by different agencies or in different countries or on different platforms
relevance feedback expands the original query so the difference observed in the feedback experiment are due to the influence of the original segmented or unsegmented query terms
individual outputs with examples for english and their knowledge bases is presented in bbn s paper to the sixth message understanding conference muc NUM
the sequences of words found by the segmenter for each sentence is then assigned a part of speech e.g. proper noun verb adjective etc
for example the granularity of segmentation and the part of speech tag set must be appropriate for the applications and capabilities of the system modules that require them
a list of words with their parts of speech is invaluable to having at least minimal syntactic semantic information about words it is presumed by any grammar
the entropy h of a random variable is the expectation value of the logarithm of p
unfortunately the entropy it ck depends on the probability distribution of context ck and thus on cro ck
this means that when estimating the j NUM gram probabilities we back off to the estimate of the jgram probabilities
in this paper we derived a general practical method for handling sparse data from first principles that avoids held out data and iterative reestimation
the trigram tagger performed better than the hmm based one in all three cases but not significantly better at any significance level below NUM percent
this means that it is not quite obvious what the standard deviation or the statistical mean for that matter actually should be
the measurement of semantic distance can be based on semantic relationship between words
interactions where initial search words do not retrieve relevant messages could be recorded
NUM NUM step NUM merging same attribute
because plandoc can produce many paraphrases for a single message aggregation during the syntactic phase of generation would be difficult semantically similar messages would already have different surface forms
for each potential distinct attribute the system calculates its rank using the formula m d where m is the number of messages and d is the number of distinct attributes for that particular attribute
after merging at step NUM the message list left in an action group either has only one message or it has more than one message with at least two distinct attributes between them
for identical attributes across two messages as shown in the bracketed phrase a deletion feature is inserted into the semantic fd so that surge will suppress the output
whenever there is only one distinct attribute between two adjacent messages they are merged into one message with a conjoined attribute which is a list of the distinct attributes from both messages
NUM sentence breaking determining sentence breaks
what about messages with two or more distinct attributes
wordkeys is not designed to assist all kinds of communication
the major requirement is to provide translations as and when users need them and do so robustly and in near real time
compared to udrs lud also has a stronger descriptive power not drss but the smallest possible semantic components are uniquely labeled
when doing semantic interpretation within such a framework we want a formalism which allows for compositionality monotonicity and underspecification
the paper discusses how compositional semantics is implemented in the verbmobil speech to speech translation system using lud a description language for underspecified discourse representation structures
the labels introduced are grouped together the group label is the main label of the lud representation the instance its main instance
obviously purely mathematical approaches for transforming the partial ordering encoded in the leq constraints into a total ordering may yield many results
a subsequent search process will attempt to match preprocessed user queries against term based representations of documents in each case determining a degree of relevance between the two which depends upon the number and types of matching terms
in this phase the system attempts to keep the word order that was originally input
expanded as mary thinks that the NUM watches were given to john by andrew
most obviously it is being used to tune the grammar to the range of input
one simple method which we use in our system is to represent a compound name dually as a compound token and as a set of single word terms
this data allows us to ensure the output from the system is in fact appropriate
although many sophisticated search and matching methods are available the crucial problem remains to be that of an adequate representation of content for both the documents and the queries
this is particularly true of verbs which are the cornerstone of the semantic reasoning
second the output structures of the sentences will not require sophisticated syntactic constructions
the network itself encodes a grammar of the telegraphic input expected from this population
since word actors hold information such as features or coverage locally updates are necessary to propagate the effects of the cre ttion of the relation
from this origin the search message can be distributed to all word actors at the right rim of the dependency irec by simply forwarding it to ihe respective heads
this includes the interaction of discourse entities organized in focus spaces and center lists with structural descriptions from the parser and conceptual information from the domain knowledge base
in this paper we will consider a framework for parallel natural language parsing which summarizes the experiences we have made during the development of a concurrent object oriented parser
in this paper we concentrate instead on the basic message passing patterns for the establishment of dependency relations viz the searchhead protocol and its concurrency aspects
hence we rather restrict backtracking to those containers in the parse history which hold governing phrases while the containers with modifying phrases are immediately deleted after attachment NUM
while still keeping the benefits of parallelism we have arrived at a point where we argue for a basically serial model patched with several parallel phases rather than a basically parallel model with few synchronization checkpoints
the number of calls to these methods for a sample of NUM randomly chosen increasingly complex sentences from the information technology domain test library is given by fig NUM cp syn
s2 the remainder of the support unes and tubes are unchanged in position
table NUM result of the third step
what decomposable idioms concerns now the afor the reason of simplification we choose english predicate names for the conditions in the drss e.g.
therefbre the ineanillg of non eompositional dloins is seen NUM NUM aal tlnstrllctllred om lex
on this basis an idiom is called decomposable because the situation to which it refers can be seen as an open relation rxb
we will show the advantages of our theoretical considerations above that can be best illustrated by drt already including mechanisms to handle referents
taking these facts into account we propose an adequate way to represent the idiomatic meaning by kamp s discourse representation theory drt
in this paper we propose an adequate semantic representation for idiomatic knowledge and show a way of processing syntax and semantics of decomposable idioms
on the base of the discussed paraphrase eine liigengeschichte erz ihlen we offer the solution shown in NUM
the rheme of each utterance is considered to be represented by the material that is new or forms the core contribution of the utterance to the discourse
NUM can he glossed in the following way
the entries in this list have the following fornmt
it was now possible to postulate some generalisations about the use of the various punctuation marks from this reduced set of rule patterns
a context with hole x is a term t that does not contain any other variable than x and has exactly one occurrence of x
for example with linguistically sophisticated users we expect the input word order to mirror the desired surface form
before any intelligent device can be developed for this population the problem of lexical access must be solved
the field of aac is concerned with developing methods that provide access to communicative material under reasonable time and cognitive constraints
the system speeds rate by allowing the user to select basic content and having the system provide expansions into well formed sentences
minspeak x deals with the cognitive load associated with standard abbreviation systems by forming abbreviations using multi meaning icons rather than letters
the system is implemented in c for economy of memory and for speed and compatibility considerations
for example mask followed by sun produces the word happy apple followed by apple
first some portion of each of the two kinds of data has been set aside for testing purposes
in this project we have decided to collapse the three levels of processing found in compansion into one level
word for word translation cdp does notsuppose cooperation and it is n t true that benda enforced dejmal
the input text is first checked for spelling errors then the lexical and morphological analysis creates data which are combined with the information contained in a separate syntactic dictionary
process of grammar checking the design of our system was motivated by a simple and natural idea the grammar checker should not spend too much time on simple correct sentences
such a relatively large number of variants is caused by the fact that our syntactic analysis uses only purely syntactic means we do not take into account either semantics or textual or sentential context
this technique substantially speeds up the processing of rules with relaxed constraints but it has also one rather unpleasant side effect the syntactic inconsistencies may be suppressed and appear later in a different location
we do not suppose that the last option is interesting for a typical user but if we do have all this information why should we throw it out
this phrase can be easily checked for grammatical correctness locally because it has a clear ler and right borders prepositions v and s
this classification therefore allows to handle ru es describing both correct and erroneous syntactic constructions in a uniform way and to use a single grammar for the description of both types of syntactic constructions
in languages with such a high degree of word order freedom as in most slavic languages the set of syntactic errors that may be detected by means of simple pattern matching methods is almost negligible
it is not a serious problem however becausecommon collocations are limited in number and we can efficiently obtain them from dictionaries or by human reflection
next the position of manual for specific instructions is examined and it is determined to follow a gap placed after refer to the
taking stri in order of frequency this step determines refer to the appropriate manual for instructions o nn refer t o the manual for specific instructions
as an alternative way to evaluate the algorithm we are planning to apply the collocations retrieved to a machine translation system and evaluate how they contribute to the quality of translation
refer to the installation manual for specific instructions fo psr refer to the manual for specific in ffn where stri is placed in a collocation
the column labeled NUM in table NUM is the result of applying a segment model which considers the starting NUM word block from a text
basically what we do is to pick up a thresholding constant k and assign words whose probability of being a title word is greater than k
in table NUM the test set NUM for instance consists of articles each of which which contains from NUM to NUM characters
the significance of exploiting information on the structure for topic identification is demonstrated by a set of experiments conducted on the 19mb of japanese newspaper articles
it was shown clearly that a text structure thus identified gives a good clue to finding out parts of the text most relevant to its content
in the light of this we have conducted a series of experiments to determine whether discarding rear portions of text affects the performance of topic identification
this is because in topic identification we want to assign documents to nouns which occur in the document rather than to categories given a priori
section NUM is a response to the problem we propose the use of information on text structure to reduce irrelevancy in the document and increase effectiveness
the table shows that at i NUM there were no break even points found for texts with more than NUM characters NUM NUM
2there are some examples of words that can be either a common noun or josftshi for example gy5 line or hako box which can follow a numeral or stand alone
for example in alt j e the countability and number of two at positive noun phrases are made to match each other unless one element is plural and the other is a gi lcb oup classifier
jos shi can not form grammatical noun phrases on their own rcb NUM NUM hiki NUM animals numeral NUM sg hiki some animals quant
quantity partitives are further divided into three cases the first where the embedded noun phrase is uncountable the second where it is plural and the third where it is singular and countable
the corpus contains about NUM million words
table NUM results from isolated word error correction
with the emergence of object oriented technology and user centered evolutionary software engineering paradigms the requirements gathering phase has become an iterative activity
we deal with this problem by requiring the modex user to follow certain conventions with respect to the labeling of relations and objects
modex does not have access to knowledge about the domain of the data model beyond the data model itself
some previous systems have paraphrased complex modeling languages that are not widely used outside the research community gist ppp
furthermore the lack of domain knowledge also means that modex can not choose the correct paraphrase for an ambiguous part of a model
hypertext links are included shown underlined for example clicking on professor will produce a description of the professor class
the general observations section paraphrases the class definition and the ezamples section gives a concrete example of an instance of this class
our design is based on initial interviews with potential users and on subsequent feedback from actual users during an iterative prototyping approach
this belief is reflected by the large number of graphical oo data modeling tools currently in research labs and on the market
he hnas that the mmnslc difficulty of the graphics mode was the strongest effect observed p NUM
thus capitalized unknown tokens are tagged as proper nouns by default
location NUM trigger words for location nanle s e.g.
in the first pass a special grammar is used to identify proper nanms
with precision and recall weighted equally it is computed by the formula
using hocu we can model this restriction directly
NUM a jon only likes mary
only for the analysis of the interaction of e.g.
i 0f ipf and the equation is
second a color constant only unifies with itself
the tagger tags a word as a proper noun as follows if the word is timnd in the tagger s lexicon and listed as a proper noun then tag it as such otherwise if the word is not found in the lexicon and is uppercase initial then tag it as a proper noun
nor is it only the sheer volume of names that makes them important for some applications such as information extraction ie robust handling of proper names is a prerequisite for successflflly performing other tasks such as template filling where correctly identifying the entities which play semantic roles in relational frames is crucial
amines if nalne is a NUM cl s ii ltall anti name2 is qth w the first tim family or NUM l h nanms if nam l then name2 niat hes nanml e.g.
f r latnc l ci i ies a t kcn if the most siiccific tyi c NUM ossiblc e.g. company or perhaps only object is ereaged and a name attrit ute ix associated with the entity the a ttritlute s
techniques relying on internal cwich n c only e xac t word and phrase mat thing gral holo null gic al conventions and t arsing m e not mlfficient t o re ognise and lassi y organisal ion names
i en fil an t e gained by analyzing il not only in t erms of ovea all recall and pre ision tigures NUM ut also in terms of sysl sll coslll oslelll s lid class s of ilaliics
futhermore we assume a binary function symbol that we write in left associative infix notation and constants like john language etc
note that the treatment of parallelism refers to contrasted and non contrasted portions of the clause pairs rather than to overt and phonetically unrealized elements
thus it is not specifc for the treatment of ellipsis but can be applied to other kinds of parallel constructions as well
in case of termination with an empty constraint the set of variable bindings describes a set of solutions of the initial constraint
the challenge is to integrate a treatment of parallelism with underspecification such as in cases of the interaction of scope and ellipsis
the example demonstrated that earlier treatments of ellipsis based on copying of the content of constituents are insufficient for such kinds of parallelism
it is clear that performing the closure operation must be based on the information that the semantic material assembled so far is complete
a subtree constraint has the form x x and is interpreted with respect to the subtree relation on finite trees
for person NUM NUM for place and NUM NUM for entity
hand coded rules are employed to delimit proper nouns within the text
in its non word and real word error mode the system treats every word as though it were a possible error
most traditional word correction techniques concentrate on non word error correction and do not consider the context in which the error appears
as we might expect the results from the context based experiments are much better than those from the isolated word experiment
NUM context dependent non word error correction the system used context to correct strings that did match valid lexicon words
on the other hand the system properly corrected NUM real errors NUM NUM of all the real errors
whenever the system encounters an unknown word it treats it as a non word error and attempts to correct it
the current system employs an iterative learning from correcting technique that treats the corrected ocr text as an approximation of the original text
we thank nata a mili4 frayling and an anonymous reviewer for their excellent comments on an earlier version of this paper
to test our ocr error correction process we used a set of electronic documents from the ziff davis ziff news wire
the overall process for correcting a sentence is as follows NUM read a sentence from the input ocr text
figure NUM shows ttle performance results for english
on the other hand it is exactly the situations where such constructions are most used that the other problem areas of the system are most prevalent
in phase NUM the message takes the path from the finite verb to the sentence delimiter no effect
dependency structures by definition refer to the sentence level of linguistic description only
we will now consider constraints on intra sentential anaphora personal pronouns and definite nps
upon instantiation of the corresponding word actor a searchrefantecedent message will be issued
mary tells peter s story about herself
the functor argument application is based on the notion of the context of a lud representation
in this example a sentence s consists of an np and a vp
consider a simplified example of a syntactic rule annotated with a semantic functor argument application
for robustness this deep level processing strategy is complemented with a shallow analysis and transfer component
in addition we have two functions that help us to keep track of the right labels
this list consists of the contexts of the arguments a category is looking for
the interpretation fnnction i is a function from a labeled condition to a drs
in mrs this is done by unification of the relations with unresolved dependencies
ffi st the robustness of the grammar can be tested by determining the number of completed input utterances found in the collected data that can be handled by the grammar
common nouns that is mass nouns and count nouns are distinguished by the lexical features kct which are assigned to common nouns in their lexical entries
two criteria have been invoked namely cumulativity of reference and divisivity of reference in spite of the fact that these criteria have long been recognized as utterly inadequate
as always a denotation is associated with a count noun namely the largest subset of the domain of discourse of each of whose members the noun is true
adhering to standard linguistic usage i shall refer to this correlation as conversion leaving open whether or not this phenomenon is to be further analyzed as so called zero derivation
particularly revealing here are the mass nouns of pizza and cake where two units are possible one corresponding to a serving and the other corresponding to a unit of fabrication
tinction it is useful to begin with to recognize that english nouns fall into four classes pronouns proper names mass nouns and count nouns
the situation certainly renders the sentence in NUM true and that it is so can be derived by any rule which assigns clausal scope to quantified noun phrases
other sub regularities include the case where common nouns for trees can be used to denote the largest aggregate of those parts considered useful for human use in construction namely the wood obtained from them
the author would like to thank arlene badman patrick demasco david hershberger clifford kushler and christopher pennington for their collaboration on the design and implementation of this project
whenever a is more specific than or contains more information than b
secondly it should be possible to define inheritance hierarchies on the linguistic knowledge described by the formalism
this makes it important to allow fail to be the result of applying a nonmonotonic rule
the second rule is used for checking completeness and it works similarly to the any definition above
consequently it is possible to have conflicting defaults and multiple w explanations for a nonmonotonic sort
the last condition on a nonmonotonic sort NUM us rs may seem superfluous
class verb isa value requires form default active
the generality of the approach was demonstrated by defining some of the most commonly used nonmonotonic operations
one very plausible subsumption order that can be used is the ordinary subsumption lattice of feature structures
more precisely given an equivalence class s defined by the response we mus t determine the minimal number of links to be added to the key so as to ensure that each of the members of the response set is in the same key set
it improves on the original approach l by NUM grounding the scoring scheme in terms of a model NUM producing more intuitive recall and precision scores and NUM not requiring explicit computation of the transitive closure of coreference
either way a minimal spanning tree of the equivalence relation will always be of size NUM which aligns with th e intuitive notion that three links will always be necessary to make four entitie s coreferential under the criterion of strict identity
for the example above these formul a yield a precision of NUM NUM which is intuitively appropriate since of the two minimum link s needed to generate the response class lcb a b c rcb the key only provides one b c
these classes are of course the models of the ident equivalence relation and this strategy is preferable for a number of reasons on e being that the scores are independent of the particular links used to encode th e equivalence relation
if tile cover extents are equal the domain specifc heuristics is used according to which the left context context enfolding the medial sounds is more infomlative and the left extension is choosen
two stem variants can be bounded by more than one stem change
the principle types of changes are the following NUM stem grade changes
to be able to determine semantically related words without loss of precision information from the morphological analysis is also used to determine the morpho syntactic categories of word forms and lemmas
in our case the number of observations n is simply the size of the context ck by which we mean the number of times ck occurred in the training data i.e. the frequency count of ck which we will denote ck
we dmtmgmsh two kinds of relations namely properttes and conceptual relationships a property denotes a relation between individuals and string or integer values a conceptual relatsonshsp denotes a relation between two mchv duals the concept description language prowdes constructs to formulate necessary and possibly sufllcmnt conditions on the properties and conceptual relationships every element of a concept class m reqmred to have the syntax of thin language m given m fig NUM
we now discuss the protocol for establishing anaphoric relations based on intra and inter sentential anaphora considering the following text NUM die firma cornpaq die den lte lite entwikkelt bestiickt ihn mit einem pci motherboard
it is also obvious that whenever the anaphor belongs to a clause which is subordinate to one that contains the antecedent both may be coreferent this holds independently of the ordering of antecedent and pronoun cf
focusing on the text analysis potential of drt its complex machinery might work in a satisfactory way for several well studied forms of anaphora but it necessarily fails if various non anaphoric text phenomena need to be interpreted
word actors combine objectoriented features with concurrency yielding strict lexical distribution and distributed computation in a methodologically clean way
as many forms of anaphors e.g. nominal and pronominal anaphors occur within sentence boundaries so called intra sentential or sentence anaphora and beyond intersentential or text anaphora adequate theories of anaphora should allow the formulation of grammatical regularities for both types using a common set of grammatical primitives
as we will show the specification of a particular message protocol corresponds to the treatment of fairly general linguistic tasks such as establishing dependencies properly arranging coordinations and of course resolving anaphors
greater than it m on the average the case for all other active concepts sc1 exploits the structure of the aggregation luerarchy and evaluates it by the associated actwation weights for the defimtmns of sets and functions we use below cf table NUM pcount c rp ff rp ee p n if c and
salient relationships and salient properties just as certain concepts may have been dealt with
topic s text parser heavily rehes on terminological knowledge about the domain the texts deal wlth hahn NUM
while this is a difficult experiment to do automatically we re hoping to approximate it using a natural language generation system based on link grammar under development by the author
this efficiency will enable us to experiment with values of n as large as seven for larger values the amount of training data not time is the limiting factor
restated inversely using entropy rates for randomly permuted sentences as a baseline sentences with higher sequence probability are relatively more rhythmical in the sense of our definition from section NUM
in both cases the studies have typically focussed on regularities in the distance between peaks of prominence or interstress intervals either perceived by a human subject or measured in the signal
highly accurate techniques for part of speech labeling could be used for stress pattern disambiguation when the ambiguity is purely lexical but often the choice in both production and perception is dialectal
the results for n gram models of orders NUM through NUM for the case in which secondary lexical stress is mapped to the weak level are shown in figure NUM
rhythm inheres in creative output asserting itself as the meter in music the iambs and trochees of poetry and the uniformity in distances between objects in art and architecture
rules governing how to map a word s orthographic or phonetic transcription to a sequence of stress values have been searched for and studied from rules based statistical and connectionist perspectives
more importantly these tests hold everything constant diction syllable count syllable rate per word except for syntax the arrangement of the chosen words within the sentence
the negative slopes of the difference curves suggests a more interesting conclusion as sentence perplexity increases the gap in stress entropy rate between syntactic sentences and randomly permuted sentences narrows
this is a list of sentences containing the key string refer to retrieved and each underlined string corresponds to a string stri
if a is equal to the empty string then rewriting operation is reduced to the insertion the same tbr the string b means deletion
l urther work provides designing the methods for the generalisation of acquired rule sets into tbrmal grammar ill term of the sound classes and elicitation ot thc corresponding exception lists
main attributes arc the character s corresponding to changing sound in the first string and the character s in the same position s in the second one
formally the recognition of the stem change rules can be reduced to the classification task with string pairs as the objects to classify and possible rules of stem changes as the classes
given a set of examples and counterexamples of a class the learning system induces a general class description that covers all of the positive examples and none of the counterexamples
ill the case of the rule set containing the both type of rules the contexts need some correction because some secondary changes in medial sounds can take place only afl er applying stem end changes
each established rule is immediately applied on the first string and the algorithm continues with intermediate word form that may not exist in real language until the frst string becomes equal to the second one
discriminative conjunctions are added to the class description and examples corresponding to them are removed from tile training set get let t context extend the co junctions by adding correspondingly the right and left context
the other way to consider all string as context at first and try to speeit the class description by dropping the redundant attributes arc much more complex complexity depends directly on string length
if a h then stop otherwise search the secondary changes NUM NUM f secondary change is observed NUM l add the corresponding rule to the set r NUM NUM
however we decided to use a custom programmed morphological module because the output of the available analysers did not correspond to our needs and at least for english a simple analysis is relatively easy to implement
the work was supported in part by an arpa aasert award number daah04 NUM NUM NUM
figure NUM the average number of syllables per word for each perplexity bin
for n NUM we obtain a rate of NUM NUM bits per syllable over the entire corpus
lexical stress is the backbone of speech rhythm and the primary tool for its analysis
that this function is roughly increasing agrees with our intuition that sequences with longer words are rarer
figure NUM shows the average number of syllables per word in sentences that appear in each bin
figure NUM NUM gram entropy rates and difference curve for weak secondary stress
figure NUM NUM gram entropy rates and difference curve for el strong secondary stress
the experiments have been of smaller scope and geared toward detecting isochrony regularity in absolute time
figure NUM NUM gram entropy rates and difference curve for e2 strong secondary stress
confirmation requests after any speech recognition or machine translation step the user is offered an accept reject button to indicate whether this is what they said
we make use of materials derived from domain scenarios and from general sources such as newspapers scanned and ocred text in the target language available on the internet and translations of select documents
when the user has typed in one or several key words and decides to start the search the following tasks are carried out tokenization the content of the input field on the interface is parsed into word forms
additionally we included statistics over word frequencies in the main lexicon in order to be able to retrieve hypernyms of words that are useful as index words these are not necessarily the closest superordinated words in the wordnet hierarchy but ofte n
they trained on NUM NUM million words and in their best system achieved precision NUM and recall NUM
false negative indicates an instance where the net fails to hypothesize a boundary where there is one
high performance correct classifications NUM f score NUM NUM is easily achieved
the network was also able to correctly identify some mistagged data marked as correct in table NUM
false positive indicates an instance where the net hypothesizes a boundary where there is none
this material is based on work supported under a national science foundation graduate fellowship
under these constraints f scores vary slightly always remaining between NUM and NUM for both validation and test sets
however common phrases such as good bye and stuff like that etc are also considered small clauses
this becomes important for example in the classification of animals where folk and specialized biological hierarchy compete on a large scale
this is achieved in different ways the cross class relations pertains to of wordnet are used more frequently
the application of these resources is essential for various nlp tasks in reducing time effort and error rate as well as guaranteeing a broader and more domain independent coverage
the category can be clearly determined in the following cases a word has one single entry in the main lexicon which means the word is already a lemma a word form has an inflectional or derivational affix which only occurs with bases of one single morpho syntactic category
i cross classification as to allow for regular polysemy germanet introduces a special bidirectional relator which is placed to the top concepts for which the regular polysemy holds c f
however these two cases are quite distinct from each other justifying their separation into two different relations in germanet
however the polysemy pointer additionally allows the recognition of statistically infrequent uses of a word sense created by regular polysemy
it furthermore encodes cross classification and basic syntactic information constituting an interesting tool in exploring the interaction of syntax and semantics
we extend its coverage to account for resultative verbs by connecting the verb to its adjectival resultative state
some of the guiding principles of the germanet ontology creation are different from wordnet and therefore now explained
for each stem grade changing rule the system has to create the description differentiating stem variant pairs placed under it from all others
as the length of the strings can be very different and in most cases strings are relatively long then the learning direction towards expanding the context is preferable
ontext can bc extended in two directions left to the beginning of the stem and right towards the end of the stem
stem can appear either as a lemmatic stem or an inflection stem stem variants are differentiated by changes enfolding the final sounds e.g.
first in stlearn the supervised inductive learning technique is used to find out the suitable jeatures jor automatic recognizing of the stem changes
about NUM of stems stay changeless mostly take place the stem end or stem grade changes or both at the same time
the aim of the current work is to create tools for the automatic recognition of the estonian stem changing rules
from the viewpoint of the modelling of the natural language stem changes system the rule sets holding for stem variant pairs are more informative than single rules
generating the rule set let r o be the initial rule set a and h arc the stem variants and r is the current rule
now we are ready to treat disamhiguation of the sentences used in section NUM by prioritized circumscription
a logi al representation relate t i o this sclll ence is as follows
for t xamtlh sul pose thai we have the following scllt hiccs
ircums ril tion is a nol her promising method for the task
this onfli l s with he 17orlner semantic preferen e of inertia of possession by NUM uying but the above preference is st ronger tbm the former since the time of giving is later t h m the time of buying
the user is assumed to assign the nonmonotonic information contained in this rule to his linguistic knowledge by using an expression of the form narne pararneterl
this means that two classes where none of them is a subclass of the other will always be considered inconsistent and thus yield a failure when unified
the two following examples will illustrate the difference between nonmonotonic rules giving multiple extensions and nonmonotonic rules giving a single explanation fail
i will now describe how the work by young and rounds can be generalized to allow the user to define nonmonotonic constructions
the notation a b is used to denote the fact that a i NUM b does not yield unification failure
as mentioned previously there is with nonmonotonic sorts as well as normal default logic a possibility of conflicting defaults and thus multiple nonmonotonic extensions for a structure
the when slot in the rule allows the user to decide when the rule is going to be applied or in young and rounds terminology explained
a nonmonotonic sort is a structure containing both information from the basic subsumption order and information about default rules to be explained at a later point in the computation
definition NUM t is a w ezplanation of a nonmono null tonic sort s a if it can be computed in the following way NUM
the fact that this system includes lexical rule templates that refer to actual words sets it apart from approaches that rely only on part of speech tags to predict chunk structure
the most frequent single confusion involved words tagged vbg and vbn whose baseline prediction given their part of speech tag was NUM but which also occur frequently inside basenps
the word in rule NUM comes mostly from company names in the wall st journal source data
the cross product of the NUM word and part of speechpatterns with the NUM chunk tag patterns determined the full set of NUM templates used
those candidate rules are then tested against the rest of corpus to identify at how many locations they would cause negative changes
transformational learning begins with some initial baseline prediction which here means a basehne assignment of chunk tags to words
text are determined by beginning with the baseline heuristic prediction and then applying each rule in the learned rule sequence in turn
this process is iterated leading to an ordered sequence of rules with rules discovered first ordered before those discovered later
to learn a model one first applies the baseline heuristic to produce initial hypotheses for each site in the training corpus
however any message being retrieved by more than one of the key words will be given an increased score the more key words a message is related to the better its score
we have detailed the reasons which lead to the design of a communication aid for non speakers based on ideas from text retrieval with semantic expansion and we demonstrated the overall design of the prototype
the reduction of the necessary user input to produce an utterance and the minimization of the cognitive the load on the user in a message based communication aid can be achieved through efficient message access
langer s and hickey m in preparation using semantic lexicons for intelligent message retrieval in a communication aid submitted to journal of natural language engineering special issue on natural language processing for communication
in the detailed system description we have shown that a precise morphological analysis can be achieved at least for english with relatively low effort ff we use data from publicly available resources
we separate the two steps in order to be able to give the link between a word form and the lemma a higher weight in message access than links between morphologically complex words and their roots
a system based on a query expansion technique has the capability of finding messages that contain words that are semantically related to the query words in addition to the messages that contain the query words themselves
figure NUM overall organization of the wordkeys system
when a message is added to the database the following actions are performed null tokenization supervisor preferences input of neyj messag e inputque ry
a receipthandler is instantiated by a synchronous message intended to detect the partial termination of the subsequently started search protocol
the parsing algorithm of the parsetalk system is centered around the head search process of the currently active word actor
in case both of these protocols are not successful containers may be skipped so that discontinuous analyses may occur
this procedure enforces a depth first style of progression leaving unconsidered many of the theoretieally possible combinations of partial analyses
still some information has to be retained in order to backtrack after failures or to employ alternative parsing strategies
this word form is the same in all cases genders and numbers
this phrase is therefore certainly not the main source of ineffectiveness in parsing
both ways create the same syntactic structure
the system recognizes the structure of this sentence in the following way
the demo is implemented as an independent program cooperating with microsoft word
these words represent NUM NUM NUM NUM unambiguous items
grammar based methods require a complex syntactic information about words
dejmala do funkce ministra ivotniho prost edi
in this im l er we pres ull a n analysis off lassitiet suii al le for use ill a apanese to ldnglish
flintily we omllarc ore mmlyms NUM oth r l ol le s s w l i n NUM
a method for the automatic correction of ocr errors would be clearly beneficial
words of four or fewer characters are also indexed by their letter bigrams
n pr w ii pr wilwl i l
the system distributes the rest of the probability uniformly among other events
note that to effect spelling correction we could include character transposition probabilities
the problems of word error detection and correction have been studied for several decades
NUM NUM the word correction system for ocr post processing
figure NUM process of correcting a sentence
these nouns can be handled in two ways a as a lexical class that combines the properties of common nouns and josftshi or b as two separate lexical entities
we l roi s m mmlysis of lassifiers based on lirol rties of l oth lapan q
naturally if we want to test emptiness we can stop the construction as soon as we encounter a final state in r r
this approach to theorem proving is rather different from more general techniques for higher order theorem proving in ways that the formalizer must keep in mind
the resulting system is only semi decidable due to the fact that the extension permits monadic second order variables to appear in recursively defined clauses
so it makes sense to search for ways to construct the parse forest automaton which do not require the prior construction of an entire grammar automaton
an obvious goal for the use of the discussed approach would be the offline generation of a tree automaton representing an entire grammar
we could for example investigate theories in which asymmetric c command was the only primitive or asymmetric c command plus dominance for example
since a quantifier prefix of the form NUM 3v v3 is equivalent to NUM NUM NUM
r contains the reachable states constructed so far and r contains possibly new states constructed on the current pass through the loop
two corpora were chosen for this research
terminology phrases are generated in three steps
these words are called as available words
news articles with 10m bytes of text
these candidates compose the bi gram candidate table
among them NUM NUM are available words
this research has practical importance in many domain related natural language applications
they belong to either the same word or two neighboring words
random sampling showed that NUM of them are acceptable terminology phrases
NUM a mary denkt an johns ankuft
in order to achieve such communication the users currently can interact with diplomat in the following ways speech displayed as text after any speech recognition step the best overall hypothesis is displayed as text on the screen
for those words which appear in the domain corpus but do n t appear in the universal corpus p is approximately replaced with the average frequency of all words
nevertheless it does suggest that useful recognition performance for a large set of languages can be achieved given a carefully chosen set of core languages that can serve as a source of acoustic models for a cluster of phonetically similar languages
the preference for coherent interpretations is especially important when there is more than one discourse level act for which the utterance is a possible decomposition
first words that could be easily identified as belonging to the vocabulary of the given domain were extracted then the rest of the vocabulary were extracted using these seed words
a user model is advantageous here since the importance of different attribute types will vary from person to person
some comparisons like the african porcupine the echidna has a browny black coat and paler coloured spines
the latter discourse goal corresponds of course to the category of direct comparisons we identified above
such confusions are possible when the entity being described is similar in relevant respects to some other entity
the degree of relatedness between the two entities can also play a role in choosing the best comparator
NUM the output from the peba ii system is a document marked up using a subset of html commands
what techniques do we need to build into a text generation system to be able to produce similar comparisons
we could then phrase our description of e to make sure that we distinguish e from such potential confusors
we describe how these comparison strategies are used within the peba ii hypertext generation system to generate descriptions of animals
currently the system makes use of two high level discourse plans which we name identify and compare and contrast
we have found that written agreements between technologists and customers signed at an appropriate management level even when operating within the same agency covering expectations of funding and personnel resources as well as the criteria for success of a project are extremely helpful in preventing disagreements and disappointments over the course of an applications development
it means having representatives from the user group from a couple levels of their management from the developers and their management from the is information services staff or the equivalent cognizant of each other of the goals and progress of the project and aware of their respective roles and obligations
we have a clearinghouse for information in the form of the tipster executive committee and other government participants
the technologist must understand the user s job well enough to explain clearly how the technology could possibly help
this strategy does not reduce the chances of failure but reduces the risk that failure will be disastrous
in addition while these two applications require better recall than other commercial ones they do not probably place the same stresses on a detection tool because the user is searching in the context of a narrower range of document types and a narrower range of types of questions which they need to answer
the architecture has been developed to be as useful as possible to a variety of systems since the tipster community incorporates a widening sphere of vendors and researchers and requires a software architecture at its core which allows all of them to work together without too great a cost in adaptation of individual systems
the demonstration projects themselves were conceived of as partly having an educational mission nothing would be so effective in persuading people that the technology was for real that it had something to offer them and a bit about how it worked than seeing it and trying it in an operational mode
requirements for the particular application must be understood defined and baselined accounting for the user s current work flow and possible changes to it the target software hardware environment the data input output and storage formats maintenance and support documentation and training testing and evaluation plans
in the case of tipster the existence of an established and well recognized program has been helpful in gaining user trust but also the program workshops and evaluation conferences as well as the proceedings coming out of them have been useful in helping to explain to users what the technology does and how it works
feedback from initial demonstrations made it clear that while we could expect the interviewer to have roughly eight hours of training we needed to design the system to work with a totally naive interviewee who had never used a computer before
our technique involves the use of high discounts and appears to provide useful constraint without corresponding fragility in the face of novel material
this is illustrated in example NUM we only expected himi to claim that he was brilliant where the presence of the pronoun hei gives rise to two possible fsvs s
thus we are left with the third equation where both imitation and projection bindings yield legal solutions the imitation binding for h3pf is kzw i pf
an additional argument in favor of a general theory of colors lies in the fact that constraints that are distinct from the por need to be encoded to prevent hou analyses from over generating
for the moment we will just consider the colors pe primary for ellipsis and pe secondary for ellipsis as distinct basic colors to keep the presentation simple
we hope to have actual user trials of either the serbo croatian or the haitian creole system in the near future possibly this summer
null for any color constant c and any c coloured variable v a well formed colored substitution must assign to vc a cmonochrome term i.e. a term whose symbols are c coloured
to remedy this shortcoming dsp propose an informal restriction the primary occurrence restriction null in what follows we present a unification framework which solves both of these problems
the weak second order theory of two successor functions
the reachability algorithm is given below in figure NUM
semantic distance is zero in the beginning of the following list and increases same word form different word form from the key word form cars car other derivation of the root of the key word investigation investigate synonyms of key word car automobile other related words the semantic paths and their weighting are defined in the settings file
another use for such a compiler is suggested by the standard divide and conquer strategy for problem solving instead of compiling an entire grammar formula we isolate interesting subformulas and attempt to compile them
these lists are produced independently and there may be many ways to pair begin and end tags together
so without loss of generality we can think of the domain of the assignment function as a sequence xz x of the variables occurring in the given formula
this can be done by identifying contrasting features within the text surrounding l s occurrences that could help us to better differentiate between sp and rf sets
consider t1 whose root and foot differ in their sfs
however a closer look at tag shows that this is an oversimplification
in ss NUM NUM we address this concern by using a multi phase compilation
most lexical items will be specified as having an empty slash list
we compute the functor argument structure on the basis of a general selection relation
thus we say that f has been reduced by the schema in question
NUM NUM detecting auxiliary trees and foot nodes
in the first phase all sfs are raised
work is in progress on compiling an english grammar developed at csli
add a node n dominating this node
the use of a tlmsaurus can obviously set up the similar word independent of the tort us and has an advantage that some ambiguities in analyzing the corpus are solved
the internal structure of the clause pair consists of phrase like constituents these include nominative nc prepositional pc adjectival ac verbal vc and clausal constituents
let psc be the set of head lemmata verbs nouns and adjectives in the subcategorization cues i.e. best frames in the sf sets for a given corpus c let
by far the most frequent type of error was the inclusion of an accusative or dative np in a verbal frame when the verb in fact only takes a pp
in the current setting the algorithm is employed to rank the frames in a given sf set by using the relative evidence obtained for each frame in the set
so the current version of the learning procedure relies on manual post editing assisted by the sf ranking and examples from the corpus in order to discard f se frames
mary takes no consideration on it that mary shows no consideration for the fact that copula ncl nl nc2 n2
prepositional verbal frames are learned by the system by relying on pps as cues for subcategorization since the system can not differentiate between complement and adjunct prepositional cues it learns frequent prepositional adjuncts as well
in the experiment described in the previous section truly new prepositional frames behaved with respect to frequency of occu ence very much like errors and would possibly have been discarded by a filtering mechanism
although other attempts have been made to learn english verbal prepositional sfs from text corpora no previous work considered a i partially free word order language such as german nor differentiated between complement and adjunct prepositional cues
we have already developed a working bidirectional serbo croatian english system and are currently developing haitian creole english and korean english versions
while we would like as much user interaction as possible it is also important not to overwhelm the user with either information or decisions
we have substantiated this claim by specifying the linguistic extra semantic constraints regulating the interpretation of vp ellipsis focus soes adverbial quantification and pronouns whose antecedent is a focused np
it is in the nature of the diplomat system that such corpora particularly acoustic ones are not immediately available for processing
this paper presents a constraint based morphological disambiguation approach that is applicable languages with complex morphology specifically agglutinative languages with productive inflectional and derivational morphological phenomena
we use an initial set of hand crafted choose rules to speed up the learning process by creating disambiguated contexts over which statistics can be collected
rc rrc NUM ic rc NUM lc rc to illustrate the flavor of our rules we can give the following examples
another important feature of these rules is that they are applied even if the contexts are also ambiguous as the constraints are tight
our experience is that after processing with these rules the recall is above NUM while precision improves by about NUM percentage points
scorei incontext c pi count p count pmax incontext c pmaz
given a training corpus with tokens annotated with possible parses projected over selected features we first apply the hand crafted rules
in general all derivations in a lexical form have to be considered though we have noted that considering one level gives satisfactory results
it can be seen that well NUM of the sentences are correctly morphologically disambiguated with very small number of ambiguous parses remaining
in other words the following equivalence holds x x y y 3c x c x ay c y indeed the satisfiability problems of context constraints and equality up to constraints over finite trees are equivalent
in a chunk tagging application with only NUM or NUM tags in the effective tagset this approach based on the confusion matrix offers much less benefit
rule NUM if a word pair is composed of two terminology words its weight is strengthened
making the decision trees more conservative in this way can also lower recall
however many also remained unresolved and many of those appear to be cases that would require more than local word and part of speech patterns to resolve
the size of the training corpus a was almost NUM NUM words
theretbre the description is represented as a disjunction of the conjunctions inductive learning system needs the domain expert who gives the possible classes and provides each class with examples objects belonging to this class
we will here consider the former case
figure NUM results on the susanne corpus
symbol indicating the beginning o1 the word
wc call this method linear successive abstraction
internet christer c coli uni sb
we will next consider some examples from part of speech tagging
we call this partial successive abstraction
in the discrete case we thus have
or there can be an agreed upon period in which changes are made up to a certain level of effort or cost
all tipster materials including research papers are published and researchers are encouraged to present their results at other open forums
some commercial participants have found their way to muc and trec where their research can be benchmarked and shared with others
as with any other endeavor no failures means both that nothing will be learned and that nothing very difficult has been tried
the tipster community began the development of the architecture because of the need to accelerate the deployment of the technology it had developed
step six is to provide initial system support rapid responses to problem reports and as much hands on user support as possible
the way in which a system is presented during training and supported will make a big difference in how well it is liked
the general user students small businesses people at home certainly have very little interest in recall and even in precision
the cost for integrating modifying and maintaining the software is born by the using organization in fees to the vendor
that review neither constitutes cia authentication of information nor implies cia i endorsement of the author s views
at l t we evaluate the appropriateness of the obtained similarity by selecting a verbal meaning
tab NUM shows that the larger the similarity in bunrui goi hyou is the larger the obtained similarity is
quantity partitives where the embedded noun phrase is headed by an uncountable noun the first case are then divided into general partitives such as piece which serve only to quantify and typical partitives such as grain which are more descriptive
the embedded noun phr se will agree in number with the head noun phrase if flflly or strongly countable a kind of car NUM kinds of cars a kind of equipment NUM kinds of equipment
we fllrther subdivide meti ic classifiers depending on whether the resulting english noun phrase will have singular verb agreement measuri classifiers or plural verb agreement containfat classifiers as its default
in the analysis given above for japanese noun phrases of the form xc no n we have given no consideration NUM o the denotation of n except for when choosing the at propi iate translation for c
in order to concentrate on the translation of classifiers and number we will restrict our discussion to noun phrases of the type xc no n and not discuss the problems of resolving anaphoric reference and floating quantifiers
the resulting numeral classifier noun phrase can modify another noun phrase either linked by no of xc no n or floating elsewhere in the sentence typically directly after the noun phrase it modifies nxc
to correct the contexts the rules are once more applied in right order stem end change at first and the contexts are updated
then for every node n e n2 if we intend to assign n to the denotation of xi we indicate this by labeling n with a bit string in which the ith bit is on
the corresponding tree automaton is shown in figure NUM on closer examination of the transitions we note that we just percolate the initial state as long as we find only nodes which are neither xl nor x2
NUM in this setting the parsing problem becomes the problem of conjoining an automaton recognizing the input with the grammar automaton with the result being an automaton which recognizes all and only the valid parse trees
NUM the recognizable sets are closed under the boolean operations of conjunction disjunction and negation and the automaton constructions which witness these closure results are absolutely straightforward generalizations of the corresponding better known constructions for finite state automata
will be decidable and b that the translation to automata will go through as long as the atomic formulas of the language represent relations which can be translated by hand if necessary to tree automata
covers the preference order for multiple occurrences of the same type of any information structure pattern e.g. the occurrence of two anaphora or two unbound elements all nominal heads in an utterance are ordered by linear precedence in terms of their text position
this is due to the fact that taktfrequenz clock frequency conceptualized as clock mhz pair is a property of the cpu of computer system and therefore only a mediated property of computers as a whole hence the whole for part metonymy
bound element s rsb unbound element s anaphora xsbo elliptical antecedent xsbo d elliptical expression nom headt p nom head2 p p nom head the constraints holding for the ranking on the cf for german are summarized in table NUM
no packing or structure sharing techniques could be used since semantic interpretation occurs online thus requiring continuous referential instantiation of structural linguistic items cf
the dependency specifications allow a tight integration of linguistic grammar and conceptual knowledge domain model thus making powerful terminological reasoning facilities directly available for the parsing process NUM the resolution of textual ellipses is based on two major criteria a conceptual and a structural one
akku accumulator in lb is a nominal anaphor referring to nickel metallllydride akku nickel metal hydride accumulator in NUM a which when resolved provides the proper referent for relating ladezeit charge time in lc to it
in the scenario we have discussed the rece ipt handle r eventually will detect the success and the termination of the search head protocol
in this approach we let phrases that constituted alternative analyses for the same part of the input text be encapsulated in a container actor
that is a surface utterance may be translated differently depending on context
a dialogue analysis model with statistical speech act processing for dialogue machine translation
therefore push operation occurs again and a new ri diagram is initiated
in dialogue NUM utterances NUM NUM are part of an embedded segment
in this case it must be translated differently depending on context
the n gram of speech acts based on hierarchical recency approximates the context
in this section we present some motivational examples
we intend to use this to provide the theoretical background of the implementation of a garbage collection procedure which projects variables from the constraint store which are either local to a definite clause or zdegnote that this corresponds to NUM to NUM different bitstrings
as usual satisfiability of a formula in the language of ws2s by af2 is relative to an assignment function mapping individual variables to members of n2 as in first order logic and mapping monadic predicate variables to subsets of n2
in the worst case the number of states can be given as a function of the number of variables in the input formula with a stack of exponents as tall as the number of quantifier alternations in the formula
the projection construction as noted above yields nondeterministic automata as output and the negation construction requires deterministic automata as input so the subset construction must be used every time a negated existential quantifier is encountered
in this section we consider how we might do this by by the embedding 7the structure of the formula does often have an effect on the time required by the compiler in that sense writing mso formalizations is still logic programming
second order variables can also be used to pick out other properties of nodes such as category or other node labeling features and they can be used to pick out higher order substructures such as projections or chains
note that this property could include that some other node be the left successor of z with certain properties that is this general scheme fits cases where the intervening item is not itself directly on the path between x and y
we take a probabilistic approach where you estimate the likelihood that a noun appears as a title term and choose among the best scoring nouns
since newspaper articles in general do not have formal structure indicators we use some similarity function to discover the structure of text
thus it is expected that discarding parts dissimilar to the title reduces irrelevancy in text contributing to an improvement on the performance
NUM note that both paragraphs include very similar types of information but radically different intonational contours due to the discourse context
this phase which represents an extension of the rhetorical constraints arranges propositions to ensure that consecutive utterances share semantic material cf
step NUM the parallel text samples are aligned sentence by sentence on the word level this is best achieved using content word islands then expanding onto the remaining words while using some common sense heuristics in deciding the replacement string correspondences misaligned sections while different in print are nonetheless close phonetic variants
the architecture relies on a mapping between a two tiered information structure representation and intonational tunes to produce speech that makes appropriate contrastive distinctions prosodically
l h l h h ll the modules described above and shown in figure NUM are implemented in quintus prolog
the proof of the decidability of ws2s furnishes a technique for deriving such automata for recognizable relations effectively
it should also be noted here that while the above efforts usually attack the more general problem of speech understanding accuracy in an ad hoc speech production situation though limited to certain domains such as broadcast news our solution has been specifically tailored to clinical dictation and would n t necessarily apply elsewhere
on the face of it this seems to depend only on how good rules we can get
the result is english and french drafts of the instructions for the procedures defined so far by the author using the developer s tool
as the result we a quired all ac urate rate of NUM NUM tm le NUM an improvemellt of NUM NUM on the the result is rather good colnt aral h to the l erformance of all averag
if we set the first threshohl as NUM md throw away the second threshold then l he success rates ill tril le onfl ination will k llt NUM NUM NUM NUM a nd NUM NUM NUM NUM in l ajr ombilmtion
pure statisticm models for disalnl iguation tltsks sll l fl olll sparse data NUM robh nl v ltot l that even when ai plying smooth t chniques such as senuultic sinfilarity or lustering it is hard to avoid malting poor est ilnat iol s
we attribute this result to the hyl rid apl roach we used in which preferences with higher rdiabilities are used NUM rior to other on s in the disalnl iguation l rocess
on the left hand of each rule a one atonl pre t concet t dictionm y consists of al out NUM NUM con cepts where fbr eolleet t classification related coneepts are orgmfized in hierm chiem ar hitecture and a concept in h wer level inherits the f atures from its upper level concepts
as we use only relial le data from corl ora to make decision on pp atta hlnellt based m ra score many pps attachlnents may be left undetermined due to sparse
for example in the sentence peter broke the window by a stone we are sure that the pp by a stone is att u hed to broke v by knowing that stone n2 is an instrument for broke v
hlil la ll looking at v nl p n2 alone al out NUM to NUM according to hindle md r ooth NUM collins and brooks NUM
the server por null tion of the system performs all the document management text preprocessing and machine learning functions
clinical tests of the system equipped with the c box that are starting in early NUM will provide additional speakers
as an example consider the following pair of sentences sl portable from view of the chest
the first is the well known fact that there is agreement between the grammatical number of determiners and the grammatical number of the nouns they modify
the administrator is not familiar with this notation and can not easily understand it
however this belief is a fallacy as some recent empirical studies show
the administrator thinks it is strange that a section may belong to zero or more courses
however none of the specification paraphrasers proposed to date have in null cluded examples
note that in the tree t1 anchored by an equi verb the foot node is detected because the slash value is shared although the subj is not
its interpretation is indicated by the part in parentheses
the next nonmonotonic structure i want to discuss is any values
note also that the result of a w explanation is allowed to be fail
these requirements can be both structures in the subsumption order and nonmonotonic rules
in this screen the workspace pane contains the procedure being documented in an outline format
vous pouvez quitter la fen re save as file en cliquant sur le bouton cancel
in particular though current ocr technology is quite refined and robust sources such as old books poor quality nth generation photocopies and faxes can still be difficult to process and may cause many ocr errors
in fact we distinguish two types of errors introduced by the system errors caused by changing correct 3the ziff collection is distributed as part of the data used in the text retrieval conference trec evaluations
pr ylx lcb NUM ify x otherwise NUM NUM pr dd x lx pr ins x i NUM
in future work we plan to explore different heuristics to deal with word boundary problems and to incorporate other models of context representation including both slm approaches such as word trigram models and simple discourse structures
finally the system suffers from the common limitation of word bigram or trigram models in that it can not capture discourse properties of context such as topic and tense which are sometimes required to select the correct word
although the system introduces additional errors since all the strings in the ocr text are treated as possible errors and subject to change the number of corrected real word errors far exceeds the number of real word errors introduced
the system starts by assuming all characters are equally likely to be misrecognized with some uniform small probability and learns the character confusion probabilities by comparing the ocr text to the corrected ocr text after each pass
let tl t ti be the first i characters of the string that is produced by the ocr process for a source word s and let sl s2 sj be the first j actual characters of
argmaxw pr w i s argmaxw pr w pr slw s argmaxw pr w pr s w NUM
we plan to compile a knowledge of bilingual collocations by incorporating the method with conventional bilingual approaches
where ge is a ftmetion from s to the natural n tubers mapping a set x to the number of times it was produced by the sf mapping for a given corpus c fm ther i pk and pk are run ions
for instance in 4c the pronominal adverb daran about it is used as a pro form for the personal pronoun es it as the object of the preposition an about
for instance NUM is mapped to lcb pp an v arbeiten pp an n woche rcb with the vc nc and pc rules
in a correlative construct main clause vc the main verbal constituent in the clause v in vc v denotes the head iemm of the verbal constituent analogously for nc n
this is the source of most errors for flames involving the preposition gegen against bei by and nach to and can not be avoided given the learning strategy
if a lemma l occurs independently of a structure s then one would expect that the distribution of l given that s is present and that of l given that s is not present have the same underlying parameter
a copula clause with one nominal and one adjectival constituent is mapped to lcb pp p n n pp p a a rcb
it is based on shallow parsing techniques employed to identify high accuracy cues for prepositional frames the em algorithm to solve the pp attachment problem implicit in the task and a method to rank the evidence for subcategorization provided by the collected data
assert yn quest wh quest imperative are possible sentence types
robotag provides for learning other types of tags as well
table NUM a part of the syntactic patterns extracted from corpus
there are the previous plan based approaches for analyzing context in dialogues
ui will be uttered given a sequence of utterances u1 u2 ui NUM
it is even used as greeting or opening in korean
ds is an index that represents the hierarchical structure of discourse
let us turn now t o tim semanl ie construction component
the tree readings of the dpf correspond to the contexts of tim packed udrs
then the predicate match1 unifies lexarg with art if lexarg does not originate in i
there is no simple way to determine from a parse forest the number of its tree readings
there is exactly one vertex without predecessors called the top vertex or root
in the above version of udrs packing disjuncts are re fled
the upper labels refer to thc minimal scope domain tile operator must occur in
they report their best precision recall results for machine learned rules on the muc NUM task with equivalent f measures NUM of NUM NUM zf measure is calculated by
accordingly our output consists of verbmobil interface terms vits which differ slightly from the lud terms described above mainly in that they include non semantic information
all the three formalisms lud mrs and umrs have in common that they use a fiat nco davidsoniau representation and allow for the nndcrspecification o functor argmnent relations
interested in a more detailed discus sign of the iul erl retation of underspccified semanti representations is referred to bos NUM lcb NUM
however there are many important tasks that are at the same time small that is well defined and out of the way that is limited in the number of users they affect
the first feature equation annotated to this rule says that the value of the feature agr for agreement of the np equals that of the respective feature value of the vp
since the macro gets the entry nodes of the fea4 ture structures as arguments all the information present in the feature structures can be accessed within the macro which is defined as
the mappings between lud conditions and i rss are then detiued in NUM NUM where l is a label or hole and b is a labeled condition
the first is making sure that the technology is ready to transfer does it offer significant new capability and is it sufficiently well understood so that it can be made robust fast and be integrated with other necessary capabilities
as with the technology itself there did not appear to be sufficient interest in the commercial world to produce a set of agreed upon standards for integrating document detection and information extraction as quickly as the tipster program needed them
better informed users will make better decisions about what technology to purchase and how to use it so that the educational aspects of any work with the user will pay dividends far beyond the success or failure of any particular project
while all these improvements in the way the software is delivered and maintained will probably result in some cost savings they should also make it easier and less disruptive for the user to upgrade systems which is just as important
while the advanced software features they need may eventually become commonplace in commercial software this is likely to take a long time to happen since the general demand for such features appears to be moderate at best
a systems engineering contractor also keeps on file records of the design of all tipster systems so that government users can get detailed information about the configuration of any of the software which they may be interested in procuring
while some non government applications such as the development of formatted patient records from unformatted physician reports have been investigated there appears to be relatively low demand for this type of technology in the commercial world at this time
for project management issues NUM NUM NUM appropriate oversight by the management of the project leader is required self evaluation at the end of the project by both the project leader and the developer can be helpful
not all aggregates have atomic constituents
NUM NUM john and mary is leaving
the wiring and the piping are in the storeroom
i the lexical semantics of english count and mass nouns
NUM NUM this student are usually meticulous
the most commonly recognized one is that of kinds
NUM NUM this foliage is touching that wiring
but servings are not the only kind of unit
NUM NUM would you care for a coffee
note also that the choice of which nonmonotonic rule to apply in each step of a w explanation is nondeterministic
the extra conditions used when forming the union of the nonmonotonic parts of the sorts are the same as in the definition of a nonmonotonic sort and their purpose is to remove nonmonotonic rules that are no longer applicable or would have no effect when applied to the sort resulting from the unification
however when applying the default rule a value that has not been instantiated is compatible with this any no value
in this small hierarchy it is assumed that all possible values of a structure is a subtype of value
note that in the definition of the transitive verb the value any value is given to the appropriate attributes
therefore the default rule can make the conclusion that the structure is inconsistent which is what we desire
the main idea in their approach is that each node in a feature structure consists of a nonmonotonic sort
the reason for including it is to ensure that applying the default actually restricts the value of the sort
the definition of value constraints as a nonmonotonic rule makes use of negation interpreted as negation as failure
such sorts can contain two different kinds of information the ordinary monotonic information and a set of defaults
a scoring function is used to evaluate the relative merits of different sets of pairings
the text is divided into sections by observing which tags can possibly affect other tags
the sampling ratio is the ratio of negative to positive examples to use for training
this parameter takes values between NUM and NUM with lower values meaning more pruning
once the training texts have been represented as tuples the learning process can begin
it makes it easy for a user to mark and edit tags within multilingual texts
each position in the tuple s feature vector holds a value from a preprocessor field
a training set of NUM texts was used with a blind test set of NUM
whenever the features are compatible with such a foot node the sfs are raised according to the relationship between the root and foot of the auxiliary tree in question
in tag different functor argument relations such as head complement head modifier etc are represented in the same format as branches of a trunk projected from a lexical anchor
if we take into account the adjoining constraints or the top and bottom feature structures then it appears that the root and foot share only some selection information
since the domination link at the root of the adjoined tree mirrors the properties of the adjunction site in the initial tree the properties of the domination link are preserved
to allow for adjoining of auxiliary trees whose root and foot differ in their sfs we could produce a number of different trees representing partial projections from each lexical anchor
more flexible methods of combining trees with dominance links may also lead to a reduction in the number of trees that must be produced in the second phase of our compilation
bridge verbs e.g. equi verbs such as want or other heads allowing extraction out of a complement share their own slash value with the slash of the respective complement
this is again because heads in hpsg are not equivalent to lexical anchors in tag and that other local properties of the top and bottom of a domination link could differ
because the tag matching algorithm only ensures non overlapping tags within each tag type it is possible to have cases of embedded tags of different types like tagging boston as a location within the tag for boston edison company
null a number of proposals have been made for the representation of regular polysemy in the lexicon
the best system on the met task utilizing hand coded rules produced f measures of NUM NUM for person NUM NUM for place and NUM NUM for entity cf table NUM while the second place system posted NUM NUM for person NUM NUM for place and NUM NUM for entity
examples of server commands include NUM process a text for training or testing NUM learn a classifier NUM for a tag NUM evaluate a learned classifier on a text NUM load a previously learned classifier or save one for future use NUM change a learning parameter NUM enable or disable a lexical feature NUM learning to tag robotag must learn to place tags of varying types within the text
the particle lexicon is hierarchically structured and lists selectional restrictions with respect to the base verb selected
for each of the word classes the semantic space is divided into some NUM semantic fields
composition itself is implemented as follows relying on a separate lexicon for particles
in order to experiment the proposed model we used NUM dialogues recorded in real fields such as hotel reservation and airline reservation
selectional restrictions giving information about typical nominal arguments for verbs and adjectives are additionally implemented
main clauses covered by the grammar include copular constructs as well as active and passive verb second and verb final constructs
of these NUM were listed only in the published dictionary and NUM only in the acquired dictionary
subordinate clauses considered include those headed by daft that indirect interrogative clauses and infinitival clauses
the weight ck x of a frame x is used to estimate its probability pk x
their deiq ltion is non standard for instance all prepositional phrases whether complement or not are left unattached
mary thinks on it that john soon arrives mary thinks about the fact that john will arrive soon
the best frames in a set are the most probable frames given the evidence provided by the data
this rule is applied to werden to be passive verb second or verb final clause with one nc
a context constraint is a conjunction of equations between second order terms
there exists a correct and complete semi decision procedure for context unification
the variables t f g1 g2 will be unified with the interpretations of the proper constituents in the sentence during the derivation
after the derivation is complete further discourse processing infers the identity of the unrealized topic from among the salient entities in the discourse model
argument order within a clause by relaxing the subcategorization requirements of a verb so that it does not specify the linear order of its arguments
a each multiset ccg category encoding syntactic and semantic properties in the as is associated with an ordering category which encodes the ordering of is components
the dg constraints for the use of reflexives and intra sentential anaphora cover approximately the same phenomena as gb but the structures used by dg analysis are less complex than those of gb and do not require the formal machinery of empty categories binding chains and complex movements cf
for instance the predicate pronanaphortest from box NUM contains the grammatical constraint for pronominal anaphors according to which some pronoun and its antecedent must agree in gender number and person and the conceptual constraint described in section NUM the predicate nomanaphortest from box NUM captures the conceptual constraint for nominal anaphors such that the concept to which the antecedent refers must be subsumed by the concept to which the anaphoric noun phrase
ii the antecedent to which an anaphor r refers may only be governed by the same head h1 which d binds 7r if is a modifier of a head hg h2 is governed by h1 and a precedes 7r in the linear sequence of text items
the verbal functions can also specify a direction feature for each of their arguments notated in the rules as an arrow above the argument
in this paper we describe a method of improving the accuracy of automated speech recognition through text based linguistic post processing
they report some interesting preliminary results however these are not directly comparable to ours for two reasons
in the syntax given above name assigns a name to the defined rule and thus allows the user to use nonmonotonic information when defining linguistic knowledge
nonetheless there are other possibilities as well and we mention them briefly in the future directions section
section NUM demonstrates the application to scope underspecification to ellipsis and to the combined cases
this reflects the dependency of the interpretation of the second sentence on material in the first one
markov chain and p indicates a pause
figure NUM a NUM gram model viewed as a first order
the resulting curves mirrored the finergrain curves presented here
but it biases our perplexity bins at the extremes
very little accuracy is lost in making this assumption
clearly semantic emphasis has its say in the decision
all of the rates calculated are substantially less than a bit but this only reflects the stress regularity inherent in the vocabulary and in word selection and says nothing about word arrangement
signal analysis too has not yet been applied to very large speech corpora for the purpose of investigating prose rhythm though the technology now exists to lend efficiency to such studies
kager postulates with a decidedly information theoretic undertone that the resulting binary alternation is simply the maximal degree of rhythmic organization compatible with the requirement that adjacent stresses are to be avoided
they have instead continually re answered questions concerning isochrony
many names are composed of more than a single word in which case all words that make up the name are capitalized except for prepositions and such e.g. the united states of america
looking in isolation at a single equivalence class in the key the recall error for that clas s is just the number of missing links divided by the minimal number of correct links i e
we are switching the figure and th e ground that is we are switching our notion of where the base sets come from th e response rather than the key and of what defines the partitions on those base sets the key rather than the response
switching the figure and ground in the recall formula the scoring arithmetic for precisio n works itself out as follows where s is now an equivalence class from the response an d p s is the partition of s vis a vis the key s
this score of NUM NUM is the intuitively correct one that the syntactic measure fails t o calculate
to see how this works in practice consider the second problematic example noted b y sundheim et al
getting a bit more formal but not much let us define recall using these notions
it is of course the case that the response classes may not be proper subsets of the key
the key partitions this class into subsets lcb b c and lcb a where the latter is implicit
this implies that any notion of regular polysemy must obey the rules of inheritance
statistically frequent cases of regular polysemy are manually and explicitly encoded in the net
note that the mmw is a preference rule because there is a possibility of reflexive use of see
NUM i m not a very good swimmer
we introduce a domain closure axiom so lhat we only consider relevant constants used in the given senten es
voted into more than one class are handled by
analysis of the immediate context surrounding company names may lead to the discovery of key phrases like said it entered a venture and is located in
the cb un the most highly ranked element of cf u NUM realized in u corresponds to the element which represents the given information
the methodological framework for text ellipsis resolution is the centering model that has been adapted to constraints reflecting the functional information structure within utterances i.e. the distinction between context bound and unbound discourse elements
as far as proposals lot the analysis of textual ellipsis are concerned none of the standard grammar theories e.g. hpsg lfg gb cg tag covers this issue
in NUM cases the anaphora resolution process failed to resolve an anaphor thus leading to an incorrect call of the ellipsis handler and in the NUM remaining cases the triggering condition was not restrictive enough
local coherence in discourse is established whenever a center element of the previous utterance is associated with an expression that has a valid semantic inteq retation i.e. is realized in the following utterance
an appropriate role filler update links the corresponding concepts via the role charge time of and thus local coherence is established at the conceptual level of the text knowledge base
NUM of these NUM false positives were due to insufficiencies of the parsing process it failed to create suitable semantic conceptual representations blocking the triggering of the ellipsis handler
our aim is to provide an aac device which will be useful to this population both by allowing their output to be more standard and as a potential language intervention therapy tool
thus an outcome that did n t occur in the training data receives half a count an outcome that occurred once receives three half counts
these techniques typically require that a separate portion of the training data be held out from the parameter estimation phase and saved for determining appropriate back off weights
when using trigram statistics this quantity is p t NUM k tk NUM
the work presented in this article was funded by the n3 bidirektionale linguistische deduktion bild project in the sonderforschungsbereich NUM kiinstliche intelligeuz wissensbasierte systeme
it was tested on a part of speech tagging task and outperformed linear interpolation with context independent weights even when the latter used a globally optimal parameter setting determined a posteriori
this indicates that the successive abstraction scheme yields back off weights that are at least as good as the best ones obtainable through linear deleted interpolation with context independent back off weights
tiere p xi is the probability of the random variable taking the value xi which is for all possible outcomes xi and zero otherwise
the fundamental problem is that there is often not enough data to estimate the required statistical parameters i.e. the probabilities directly from the relative frequencies
the basic idea is thaf there is a substantial amount of information in the word suffixes especially for languages with a richer morphological structure than english
quite in general if n is the sample mean of n independent observations of any numerical random variable with variance a0 NUM i.e.
for instance of the errors in the NUM sets out of the NUM ambiguous sets examined containing incorrect sfs only about NUM were due to the fact that an additional accusative dative np was incorrectly included in a verbal frame although the prepos tion in the frame was subcategorized for
this allowed us to extend the icon prediction mechanism described in the communic ease maptmto the word level
several evaluations of the completed prototype system are planned and made possible by the setaside collected data
thus the system must be extremely robust and capable of handling any input given to it
the user may then select among the generated expansions with NUM additional keystroke for example
in icon prediction the user is prompted for valid icon sequences using lights on keys
xn corpus also contains many new words but the number is much smaller
NUM the wordkeys system wordkeys is a system based on full text retrieval of pre stored messages
note however that every proposition put forth by the generator is assumed to be incorporated into the bearer s set of beliefs
for the present implementation only properties with the same parent or grandparent class are considered to be alternatives to one another
for example the it cleft in NUM may mark john as standing in contrast to some other recently mentioned person
automatically generating natural language descriptions of software models and specifications is not a new idea
this paper presents such a tool the modelexplainer or modex for short
the lower tier in the information structure representation specifies the semantic material that is in focus within themes and themes
however this also means that the system can not detect semantic modeling errors
courses math100 and physics100 figure NUM two descriptions of is section of
initial development of modex was funded by usaf rome laboratory under contracts f30602 NUM c NUM and f30602 NUM c NUM
the modex is implemented in c on both unix and pc platforms
modex uses an interactive hypertext interface to allow users to browse through the model
the analyst has devised a data model and shows it to a university administrator for validation
certain aspects of the semantic form are considered unaccentable because they correspond to the interpretations of closed class items such as function words
the data used for analysis is partially based on the wordnet morphological information
we call this string key string hereafter
the success of the general minspeak r paradigm of vocabulary access led prc to start designing tailored prestored vocabulary programs known as minspeak application programs maps tm for specific populations of users
links to other words in the lexicon and specification of the type of link synonym hyponym etc
the frequency stored is retrieved from a large database of mainly written text the british national corpus bnc
only if the form is found there it is accepted as a lemma and added to the message index
trials with a number of different settings for the message retrieval algorithm have been carried out to improve message ranking
the main function of this lexicon however is to serve as a basis for the semantic query expansion
semantic query expansion is especially suited for communication aids where minimal input and high recall are the key factors
the different lexicons are text files and correspond to a simple and clearly specified format
the related words are re applied for another query to the index of the message database
to choose the right lexicon we had to bear in mind that wordkeys is a retrieval system for unrestricted text
note that this amounts to making use of only internal evidence
setting NUM full discourse interpretation is added to NUM
tile high level structure of lasie is illustrated in figure NUM
evaluation of an algorithm for the recognition and classification of proper names
ontext beyond the words in th
in a nondeterministic parser all NUM variants are used in the subsequent parsing
one might also want to introduce subclasses within the selected classes
ford to lie an organisi tion name
sin its non word error mode of operation the system treats every word that does not match a lexicon entry as a possible error
note also that the tf score can be used to establish a threshold or cutoff score to limit the number of candidates to consider
the error reduction rates in experiments NUM and NUM are respectively NUM NUM and NUM NUM higher than the rate in experiment NUM
in each experiment the system conducted four correction passes one initial pass with prior probability c NUM NUM and three feedback passes
in addition the system can learn the character confusion probability table for a specific ocr environment and use it to achieve better performance
obviously in practice we typically do not have the original text to compare to the ocr text or to use for correction
the goal of the work described here is to investigate the effectiveness and efficiency of slm based methods applied to the problem of ocr error correction
in addition the system can learn the character confusion probabilities for a specific ocr environment and use them in self calibration to achieve better performance
NUM retrieve up to m candidates from the lexicon for each possible errorj rerank the m candidates by their conditional probabilities to the error
the lexicon is generated from the training text it includes all the words in the training set with frequency greater than the preset threshold
default translate as x c of n where the classifier is replaced by a default that depends on the embedded noun and n is uncountable
we divide lassitiers into four ma ior tyl es unit meti ic l ul
the main criteria for the analysis are the restrictions placed in english on the countability and number of the embedded noun phrase in a partitive construction
the translation of classifiers is complicated by the fact that classifiers and their relationships to nouns are both arbitrary and language dependent
but it is our belief that rhythm makes a nontrivial contribution and that the tools of statistics and information theory will help us to estimate it formally
NUM NUM pal no mizu l cup of water NUM e lp of watedegf containfi
in other words it needs only little effort to translate a prolog program to a t ws2s one
one can now see that with the relational extension we can not only use those modules which are compilable directly but also guide the compilation procedure
the intuitive point should be clear it being stored in a global table so that we do not have to present them in each and every constraint
uppercase letters denote second order variables lowercase ones first order variables reflexive domination proper domination and NUM proper precedence
first we treat individual variables as set variables which are constrained to be singleton sets we can define the singletonhood property in mso tree logic
our discussion considers the kinds o fin formation that participants use to interpret an utterance even if it is inconsistent with their beliefs
NUM branches in the sequential structure enable the participants to retract attitudes via repair and to reason about the alternatives that they have achieved
among these NUM NUM high rank candidates NUM NUM are available in the dictionary which amount to NUM of all the available words in the whole table
the reason for approximate evaluation was that it was impossible to manually examine all NUM NUM terminology phrases therefore only randomly selected NUM NUM candidates were examined
since they are very common in scientific articles the ability to automatic identification of terminology could greatly assist any domain related natural language processing applications
therefore the ability to automatic identification of terminology could greatly aid any domain related natural language processing applications such as automatic indexing information retrieval and document categorization
proper names to extract terminologies from the original universal dictionary the frequency of each of the NUM NUM words in cw corpus was compared to the frequency in xn corpus
perhaps the most obvious enhancement to our representation involves giving the learning system the actual text of the token in the feature vector
for example joint venture is an important term in the wall street journal wsj henceforth database while neither joint nor venture are important by themselves
the first point is how to locate text passages that are worth looking at it is impractical if not downright impossible to read all NUM documents some quite long in under NUM minutes
the echidna lives by itself whereas the african porcupine either lives by itself or in groups
a direct comparison is generated by peba ii whenever the user requests a comparison between two entities
this subcorpus contains NUM sentences from the encarta corpus and NUM from the groliers corpus
the features of a particular animal the sheep for example might also vary geographically
the architecture of the peba ii system is shown in figure NUM the components are as follows
the monotreme is a type of mammal that lays eggs with leathery shells similar to reptiles
the user s degree of familiarity with the potential comparators can help in making a choice
other more advanced techniques of phrase extraction including extended n grams and syntactic parsing attempt to uncover concepts which would capture underlying semantic uniformity across various surface forms of expression
no tipster system with which i am familiar has actually reached this stage as of the writing of this article
customer service as many commercial organizations are aware makes the difference between success and failure in many instances
the central role of technology transfer in the initial formation of tipster had several causes
technology transfer has been an important part of the tipster text program from the beginning
this step tests the effectiveness of step two and prepares the way for step four
it can perhaps serve as a basis for others to reflect on the same issues
the development of new technology to support a user task must be a collaborative process
the system of distribution that exists now for tipster gots is informal and low cost
if this is to be the case then tipster technologies would have to become part of standard commercial offerings
it is the user s purview to control his her own work style after all
the constraints are solved by context unification
we use constraints interpreted over finite trees
a uniform approach to underspecification and parallelism
for gapping constructions contexts with multiple holes need to be considered
both alternatives lead to new rigid flexible situations
and figure NUM the equality up to relation
context unification is the problem of solving context constraints over finite trees
the first clause of NUM is scope ambiguous between two readings
the user can highlight an incorrect portion using the touchscreen and respeak or type it
figure NUM screen shot of user interfaces interviewer left and interviewee right
thus the chart contains multiple possibly overlapping alternative translations
the only change that the user sees is that the quality of translation improves over time
this requires a careful balance which we are trying to achieve through early user testing
the actual communication process is quite flexible but this is a normal scenario
addition of new engines to occur within an unchanging framework
differences in the development times and costs of different technologies
for the purposes of this paper the most important aspects of the memt architecture are the initially deployed versions are quite errorprone although generally a correct translation is among the available choices and the unchosen alternative translations are still available in the chart structure after scoring by the target language model
figure NUM the transitions of dialogue NUM
NUM dab peteri e hen brief bekommt erwartet er
NUM maria erz hlt eine geschichte fiber sichi
but one can easily think of cases where this rule is overridden
needless to say any errors and infelicities which remain are ours alone
the resulting text includes a comparison with the goat but is aimed at describing the sheep and hence goes further than a direct comparison between the sheep and goat
these are probably two of the easiest properties to deal with it remains to be seen to what extent the mechanisms we propose will generalise to other attributes
we would like to thank mike johnson of macquarie university and the members of the language technology group at microsoft research institute for many discussions related to this work
the text shown above very carefully describes both similarities and differences for only the most salient features which clearly distinguish the animals
the user poses new discourse goals to the system by clicking on any of the hypertext tags and the cycle continues
we have identified three different types of comparative forms that appear in descriptive texts which we refer to here as di
for example peba ii currently generates the following sentences the platypus is about the same length as a domestic cat
in this case it is important that when describing the focused entity it is sufficiently distinguished from the potential confusor
it can significantly reduce the manual effort in the generation of terminology dictionary
a chinese word is usually composed of no more than NUM chinese characters
kulehsupnila is used to accept the previous utterance in korean
ii xa bill a xt c xot by applying the algorithm for context unification to this constraint in particular to part i as demonstrated in figure NUM we can compute the context c to be ay speak chinese y
the primary potential use for diplomat identified so far is to allow english speaking soldiers on peace keeping missions to interview local residents
that is one would not expect any language to explicitly verbalize the concept of for example manner of motion verbs which specify the specific instrument used
hindle and rooth NUM ratnaparkhi reynar and roukos NUM brill and resnik NUM collins and brooks NUM
to avoi l using very low frequen ies we set two thr sholds for each one above
in the later discussion the four head words are referred to as a quadrul le v nl p n2
here we use preference rules to determine pp attachments NUM y judging features of head words and conceptual relationships among them
lwe assulue that only two kinds of pp atta hmerits vp or np attachment in the training data
here we use two ammtated corpora edr english corpus NUM and susanne corpus a to supl ly training data
these relationships which reflect the role expections of the pre1 osition sut l ly important chics for disambiguation
however corpus based approaches shffer fi om the notorious sparse data problem estimations based on low occurrenee frequencies are very unreliable and often result in bad performances in disambiguation
NUM try the rules related to the prel osition if only one rule is applicable use it to decide the attachment and then exit
in the simplest case the noun phrase xc no n should be
brandon to be a person name
in english numerals can directly modify countable nouns x n
examples of rein classifiers are given in table NUM
the global performance as measured by the proportion of correct classifications i.e. both and increases as the f score increases
still it is interesting to note that quite a reasonable performance can be obtained just by looking at the NUM most indicative pre and post boundary trigger words
this score is thus high for words that are likely based on NUM and reliable based on c predictors of small clause boundaries
the best performance was obtained using a net with NUM hidden units a window size of NUM and the output unit threshold set to NUM NUM
NUM boolean encoding of triggers xi NUM i c NUM is set to NUM NUM if the word is the ith trigger and to NUM NUM otherwise
any opinions findings conclusions or recommendations expressed in this publication are those of the author s and do not necessarily reflect the views of the national science foundation
given a window size of w and c features per encoded word the input layer is dimensioned to c x w units that is w blocks of c units
to illustrate the last point we present a graph that shows a comparison between the three encoding methods used for a window size of NUM figure NUM
figure NUM demonstrates the recall precision curves of two corpora using chi method
unfortunately the original government time estimate for architecture definition was low and a concrete definition of the architecture were not available during the demonstration design phase
within the limitation of the evaluation methods we can conclude that the performance of chinese inquery is quite satisfactory and conforms to that of inquery in other languages
based on work in english and japanese it is expected that a combination method combining a word based query with its character based raw text would perform best
in our first version of the chinese ir system we convert between whatever character sets are represented in our document database and whichever encoding the user has requested
the fundamental concept of what is an indexable word or term changes from language to language as does the concept of a word stem or root
even though the contract would have benefited from a joint government and contractor requirements analysis the central problem was not in understanding requirements but rather prototyping developing technologies
since this did not produce good results we modified the feedback selection techniques to select significant pairs of adjacent characters from the relevant documents bigram model
a user may handsegment the query the query may be segmented automatically or adjacent bigrams from the query may be used
among the most relevant lessons keep the scope of technology al development efforts small until an advanced technology is proven to work for a given language
equation NUM represents the approximated sentential probability
for future work we intend to elaborate upon and extend further the techniques described here
dags after the next two recursive applications of rl d NUM and d NUM respectively NUM are shown in figure NUM
in the experiment all features around NUM syntactic semantic features except for one in the example in this paper were able to be used in propagation
however in the experiment on link system using a fairly broad grammar over NUM rules precompilation terminated with only a marginally longer processing time
ypo head sem owner np1 head sem this rule is used to parse phrases such as kris s desk
in the case when p d nil the top node in d1 will have no connections to the rule dag under the lc arc
in many unification based systems subsumption is used to avoid redundancy a dag is recorded in the table if it is not subsumed by any other one
let a be a dag created by the first application of the rule r and b be a dad created by the second application during the top down propagation
if such a constraint is found the function selects a path in x or y and detects its last arc feature as being involved in the possible loop
by doing the empirical analysis of precompilation and parse efficiency for different grammars we will be able to conclude the practical applicability of the proposed method
in our case unseen words never occur for 2this ignores edge effects for s c sl s2 sa n k NUM but this discrepancy is negligible when n is very large
this smallest class has a few common words such as refuse used as a noun and as a verb but most either occur infrequently in text obscure proper nouns for example or have a primary pronunciation that is overwhelmingly more common than the rest
later bins are also likely to be prejudiced in that direction for the inverse reason the increasing frequency of multisyllabic words makes it more and more fashionable to transit to a weak stressed syllable following a primary stress sharpening the probability distribution and decreasing entropy
sentence perplexity pp s is the inverse of sentence NUM probability normalized for length NUM p s r NUM where p s is the probability of the sentence according to the language model and isi is its word count
each sentence in the control set was permuted with a pseudorandom sequence of swaps based on an insensitive function of the original that is to say identical sentences in the corpus were shuffled the same way and sentences differing by only one word were shuffled similarly
we describe an information theoretic model for measuring the regularity of lexical stress in english texts and use it in combination with trigram language models to demonstrate a relationship between the probability of word sequences in english and the amount of rhythm present in them
to gauge the regularity and compressibility of the training data we can calculate the entropy rate of the stochastic process as approximated by our model an upper bound on the expected number of bits needed to encode each symbol in the best possible encoding
early bins with sequences that have a small syllable rate per word NUM NUM in the NUM bin for example are predisposed to a lower stress entropy rate since primary stresses which occur roughly once per word are more frequent
we observe that the average number of syllables per word is greater for rarer word sequences and to normalize for this effect we run control experiments to show that the choice of word order contributes significantly to stress regularity and increasingly with lexical probability
achieving this ambitious goal depends in large part on allowing the users to interactively correct recognition and translation errors
see the following examples in figure NUM noble man is a co hyponym to the other three hyponyms of human even though the first three are related to a certain education and noble man refers to a state a person is in from birth on
in figure NUM the entry bank1 a financial institutzon that accepts depossts and channels the money into lending activities may have the synonyms depository financial institution banking concern banking company which are not synonyms of banks a building in which commercial banking is transacted
however they do not have to agree completely with the net s top level ontology since a lexicographer can always include relations across these fields and the division into fields is normally not shown to the user by the interface software
others as verbs of introducing direct speech annabel squeaked why ca n t you stay with us or verbs expressing the causation of the emission of a sound he crackled the newspaper folding it carelessly
the particular resource situation for german makes it necessary to rely to a large extent on manual labor for the creation process of a wordnet based on monolingual general and specialist dictionaries and literature as well as comparisons with the english wordnet
a general practical method for handling sparse data that avoids held out data and iterative reestimation is derived from first principles
to put it a bit more abstractly we need to calculate the standard deviation of a non numerical random variable
both taggers handled unknown words by inspecting the suffixes but the hmm based tagger did not smooth the probability distributions
in the general case where we have m one step generalizations c of c we arrive at the equation
since the probability estimate is a linear combination of the various observed relative frequencies this is called linear interpolation
thus it stands the best chances of the relative frequencies being acceptably accurate estimates
to the extent that the project leader can also communicate the excitement of developing a new capability and create a team which enjoys working together these factors will cause people to put forth their best effort to make the project successful
we therefore propose a mixed approach treating irregular particle verbs by enumeration and regular particle verbs in a compositional manner
figure NUM protocol for establishing a dependencies
container actors comprising single word phrases are called lexical containers
therefore it is only considered when the syntactic criteria are fulfilled
we have presented a restricted approach to parallelism for object oriented lexicalized parsing
figure i protocol for word actor initialization
the computationally most expensive methods we consider are syntaxcheck and conceptcheck cf
for simplicity we assume a standard left to right depth first interpreter for the execution of the programs
nonetheless the automaton compiled from a much cleverer formalization would still be essentially the same
for example recall the definition of asymmetric c command and its associated automaton in figure NUM
in the meantime for tests we are using a comparatively simple implementation of our own
all other transitions are to a4 figure NUM the automaton for ac com xl x2
finally tree automata can string languages so mso logics are limited to context free power
clearly the automaton recognizes all trees which have the desired c command relation between the two nodes
as we validate this rule against the training corpus we find some supporting evidence but also many cases where the rule ca n t apply like the one below are unchanged in position from the prior examination
this is accomplished by the following simple set of rules
visibly the time needed per reading remains approximately constant in the construction of the underspecified representation whereas it grows sharply when the ambiguities are enumerated
the mrs approach is restricted to adjunct on ambiguities while the othex approaches are applicable to all the kinds of ambiguities mentioned
i act el have a actor q a aobjeet el o a time el i NUM NUM if a buys o at tinle i then a has o at time i NUM this preference of another inertia of ownership is weaker than the former preference NUM ecause time interval is longer than the fornler t reference
it also gives the direction of this matching by fixing in which lexical item an argument originates see last slot of lcxical entries
hel ween the t arser and the semantic onsl rllc ion colnponen oo
to compute the correlation coefficient of all tri grams we should n t set the null hypothesis to p abc p a xp b xp c otherwise we would be faced with the critical problem of data sparseness and then get unreliable and vulnerable results
there are many statistical methods to calculate words correlation coefficient including co occtarence frequency NUM mutual information NUM generalized likelihood estimation NUM chi square test NUM NUM dice coefficient NUM etc
in addition many tri grams are proper names because most of chinese names are composed of NUM characters
the following sections are organized as such section NUM introduces the identification of domain specific words section NUM describes how to extract terminology words from the universal dictionary section NUM presents the method for terminology phrase extraction section NUM provides detailed experimental results the final section is the concluding remarks
therefore new words were only extracted from the top NUM tri grams and the top NUM NUM grams
experiment shows that NUM NUM percent of all the occurrences of computer terminology can be identified with this terminology dictionary
there are also many valuable works in china especially about the distinctive new word extraction of chinese text
abstract terminologies are specialized words and compound words used in a particular domain such as computer science
unfortunately the collection of terminology information is very difficult and requires much tedious and time consuming manual work
the idea is to let the probability estimate NUM x i c in context c be a flmction g of the relative frequency f x i c of the outcome x in context c and the probability estimate p x c ill context c
john likes mary has only one reading semantically so just one of its analyses 5f 5g is discovered while parsing NUM
signs different types to john likes and mary pretends to like thus losing the ability to conjoin such constituents or subcategorize for them as a class
although rule blocking may eliminate an analysis of the sentence as it does here a semantically equivalent analysis such as 5g will always be derivable along some other route
no constituent produced by bn any n NUM ever serves as the primary left argument to bn any n NUM
moreover since fll serves as the primary subtree of the nf tree nf fl NUM can not be the output of forward composition and is nf besides
in addition to the familiar atomic nonterminal categories typically s for sentences n for 1namely mary pretends to like the galoot in NUM parses and the corner in NUM
if we group a sentence s parses into semantic equivalence classes it always turns out that exactly one parse in each class satisfies the following simple declarative constraints NUM a
theorem NUM assuming pure ccg where all possible rules are in the grammar any parse tree is semantically equivalent to some nf parse tree nf
because of the content generator s use of the rhetorical contrast predicate items are eligible to receive stress in order to convey contrast before the contrasting items are even mentioned
in many applications for computational linguistics for example when doing semantically based translation as in verbmobil the german national spoken language translation project described in section NUM a complete interpretation of an utterance is not always needed or even desirable
the second of these tasks however relies on information such as the construction of referring expressions that is often considered the domain of the sentence planning stage
based on this contrastive focus algorithm and the mapping between information structure and intonation described above we can view information structure as the representational bridge between discourse and intonational variability
aset z the set of alternatives for object x i.e. those objects that belong to the same class as x as defined in the knowledge base
a lud representation u is a triple hu lu cu where hu is a set of holes variables over labels lu is a set of labeled lud conditions and cu is a set of constraints
material may be in focus for a variety of reasons such as to emphasize its new status in the discourse or to contrast it with other salient material
after applying the third step of the focus assignment to NUM the result appears as shown in NUM with tube contrastively focused as desired
such situations are produced only when the contrasting propositions are gathered by the content planner in a single invocation of the generator and identified as contrastive when the rhetorical predicates are applied
the ranking algorithm assures that messages which are retrieved but are not considered very relevant for a query are put lower in the list or excluded from the display
table NUM module contritmtions for time expressions
the input text is first tokenise d
this constitutes an important step towards the design of truly multilingual tools applicable in key areas such as information retrieval and intelligent internet search engines
NUM x precision x recall f precision recall
a few further classes one might chose to identify
there are tour different settings of the system
we have not done so here for two reasons
this experiment demonstrates tim power of designator features across all proper name types and the importance of the alias feature for companies
other potential learning or statistical approaches for a problem like this e.g. neural nets or hidden markov models did not offer this advantage
these are combined with a matching algorithm to produce complete tags
to this end robotag was designed as a client server architecture
the client interface is an enhancement of a manual annotation tool
there are several parameters to obotag that affect tagging performance
figure NUM shows the working development system
therefore if we use n utterances linearly adjacent to an utterance as a context we can not refleet the hierarchical structure of a dialogue in the model
since a syntactic pattern can be matched with several speech acts we use sentential probability p uilsi using the probabilistic score calculated from the corpus
for english the muc NUM wall street journal corpus was used
uj and uk are the utterances which uj is hierarchically adjacent to ui and uk to uj where NUM j k i NUM
therefore we approximate the context for an utterance as speech acts of n utterances which is hierarchically recent to the utterance
sentence type main verb aux verb clue word are selected as the syntactic features since they provide strong cues to infer speech acts
three experiments have been conducted for spanish
since previous speech acts constrain possible speech acts in the next utterance contextual information have an important role in determining the speech act of an utterance
since a dialogue has a hierarchical structure than a linear structure the discourse structure of a dialogue must be analyzed to reflect the context in translation
figure NUM feature strengths for english
rohotag scores on the test set are reported in table NUM
at the end of this selection process only paths of the strongest type are retained in the tinal path list
the arrival of a message at an actor triggers the execution of a method a program composed of grammatical predicates
xs cs spoken by vary varx NUM x c two e language a xt c two a language
though related in some respects there are formal differences and differences in coverage between this approach and the one we propose
null as a demonstration system the goal was to port phase one technologies to chinese
all involved parties should agree in advance on what constitutes a successful system development
we believe the following can be learned from this effort NUM basic linguistic resources
chinese has one uniform character set and therefore does not provide as many easy boundaries
it is possible that a combination of bigram treatment with segmentation would produce consistently good results
furthermore this parsing is done using essentially domain independent syntactic information
include support for any component software as a separate task for project scheduling and budgeting purposes
developers need to manage requirements crop and identify potential negative impacts on scheduling initiatives
the use of tdm was the primary means of demonstrating tipster compliancy another phase l goal
for example there are specific rules for choosing among alternative meta plans on the basis of clue words implicit expectations or default preferences
these expectations are independent of the belief state of an agent and are specified down to the semantic and sometimes even lexical level
thus where we refer to the displayed interpretation of an utterance we mean displayed given the perspective of a particular speaker
her explanation would include the metaplanning assumption that he was doing so as part of an adopted plan to get her to produce an informref
sets g of equations in solved form i.e.
for example if external appearance is the most important attribute then we would want to compare the echidna to the porcupine
this assignment is made with respect to two data structures the discourse entity list delist which tracks the succession of entities through the discourse and a similar structure for evoked properties
while the present implementation only considers entities with the same parent or grandparent class to be alternatives for the purposes of contrastive stress a graduated approach that entails degrees of contrastiveness may also be possible
while research on generating coherent written text has flourished within the computational linguistics and artificial intelligence communities research on the generation of spoken language and particularly intonation has received somewhat less attention
the process of natural language generation in accordance with much of the recent literature in the field is divided into three processes high level content planning sentence planning and surface generation
since el and e2 are both instances of the class amplifiers and cl and c2 both describe the class araplifiers itself these two pairs of discourse entities are considered to stand in contrastive relationships
the echidna has a lifespan of NUM years in captivity whereas the african porcupine has a lifespan of up to NUM years
in german however not many large scale monolingual resources are publically available which can aid the building of a semantic net
diplomat thus involves research in mt speech understanding and synthesis interface design as well as wearable computer systems
over the years a number of techniques have been proposed to handle this problem
third unlike our approach even the current sg model for anaphora resolution does not incorporate conceptual knowledge and global discourse structures for reasons discussed by lappin and laess
here are some illustrative comparisons from our corpus powerful and aggressive animals about the size of a large dog baboons have strong elongated jaws large cheek pouches in which they store food and eyes close together
NUM a comparison is the linguistic realisation of a set of one or more comparative propositions where the purpose of the set of propositions is to draw the hearer s attention to one or more differences or similarities between two entities
we will adopt the following definitions a comparative proposition is a proposition whose purpose is to draw the hearer s attention to a difference or a similarity that two entities have for the value of a shared attribute
corpus derived property classification system the discourse plan used here pairs up those at tributes which are of a similar type for exam ple measurements such as height and length and compares their values
the main difference between a clarificatory comparison and a direct comparison is that a clarificatory comparison is made within a text whose purpose is to describe one entity and not purely to provide a comparison between two entities
in the first instance we have concentrated on the domain of animal descriptions we intend to widen the scope of this analysis to other domains in order to provide a more domain independent theory of comparative forms
as always our methodology is to pursue solutions that first assume a considerable amount of precompiled knowledge and then introduce generalisability and flexibility through subsequent parameterisation rather than beginning with a very limited coverage solution that works from first principles
derivates and a large number of high frequent german compounds are coded manually making frequent use 1we have access to a large tagged and lemmatized online corpus of NUM NUM NUM
a simple automatic pattern matching program was used to identify terminologies and NUM NUM occurrences of terminologies were spotted
table NUM features used in learning
making the matching algorithm sensitive to lexical features should help correct this
decision trees are learned to predict where users begin and end tags
this is one way that robotag could adapt to new extraction domains
he expects that peter will get a letter
germanet is facing the problem that lexical entries are integrated in an ontology with strict inheritance rules
we used umls lexicon and metathesaurus available from the national library of medicine as well as a cornmercial radiology oncology spell checker
the initial validation is based on estimated distribution of l within sp and rf sets and it may look as follows
acquiring german prepositional subcategorization frames from corpora
starred structures are considered to be errors
NUM er hat daran gedacht dat3
NUM mary wird daran erinnert da6
NUM jetzt arbeitet der student daran
rna ny linguist la my two a language lamx spoken by varyqvarx
2notice that closure is applied to the solved form of the combined constraints i.e.
standard theories for scope underspecification make use of subtree relations and equality relations only
equations and subordination constraints alone do not provide us with a treatment of parallelism
our algorithm proceeds on pairs consisting of a constraint and a set of variable bindings
the simplest classification problem involves learning to distinguish positive and negative examples of some concept
robotag would have ranked 2nd among the met systems on the japanese entity task
our english score is significantly better especially for the person and place tasks
because their japanese results were not reported we can not compare our japanese performance
we briefly present the multi engine machine translation memt architecture describing how it is well suited for such an application
the editor allows the interviewer to easily view and select alternative translations for any segment of the translation
due to the distinct characteristic of chinese there is still no systematic approach to generate practical and relatively complete chinese terminology dictionaries from on line corpora
although all the permutations have the same propositional interpretation see fatma ahmet each word order conveys a different discourse meaning only appropriate to a specific discourse situation
topic focus of a sentence and uniformly handles word order variation among arguments and adjuncts within a clause as well as in complex clauses and across clause boundaries
in figure NUM every word in the sentence is associated with a lexical category right below it which is then assoc lcb ated with an ordering category in the next line
backward composition b rl argsy x i argsx u lcb y rcb x i argsxu argsy c restriction y np
we assume that a category x i0 where there are no arguments left in the multiset rewrites by a clean null up rule to just x
adjuncts are assigned a function category si lcb s rcb that can combine with any function that will also result in s a complete sentence
what is novel about this formalism is that the predicate argument structure and the information structure of a sentence are built in parallel in a compositional way
the forward and backward slashes in the category indicate the direction in which the arguments must be found and the parentheses around arguments indicate optionality
in addition it provides a uniform approach to handle word order variation among arguments and adjuncts and as we will see in the next section across clause boundaries
in general sl and s2 correspond to formulae invo ving atomic sets and NUM operators sl sll u u 81m and s2 s21 u u 82n
our choice are parse forests since there are wellknown methods of construction for t hem and it is guarant eed that every syntactic ambiguit y can be represented in this way
several types of syntactic ambiguities can be distinguished
a resolution procedure derives the hillfledged semantic representations
let si be a set of tree readings
the root is marked with the start symbol
in the implementation contexts are represented by prolog variables
apply the procedure to all successors of v
we experimented also with various pruning criteria passages could be either imported verbatim into the query or they could be pruned of obviously undesirable noise terms
finally some experiments involved the fragments stream
to create a tuple from a token robotag collects the preprocessor features for the token as well as its immediate neighbors
the mean distance standard deviation and match threshold determine the distance interval within which the matcher searches for tag pairs
for each begin tag found there may be several plausible end tags that could pair with it and vice versa
we believe that allowing the user to give direct feedback to the learning system is key to rapidly addressing new extraction tasks
these include graphical displays of the induced logic for tagging cf figure NUM graphical displays of tagging accuracy i.e.
ne unabh a agig wird er fiir ca NUM stunden mit strom versorgt
we did an exl eriment to test our lnethod
recent work has turned to corpus based or statistical approaches e.g.
we successfully disalnbiguated NUM NUM of th test data
e nouns starting with it calfital letter are replaced with lmme
recent work has been dependent on corpus based approaches to deal with this problem
the resolution of prepositional phrase attachment ambiguity is a difficult problem in nlp
fable NUM results of the test in pp attachment
we would like to stress that while the experiments described in this paper are relatively modest and preliminary the system we designed is robust and fully automatic there is no human intervention involved
NUM conceptual relationships NUM etween v and n2 or between nl and n2
the information or clues we use are the following NUM syntactic or lexical cues
in this section we discuss the major steps in the c box algorithm
using n best sentence hypotheses may also alleviate the need for parallel correct transcriptions in the training sample as multiple hypotheses can be aligned in order to postulate correction rules
in the experiments described in this paper we used about NUM reports NUM kbytes of parallel text plus an additional NUM correct transcriptions corpus total NUM NUM mbytes
in addition the use of a vector space querying to find candidate lexical entries including our special approach to word decomposition and scoring can present problems when processing some ocr errors especially short strings
katja markert is supported by a grant from dfg within the freiburg university graduate program on tluman and ar
thus the problem of calculating w is reduced to estimating the word bigram probability pr wil w NUM and the word confusion probability pr silw
we also ran another NUM experiments with the large data set focusing on the region defined by the parameters that achieved the best results in the preceding experiments i.e.
as noted above this is the number of links necessary to fully reunite any components o f the p s partition
key links a b b c response links a c the key generates a single equivalence class s a b c rcb
the recall scoring procedure operates by merging the subsets of a key equivalence class that are defined by equivalence classes in the response
for recall sundheim et al advance the desirable score of NUM NUM which is not obtained by the syntacti c scoring measure
then we define the following functions over s p s is a partition of s relative to the response
for example say the key generates the equivalence class s lcb a b c d and the response is simply a b
the size of the class i s isi NUM and the minimum number of links necessary to establish the class i s
these NUM NUM phrases as well as the NUM NUM words compose our computer terminology dictionary
scorepre w c w b NUM w b w scorepost w c b w NUM b w w
wait lists and all table NUM sample of misclassifications on unseen data net with encoding of pos and triggers w NUM h NUM
whereas for the encodings pos only and pos and triggers the peaks are in the region between NUM NUM and NUM NUM for the triggers only encoding the best f scores are achieved between NUM NUM and NUM NUM
icl ssi catidegnr tdeglprdegcisidegnlrec lllfscdegrel0 NUM o NUM NUM NUM NUM NUM some general trends are observed as the window size gets larger the performance increases but it seems to peak at around size NUM
where c is the number of times w occurred as the word before after a boundary and NUM is the bayesian estimate for the probability that a boundary occurs after before w
in the case of human to human dialogues the speech recognizer s output is a sequence of turns a contiguous segment of a single speaker s utterance which in turn can consist of multiple clauses
l m i am ii correction of frequent tagging errors and iii generation of segment boundary candidates using some simple heuristics to speed up manual editing
mrs re ambiguates the parse tree only afi erwards within semantic constrn tion
for usability issues NUM NUM we have less experience
the first two e e j and j e j have been completed j j j is in progress
company tree example context is NUM
only a few new features added to the core feature set allows for significant pcrfommnce improvement
the recognition task can be broken down into delimitation and classification
these language specific modules are highlighted in figure NUM with bold bordcrs
proper names represent a unique challenge for mt and ir systems
brandon context the new company safetek will make air bags
in addition ambiguities with delimitation are handled by including other predictive features within the templates
since rechner subsumes lte life at the conceptual level nomanaphortest succeeds
fig NUM which depicts two instances of anaphora resolution
hence our proposal provides a more tractable basis for implementation
this can be described in terms of two phases NUM
the support structure for such a system must be developed and refined as the technology matures in order to be able to handle any future problems
when a semantic form for an event of interest is encountered a ddo is generated and any slots already found by the interpreter are filled in
the inquery technology has been formally evaluated in tipster and trec trials in english spanish and japanese with outstanding results and comparable performance in each language
in the user guided approach the user gets the added benefit of immediate feedback as to which concepts in the collection are related to the query
in relevance feedback selected documents are processed by the system and terms which are suggested by those documents are added to the original query
however the characters picked can and often do occur anywhere in the word and no easy algorithm exists to determine which characters these are
these delivery dates should be adhered to to the extent possible and any slippages should be documented and the cause for the slippage understood
text and user interface issues writing style varies according to language including right left left right or top to bottom starting on the right
ideally name recognition should be efficiently interleaved with segmentation so that when segmentation fails on a short sequence the name recognizers can be called
the quantities of accepted tri grams and NUM grams is also smaller than that of bi grarns
null r tag n grams the probability of tag t i at position k in the input string denoted t given that tags NUM n NUM t k NUM have been assigned to the previous n NUM words often n is set to two or three and thus bigralns or trigrams are employed
we can then build the estimate of p x i ck on the relative frequency f x i ck in context ck and the previously established estimate of p x i ck NUM
we thus have to estimate the two following sets of probabilities lexical probabilities the probability of each tag t i conditional on the word w that is to be tagged p r i i wr i often the converse probabilities p w are given instead but we will for reasons soou to become apparent use the former formulation
blending the involved distributions f x c and NUM x i c rather than only backing oft to c if f x c is zero and in particular instantiating the flmction g f p to a weighted sum distinguishes the two approaches
however if we look at the sequence of generalizations of ending with same last j letters here denoted ln j l in we realize that sooner or later there will be observations available in the worst case looking at the last zero letters i.e. at the unigram probabilities
we will next consider improving the probability estimates for unknown words i.e. words that do not occur in tile training corpus and for which we therefore have no lexical probabilities the same technique could actually be used for improving the estimates of the lexical probabilities of words that do occur in the training corpus
to estimate cr0 ck we assume that we have either a discrete uniform distribution on lcb NUM m rcb or a continuous uniform distribution on NUM m that is as hard to predict as the one in ck in the sense that the entropy is the same
a dynamic programming technique is then used to find tag the sequence NUM that maximizes
these probabilities can bc estimated either from a pretagged training corpus or from untagged text a lexicon and an initial bias
we will here consider the special case when the flmction g is a weighted sum of the relative frequency and the previous estimate appropriately
for this reason we will use the standard deviation in ck as a weight i.e. NUM r ck
we will next compare the proposed method to in turn deleted interpolation expected likelihood estimation and katz s back off scheme
the other main idea is concerned with improving the estimates of low frequency or nofrequency outcomes apparently without trying to generalize the conditionings
for example consider generalizing symmetric trigram statistics i.e. statistics of the form p t i tz tr
the reason that this is a research issue is that a word can in general be assigned different tags depending on context
we will start by estimating the probability distribution in the most general context c1 if necessary directly from the relative frequencies
plandoc uses a heuristic that always joins the first and second messages and continues to do so for third and more if the distinct attributes between the messages are the same
as a result the combining algorithm must have a way to determine when to break the messages into separate sentences that are easy to understand and unambiguous
applying deletion on identity blindly to the whole message list might make the generated text incomprehensible because readers might have to recover too much implicit information from the sentence
in this paper i have described a general algorithm which not only reduces the amount of the text produced but also increases the fluency of the text
the system combines the maximum number of re null lated messages to meet the second design criteriongenerating the most concise text
how much information to pack into a sentence does not depend on grammaticality but on coherence comprehensibility and aesthetics which are hard to formalize
continue from step NUM e2 s1 d1 and e2 NUM d1 are merged because they have only one distinct attribute site
if at least one attachment to one of these phrases is possible no further forwarding to containers which cover text positions preceding the current position will occur
since top can be either a noun or a verb in english the analyst could either mean that a gulfinkel tops a worrow or that a gulfinkel has a worrow as its top
the modex expects classes to be labeled with singular nouns and relations to be labeled with third person singular active verbs passive verbs with by or nouns
this text especially with its boundary value examples makes it very clear that the model allows a section to belong to no courses and also allows a section to belong to more than one course
currently we are pursuing several development directions
relations were particularly troublesome to both analysts and users
the text now contains a new section with negative examples which makes it clear that it is no longer possible for a section to belong either to zero courses or to multiple courses
several control buttons give access to additional texts
figure NUM the university o o diagram
at least one previous system has used such knowledge gema
this means the senmndc entry for schicficn as part of cinch bock schicflen already contains the predicate make z y
this is done with the help of extra equations over the special features val and vpl of the idiomatic featm e structures
otnt onents like bucket which do not carry a ny individual mea ning are called quasi argument s
the semantic of all the other parts is considered empty l he empty k i rs is bound l o the eorresi onding edges
for very word of a sentecne to be parsed it is checked if it is a base lexeme of an idiom
the feature structure of this idiom edge contains information al out how the idiom has to be completed and its underlying syntactic structure
verbal idioms can be divided into two main groups non compositional idioms as kick the bucket and compositional decomposable idioms as spill the beans
we claim that a more appropriate semantic representation of this idiom should respect its kind of composition and take its referents into consideration
we claim that individu fl components of decolnposable illiotas can lm onsidered figurative arguments and that these figurative t rgmnents lmve referents on their own
which is the hidden semantic stuff of beck or NUM rest e tively that is modified inquired quantified and emphasized
there were other pressures continuing reductions in the numbers of analysts available to do analysis reductions in budgets and constant pressures to justify government sponsored research in terms of demonstrable and practical benefits to the government
what we have to do to transfer technology successfully and what we have done in the tipster program is make a place where the two can meet user and technologist to work collaboratively in a joint effort to tame the text monster and improve the way textual information is handled in our operational environment
general business users doing market research perhaps or tracking competitors activities would likely be most interested in precision in the top of their return document list they are not tracking events at a level that requires a total and detailed picture of everything that has been said related to a particular topic over a long period of time
the interpretation of a lud representation is the interpretation of top the label or hole of a lui representation tbr which tt ere exists no label thai subordinates it
the seven tbrmal rules of stem changes described above make up the set of classes
much more serious is the possibility of some grammatical constructions not being understandable in telegraphic form even to a human reader
we describe our processing a simplification of the processing in compansion and note some challenges that still remain
on the other hand this population of users does bring with it other difficulties
the second algorithm is created jor the identifjdng the whole set of rules jor stem pairs
how does the system work in order to demonstrate the function of the pivot implementation of our system we decided to connect it to a commercially available text editor
c in case that the grammar checker identified and localized an error it creates a message box with a short description of the error s
in order to be able to use grammar exe we had to create a macro grammar assigned to the grammar checker item in the tools menu
this type of connection is of course much slower than the intended one but for the purpose of this demonstration the difference in speed is not so important
with the growing length of sentences the parsing will be more complex with respect both to the length of the processing and to the number of resulting syntactic structures
this macro selects a current sentence sends it to grammar exe via dde receives the result and indicates the type of the result to the user
the system is far from being ready for commercial exploitation the main obstacle is the size of the syntactic dictionary used
i the work was supported by the tollowing research grants ga r NUM NUM NUM rss h esp no NUM NUM and jep peco NUM language technologies for
the first step of simplifying the original input sentence represented almost NUM acceleration although it was only a cosmetic change from an abbreviation to a full word form
accompanied by an error marker may be relaxed in phases b and c hard constraints with an operator may never be relaxed
table NUM shows a result of this step
table NUM strings including for more
figure NUM summarizes the result of the evaluation
table NUM shows the result of this step
this method retrieve collocations in the following stages NUM extracting strings of characters as units of collocations NUM extracting recurrent combinations of strings in accordance with their word order in a corpus as collocations
on the contrary the words which follow local area network are hardly identified because a local area network is a unit of expression and innumerable words are possible to follow the string
verbs and nouns can be followed by a preposition and there can be additional material arguments adjuncts between a verb and its preposition
he invokes modex to generate a textual description in english of a particular aspect of the model namely of the section class figure NUM
thus modex can serve the purpose of enforcing such naming conventions since if they are not followed the text will be nonsensical or even unreadable
NUM restrictions on the object model modex is designed for use independent of the domain of the oo data model that is being described it lacks domain knowledge
this free text can capture information not deducible from the model such as high level descriptions of purpose and will be integrated with the automatically generated text
instead modex works by providing the analyst or domain expert with a different representation of the model namely in english
this result can be attributed to the fact that xn corpus contains less new words
in l description of the relation is section of general observations a section may belong to zero or more courses
we are also developing a facility to direct the output of modex to commercial off the shelf publishing environments for the production of standard paperbased documentation
those candidates on the top of the table have higher probability to be real words
futsu gin ga kiev ni chuuzai in jimusho
this would mean that successful semantic links will get a higher weight than other ones
inter1 retcr will tell us that the re is a qnalitier relation l ctwcen erikson and stocks and sin e the system stores the fact thai named entities qualifying things of type stock are of type company it can classil y the i roper name l ikson as a oral any
too hilt performs two a l ivities l ha nttribute to t roper name lassification no fllrth r rc ogniti m of pr NUM r ll nlcs goes on at this point only a rctlning of their classification
wh n an un lassified t rolmr noun is matched with a previously classilied proper llatn ill the text it is marke l as a tn p r name of the lass of th kllowt l rop r ltai lo
we have quantitatively evaluated the system against a blind test set of wall street journal business articles and report results not only for the system as a whole but for each component technique and for each class of name
the sysl cltt llses thes t et hnittucs in a fairly linfited and ext erimental way ill l resent and there ix much room f n their xtcnsi m
we have observed such cardinality mistakes in many oo models
in addition this design meets the requirements of a number of us government agencies
a number of commercial spin offs of tipster technology are happening
rapidity of insertion of the technology at this point is an important objective
the need for information extraction technology appears even less pressing outside the government
it requires the persistence and flexibility to tackle many obstacles
there are many facets to building and maintaining this trust
research alone was insufficient as a motivation for the program
we get a combination of constraints for a scope underspecified source clause NUM and parallelism constraints between source and target NUM
this possibility can be excluded within the given framework by using a stronger set of equations between second order terms as in NUM
NUM i xs o1 twoqlanguage la mx cs many linguist lamy c4 spoke by var var
the algorithm for context unification leads to a disjunction of two solved constraints given in NUM i and ii
on the other hand their approach treats a large number of problems of the interaction of anaphora and ellipsis especially strict sloppy ambiguities
our use of context unification does not allow us to adopt their strategy of capturing such ambiguities by admitting non linear solutions to parallelism constraints
it replace the to constraint xcs j by a variable binding xcs j and eliminates xc8 in the remaining constraint
in this way the preprocessor forms the background knowledge for the target language
another accuracy enhancement is to improve the tag matching algorithm
overall totals are given at the bottom of the table
then each independent section is searched separately for tag pairings
as shown in figure NUM the system contains two main modules a developer s tool this allows tech null nical authors to specify formally the procedures necessary for the user to achieve their goals thus supporting user oriented instructions
other members of the project include anthony hartley markus fischer lyn pemberton richard power and donia scott
as with any generation system drafter requires a semantic knowledge base from which text can be generated
in step NUM the system retrieves a large list of word candidates for a given string
then the learned character confusion probabilities are used for the next pass processing feedback processing
it is not feasible to train on texts to acquire character confusion probabilities for each ocr environment
ruel is an obscure french derivative word meaning the space between a bed and the wall
the words in the lexicon are indexed by letter n grams as described in the previous section
we used NUM of the collection for training and the remaining NUM for testing
the error reduction rate was calculated by subtracting total errors from NUM NUM and dividing by NUM NUM
these results indicate that an initial pass followed by two feedback passes may optimize the method
the probability pr w is given by the language model and can be decomposed as
the documents in the corpus are business articles in the domain of computer science and computer engineering
believe that it is not enough to cover the whole phenomenon of dialogues
error tolerant recognition can be applied to spelling correction for any language if a it has a word list comprising all inflected forms or b its morphology has been fully described by a finite state transducer
first though the construction of a grammar automaton is almost certainly infeasible for realistic grammars the construction of a grammar andinput automaton which is a very much smaller machine may not be
NUM anaphora are only one yet very prominent phenomenon that yields textual cohesion in discourse
i we will now illustrate the working of these constraints starting with the consideration of reflexives
consider e.g. a subordinate clause preceding its matrix as is always true for topicalizations
finally considering the case of text anaphora binding theory has nothing to offer at all
that hel will get a letter peter expects
that peter will get a letter hei expects
the second sentence of NUM contains the definite noun phrase der rechner
NUM er erwartet daft peter e hen brief bekommt
peter expects that he will get a letter
c4 NUM has been specially adapted to work directly on our preprocessor produced data structures for more efficient operation rather than through data files which is the normal mode of operation
during this dialogue the server maintains intermediate results such as learned tagging procedures texts that have been preprocessed for learning or evaluation and state information for the current task
it manages the training and testing files extracts features learns tagging procedures from tagged training texts and applies them to unseen test texts
when the users speak english only keyword spotting for the dialogue management is undertaken
this makes them easy to refer to and results in a very powerful description language
so the predicates introduced by some lexical entry percolate up to the topmost node automatically
as a supplement to semantic predicates our output contains various kinds of additional information
the system is currently being extended to cover nine additional dialogues from the corpus completely
we have discussed the implementation of a compositional semantics in the verbmobil speech to speech translation system
as sketched in section NUM our semantic construction component delivers output to the components for semantic evaluation and transfer
ostler and atkins NUM p NUM
NUM NUM eric is a veritable napoleon
the semantic import for a demonstrative noun phrase of singular grammatical number is that its denotation be one while that of plural grammatical number is that its denotation may be be one or greater
indeed this conversion is true of many mass nouns denoting mental states in general such as surprise wonder admiration pleasure worry aspiralion ambition and desire
indeed these two examples are instances of still another sub regularity namely common count nouns for products can be used to denote parts which contribute to the enlargement or enhancement of the product
in the case of a mass noun for concrete things or stuff its count noun version has as its denotation either units of what the mass noun denotes or kinds of what it denotes
NUM NUM george did a willie nelson
such lexicalizations require lexical entries of their own
moreover i accept the suggestion of the term that the correlation has a directionality to it though i am aware of the fact pointed out by leech NUM pp
on the one hand there is a large class of words which pattern morpho syntactically with mass nouns yet their denotations have parts which do not fall within the same noun s denotation
we are planning to explore several different paths that might increase the system s power to distinguish the linguistic contexts in which particular changes would be useful
compared with reiter s default logic our notion of nonmonotonic sorts corresponds to default theories
there will also be some discussion on the computational properties and limitations of the given approach
this reduces the amount of work required when computing a w explanation
this work has been supported by the swedish research council for engineering sciences tfr
we also gave formal definitions for the approach and provided a discussion on its computational properties
a a i u a2 can be defined as an alternative to the given definition
nonmon any posterior any no value fail
therefore i will consider the general form of default rules
it is however very useful when defining nonmonotonic constructions
the following notation will be used for describing an inheritance hierarchy
figure NUM procedural hierarchy for saving a file
it also allows them to control the drafting process
this mechanism allows authors to drag actions from the actions pane and drop them on the various procedural relation slots in the workspace pane or alternatively to create new actions to fill the slots
null NUM cliquer sur le bouton save
figure NUM generated english and french drafts
the text is produced in english and in french
NUM type a name in the filenamestring field
NUM click on the save button
clearly though the por can be used to rule out 5b if we assume that occurrences that are directly associated with a focus are primary occurrences
due to the small amounts of readily available data on the order of 50k words for the languages we have worked with standard language modeling tools are difficult to use as they presuppose the availability of corpora that are several orders of magnitude larger
in automatic tests using treebank derived data this technique achieved recall and precision rates of roughly NUM for basenp chunks and NUM for somewhat more complex chunks that partition the sentence
these NUM word and part of speech patterns were then combined with each of the NUM different chunk tag patterns shown on the right side of the table
we also note some related adaptations in the procedure for learning rules that improve its performance taking advantage of ways in which this task differs from the learning of part of speech tags
bourigault claims that the grammar can parse around NUM of the maximal length noun phrases in a test corpus into possible terminological phrases which then require manual validation
however it is possible to construct a limited index that lists for each candidate rule those locations in the corpus at which the static portions of its left hand side pattern match
we would also like to explore applying these same kinds of techniques to building larger scale structures in which larger units are assembled or predicate argument structures derived by combining chunks
applying transformational learning to text chunking requires that the system s current hypotheses about chunk structure be represented in a way that can be matched against the pattern parts of rules
these patterns eliminate impossible readings to identify a somewhat idiosyncratic kind of target noun group that does not include initial determiners but does include postmodifying prepositional phrases including determiners
encoding chunk structure with tags attached to words rather than non recursive bracket markers inserted between words has the advantage that it limits the dependence between different elements of the encoded representation
this value is used to rank the subcategorization cues produced by the previous components of the system
it consists of four components sf detection mapping disambiguation and ranldng
roles c f x NUM v is the set of relations with role names r lcb has part has cpu rcb and denotes the established relations in the knowledge base while r characterizes the labels of admitted conceptual relations
based on the definition of d binding we are able to specify several constraints on reflexive pronouns and anaphors in dg terms reflexive pronoun the reflexive pronoun and the antecedent to which the reflexive pronoun refers are d bound by the same head
NUM NUM the definition of d binding roughly corresponds to the governing categoryin gb terminology which relies upon the notion of c command while the latter two grammar constraints correspond to the three major binding principles of gb
if the intermediate noun has no possessive modifiers the subject of the entire clause is the antecedent of the reflexive since the reflexive is d bound by the finite verb irrespective of the occurrence of the object np cf
the main advantage of our approach lies in the unified framework for sentence and text level anaphora using a coherent grammar format and the provision for access to grammatical and conceptual knowledge without prioritizing either one of them
pronanaphortest pro ante c ante isac noun a pro features selt agr gen lj ante features self agr gen
we shall illustrate the linguistic aspects of word actor based parsing by introducing the basic data structures for text level anaphora as acquaintances of specific word actors and then turn to the general message passing protocol that accounts for intra as well as inter sentential anaphora
e permit box NUM pronanaphortest nomanaphortest defnp ante ante isac noun a defnp features sel agr num u ante features sel ag num
despite the intuitive appeal of their approach in which transcribed sentences are re ranked using the likelihood or degree of syntactic correctness they have thus far been unable to obtain noticeable reduction of word error rates partly due to as they point out a limited possible range of improvement one can only improve n best ranking if there is a better transeription among them
the trees assigned by the solutions represent expressions of some semantic representation language
there is a complete and correct semi decision procedure for solving context constraints
we assume an infinite set of hol variables ranged over by x and y
des saarlandes and the esprit working group ccl ii ep NUM
in the course of text analysm the parser extends thin dommn knowledge incrementally by new concept definltlons in order to dlstmgumh
we believe that our use of a separate constraint language is more transparent
this requires modifying the parallelism requirements in an appropriate way
this contribution was measured by comparing the entropy rate of lexical stress in natural sentences with randomly permuted versions of the same
the difference is small indeed but its consistency over hundreds of well trained data points puts the observation on statistically solid ground
ideally we should factor out semantics as well as word choice comparing each sentence in the corpus with its grammatical variations
to determine how much is contributed by the way they are glued together we need to remove the bias of word choice
most frequent among the NUM grams are the patterns wsws and swsw consistent with the principle of binary alternation mentioned in section NUM
the entropy rate bounds how compressible the training sequence is and not precisely how predictable unseen sequences from the same source would be
compression can take place off line after the entire training set is read while prediction can not cheat in this manner
as was noted above the users of this particular vocabulary access system generally use only the vocabulary items programmed into the communic ease map tm
class descriptions were formed using the NUM training exmples
counterexamples are positive examples of all remaining classes
procedure optimize generalises fnat class description to the terms of the sound classes
procedure update refi eshes the sets of examples
the best of two extended conjunctions
ideal here is a complex function involving production perception and many unknowns
it is crucial to detect and understand potential sources of bias in the methodology so far
comments from john lafferty georg niklfeld and frank dellaert contributed greatly to this paper
null word external stress regularity has been denied this level of attention
these include the breath groups of syllables that influence both vocalized and written production
we found the entropy rates to be consistently midway between the fully randomized and unrandomized values
null our first experiments used the binary NUM alphabet
figure NUM the amount of training data in syllables
lexical stress is a well studied subject at the intraword level
null the major processing in the system takes place in an augmented transition network type of grammar
what is interesting about the sampling ratio is that it allows recall to be traded off for precision directly
the purpose of this division is mainly of an organizational nature it allows to split the work into packages
all of the positive examples are used and negative examples are chosen randomly in accordance with this parameter
the first is the confidence with which the begin tag tree classifies the token as a good begin tag
we will attempt for the system to mimic the partner on the videotaped sessions
in this way we can most easily compare robotag performance against a variety of other name tagging systems
the best pairing set for a section maximizes the sum of the scores for each pair in the section
before starting re dictation the speaker underwent a few hours training session learning how to use the system and having his voice patterns incorporated into the language model the system we use is speaker adaptable
by using bnnru goi hyou as the handmade thesaurus and newspaper articles with about NUM NUM m sentences as a corpus we confirmed the appropriateness of this method
the average of coefftcient of variation for similarities used in each first cs l he coefficient of variation is the stamtard deviation divided by the mean
his table shows that the similarity obtained by our method is a little better than the similarity in bunrui goi hyou
we deeply appreciate the nihon keizai shinbun company to permit the use of this corpus and many people who negotiated with the company about the use of this corpus
tab NUM shows the average of similarities between two classes such that these two classes have the common parent node whose level is x in bunrni goi hyou
a higher radius gives the decision tree algorithm more contextual information in deciding whether a token makes a good begin or end tag
the last part is a distance score which is calculated from the tag length mean distance and match length preference
in dictation where the speaker normally has an option of backing up and re recording such things are less of an issue than the word for word accuracy of the final transcription since there are serious liability considerations to be reckoned with
the plan library consists of discourse plans which are used by the text planning component
it ensures a wide range of real language is covered and because of its size it should minimize the effect of any errors or idiosyncrasies on the part of editors parsers and transcribers
for example the np np vp rule pattern was removed since all the verb phrases occurring in this pattern were imperative ones which can legitimately act as sentences NUM
current work presents tools br creating a formal description of the estonian stem changing rules starting from the pair of the stem variants
the main problem consists in bringing together the fi rmal classification features available to the computer and classification based on human knowledge
we do this by making use of the notion of a common comparator set
the next step is to delete further groups of words from the input sentence
in case that neither phase provides any result no error message is issued
all partial parses from the first phase are used in the phases b and c
we have chosen the other possible way which prefers the subtrees with minimal number of errors
the rfodg provides a formal base for the description ofnonprojective and incorrect syntactic constructions
on the other hand after the relaxation of constraints there are NUM items created
word for word translation charles fern sing
in this case it is necessary to pass the results to the evaluation phase
if this phase succeeds the evaluation module issues a relevant error message or warning
typically a large value of k gives high recall and low precision while the opposite is the case with a small value of k
the results of experiments using paragraphs are shown in fig NUM the experiments used the first paragraph of a text as a segment
in the following we will investigate ways of reducing the size of s d without hurting the performance of topic identification
topic identification concerns the problem of identifying a topic of text with no information being given on the text s title or keywords whatsoever
it contains a synonym dip of the key word
figure NUM demonstrates the overall architecture of the wordkeys system
rules for the first order predicate cah ulus that is introducing an order over h gical interltretations
there is only one extra underlying mc hanism besides iufi rellc
a logical representation of the above sentences ix as folh ws and we denote it as ai p
items that are assigned focus based on their newness are assigned the o focus operator as shown in NUM
other relevant propositions concerning the object in question are then linearly organized according to beliefs about how well they contribute to the overall intention
dividing semantic representations into their thematic and rhematic parts allows propositions to be presented in a way that maximizes the shared material between utterances
the list may be limited to some size k so that only the k most recent discourse entities pushed onto the list are retrievable
NUM which should be interpreted as a single two paragraph monologue satisfying a goal to describe two different objects
for the two structures above the default rule can be applied for skickade since active is consistent with d but not for skickades since active and passive are inconsistent
i would also like to thank lars ahrenberg and patrick doherty for comments on this work and mark a young for providing me with much needed information about his and bill rounds work
this is done by defining a nonmonotonic rule default for the class value which is assumed to be the most general class in a defined hierarchy
the class any value is then further divided into a class called any no value which only contains this single value and the actual values of a structure
it is however possible to use some other kind of subsumption order if that is more suitable for the domain to be modelled by the formalism
given the unification operation of objects within the subsumption order and the definition of nonmonotonic sorts it is possible to define an operation for nonmonotonic unification
posterior explanation means that the explanation of the rule is postponed until reaching the result of some external process for example a parser or generator
thus each member in the inheritance hierarchy is called a class which is defined by giving it a name and a parent in the hierarchy
fi ove example of the disalnbiguation is tre ted in tlm hclp language
lemmatization word forms are analyzed to be able to look them up in the lexicon
if they are found the corresponding message numbers are added to the list of retrieved messages
word forms leading to several possible lemmas are currently not disambiguated
one idea is to use a semantic lexicon that is able to learn from the input
they can select a conversational item from the database by entering one or several key words
after the lemmatization procedure a derivational analysis is carried out on the lemmatized word forms
the relations used for query expansion are dependent on the semantic paths defined in the settings
currently user trials are being carried out to determine the suitability of the approach for aac
removing ambiguities concerning syntactic categories has a certain impact on the performance of the semantic expansion module
furthermore ordering restrictions on dependency analyses lor german can be formulated more transparently if discontinuous structures are allowed
but still the number of ambiguities remained prohibitively large often due to unnecessary partial structures with large discontinuities
hence the incompleteness of the algorithm trades theoretical purism for feasibility of realistic nlp
null within realistic nlp scenarios the parsing device will encounter ungrammatical and extragrammatical input
we consider constraints which introduce increasing restrictions on the parallel execution of the parsing task
a word class specifies morphosyntactic features valencies and allowed orderings for its instances
furthermore the parsetalk parser by design is able to cope with discontinuities stemming from unor extragrammatical input
figure NUM calls to conceptciieck ison with the extended version of the chart parser is about six to nine
in the first prototype we enforced confluency by an incremental structure building condition on the basis of a synchronization schema
we present an approach to parallel natural language parsing which is based on a concurrent object oriented model of computation
those words are pana mister gen
b the analysis is successful but all results contain at least one syntactic inconsistency
the last but not least problem was to incorporate the prototype into an existing text editor
with the growing degree of word order freedom the usability of simple pattern matching techniques decreases
for the purpose of the pivot implementation of the system we have chosen microsoft word NUM NUM
it tries to locate the source of the error using an algorithm that compares available trees
according to the settings given by the user the evaluation phase issues warnings or error messages
the last variant of the input sentence will serve as a contrast to the previous ones
the result of the processing is a unique structure and NUM items are derived in NUM NUM s
it can correct non word as well as real word errors
a statistical approach to automatic ocr error correction in context
for example hiki small animal is used to count small animals excluding rabbits which are counted with wa bird
however this approach would be computationally too expensive
table NUM results from context dependent non word error correction
table NUM ocr errors originating from literal words
the resulting word bigram table had about NUM NUM NUM entries
the dynamic programming recurrence is given as follows
NUM NUM generation of word candidates for a given string
statistical modeling of course presupposes that sufficiently large corpora are available for training
the techniques we have used are subject to certain systematic problems
only the first n candidates are retained for context based word error correction
keep only the top n candidates for the next processing step
if we have no information about the character confusion probabilities we can estimate them as
pr del y ly is the probability that letter y is deleted
the estimator a can be regarded as the probability that a given character is correctly recognized
where pr ins y is the probability that letter y is inserted
similar method could be found in zhou95 NUM
terminology phrases are word pairs composed of terminology words and other words
most of the words are uni grams hi grams tri grams and NUM grams
wang kai zhu presented a statistical method to extract possible words from texts
the final computer terminology dictionary contains NUM NUM words and NUM NUM compound words
with regard to chinese the identification procedure is even more difficult
naturally the authors alone are responsible for any errors or omissions in the current version
for mt backtranslations provide the user with an ability to judge whether they were interpreted correctly
other isa properties are realized by nouns or noun phrases
NUM NUM the x5 is a tube amplifier
such focal distinctions may affect the linguistic presentation of information
first definitional isa properties are realized by the matrix verb
sentences conveying the same propositional content in different contexts need not share the same information structure
several aspects of the output shown above are worth noting
information structure refers to the organization of information within an utterance
the realization of information structure in a sentence however differs from language to language
the program therefore interjects the o her property and produces another audio journal
the metrics used were recall r precision p and an averaging measure p r defined as
using id3 a dccision tree is generated based on the existing feature set and thc specified level of context to be considered
the obtained japanese scores as compared to the scores from the initial english experiment e e e are shown in figure NUM
the obtained spanish scores as compared to the scores from the initial english experiment e e e are shown in figure NUM
for example if person trces perform better independently than location trees then a person classification will be chosen over a location classification
safetek can be recognized as a company name by utilizing the preceding contextual phrase and appositive the new company
only a few days of human effort for each new language results in performance levels comparable to that of the best current english systems
the remainder is constant across languages a language independent core development system and an optimally derived feature set for english
the result is a hierarchical collection of co occurring fcatures which predict inclusion to or exclusion from a particuhtr proper name class
currently most of our attention is focused on the third category of comparisons those we have termed illustrative comparisons
the identify discourse plan is used to describe an entity and the compare and contrast discourse plan is used to compare two entities
illustrative comparisons are the focus of the current work and we describe our approach to these in section NUM NUM
a new discourse goal is generated by the user clicking on a hypertext link in the current document being viewed
how do we make clarificatory compar null isons which do not cause the user to make incorrect inferences
the leaves of the instantiated discourse plan are then realized via a simple template mechanism
this document may be displayed using any www document renderer such as mosaic or netscape
the echidna is found in australia whereas the african porcupine is found in africa
the echidna is active at dawn and dusk whereas the african porcupine is nocturnal
the application of automated language processing to concrete human tasks is itself an important research method in this field
there is considerable hope among some in government that many text handling needs can be met with commercial products
for phase iii the architecture and capabilities platform will provide an environment to work on these issues
it is as much a matter of business and psychology as it is of technology and engineering
what was discovered in the laboratory had also to be transferred as quickly as possible into the workplace
these small scale and limited applications have also simplified dealings with the user since there were fewer of them immediately involved
i have included in this paper many suggestions for dealing with the problems that arise in technology transfer
for this reason testing and evaluation of not only the research but also applications is crucial
it requires understanding the user s style what do they require from someone in order to trust them
so tipster gots is not free but cheaper than developing the capability repeatedly in different locations
robotag does not currently use the lexical features of the tokens during the match process
each robotag client invokes its own instance of the server to handle its learning tasks
this approach achieves its best performance using different hand coded rule sets for each language domain pair
where n is the total number of printable characters
tom take x to z s mother tom take sue to z s mother where both occurrences of the parallel element m have been abstracted over
the distinction is implemented by coloring all occurrences that are directly associated with parallel element ps whereas the corresponding free variable an is colored as ps
the standard solution for finding a complete set of solutions in this so called flex rigid situation is to substitute a term for x that will enable decomposition to be applicable afterwards
iof iof which does not unify with the right hand side of the original equation i e x ex of x i 0f i0f
just as in the case of unification for first order terms the algorithm is a process of recursive decomposition and variable elimination that transform sets of equations into solved forms
semantics the basic idea underlying the use of hou for nl semantics is very simple the typed a calculus is used as a semantic representation language while semantically under specified elements e.g.
in this paper we have argued that higher order coloured unification allows a precise specification of the interface between semantic interpretation and other sources of linguistic information thus preventing over generation
thus we are left with i pf t ia which can uniquely be solved by the color substitution pf a
if we collect all instantiations we arrive at exactly the two possible solutions for r pf in the original equations which we had claimed in section NUM
in addition the statistical data from the corpus are weighted
here we select and summarize relevant results on context unification from the latter
notice that developing a ystem for this particular population will overcome some of the difficulties faced with the general compansion system built as a writing tool for people with sophisticated linguistic ability
to support repair we model how misunderstandings can lead to unexpected actions and utterances and describe the processes of interpretation and repair
this context includes the tasks that the participants are involved in the prior beliefs that they had and the discourse itself
the aim of our research is to construct a model of communicative interaction that will be able to support the negotiation of meaning
that is both response links are arcs in the equivalence graph generated by the key
first let s be an equivalence set generated by the key and let r1
further let q be a finite set of nonempty subsets of f and let f0 i j x xe8 initialization step for each frame z in f0
the dis rnhiguation component uses the expectation matirni tion em algorithm to assign probabilities to each frame in an sf alternative given all sf sets obtained for a given corpus
because this an indication on in is that because this is an indication of the fact that copula nc n ac a
of the NUM sets produced by the sf mapping NUM contained more than one sf i.e. reflected some form of ambiguity in the original sentence of which NUM were unique
according to this rule this sentence is mapped to lcb pp an v arbeiten pp anl n student rcb
the overall precision rate for the system described in this paper is lower than that of similar systems developed for english since no test of significance was used to filter out possibly erroneous cues
further the system should be extended to handle other types of pronominal adverb cues such as pro forms for interrogative personal and relative pronouns possibly pps headed by prepositions should also be considered
in the experiment described the error bounds for the filtering procedure were chosen with the aim of get ing a highly accurate dictionary at the expense of recall
the system uses expected frequencies of head words and frames calculated using a hand written grammar and occurrences in a text corpuswto iteratively estimate probability parameters for a pcfg using the expectation maximi ation
the third element is a set of tree readings c d and encodes the tree readings in which the edge is used
let d2 be the context set assigned to e then only the argunmnt values of the contexl s in d2 are unified
generating representations for all the interpretations is nol feasible in view of the strict computa ional bounds imposed on nlp systems
NUM NUM NUM the operator tj differs from the set theoretic notion of disjoint union in that it is neither commutative nor associative
the experiment was conducted on a sparcstation NUM using input sentences of the form i saw a man with a telescope ff
let k be the edge vet which the vertex v was reached from another vertex w in the top down traversal
the simplest word based representations of content while relatively better understood are usually inadequate since single words are rarely specific enough for accurate discrimination and their grouping is often accidental
while our nlir system still performs extensive natural language processing in order to extract phrasal and other indexing terms our focus has shifted to the problems of building effective search queries
this was the result of spliting the documents of the stem stream into fragments of constant length NUM characters and indexing each fragment as if it were a different document
this stream was obtained by indexing the text of the documents as is without stemming or any other processing and running the unprocessed text of the queries against that index
at this time automatic text expansion produces less effective queries than manual expansion primarily due to a relatively unsophisticated mechanism used to identify and match concepts in the queries
each time a match was found the text around usually the paragraph containing it was read and if found fit imported into the query
based on our earlier results indicating that nlp is more effective with long descriptive queries we allowed for long passages from related documents to be liberally imported into the queries
measured as average precision on training data NUM the number of streams that retrieve a particular document and NUM the ranks of this document within each stream
this would suggest that relatively self contained text passages such as paragraphs provide a balanced representation of content that can not be easily approximated by selecting only some words
using linguistic terms such as phrases head modifier pairs names or even simple concepts does help to improve retrieval precision but the gains remained quite modest
the bracketings were analyzed so that each node with a punctuation mark as its immediate daughter is reported with its other daughters abbreviated to their categories as in NUM NUM
the development of a theory of punctuation can then progress with investigations into the semantic function of punctuation marks to ultimately form a theory that will be of great use to the nlp community
the next stage of this research is to test the results of both these approaches to see if they work and also to compare their results
there were NUM NUM unique category patterns extracted from the corpus for the five most common marks of point punctuation ranging from NUM NUM for the comma to NUM for the dash
the sentences for patterns with low incidence and those whose correctness was questionable were carefully examined to determine whether there was any justification for a particular rule pattern given the content of the sentence
punctuation could occur adjacent to any complex structure
the nf function is defined recursively by ss4 NUM s proof of theorem NUM semantic equivalence is also defined independently of the grammar
using the vector as the query the lexicon words that are similar to the word error are retrieved giving a large list of candidate correct forms
the second text from which fragments whose labels start with c are derived is a book on early NUM h history of turkish republic
these are nevertheless inflected using turkish word formation paradigms with inflectional features demanded by the syntactic context and sometimes even go through derivational processes
the set of features selected for each part of speech category is determined by a template and hence is controllable permitting experimentation with differing levels of information
null there are also numerous other examples of word forms where productive derivational processes come into play NUM geldigimdeki at the time i came
tables NUM and NUM present the results of further disambiguation of ark and c270 using rules learned from training texts c500 c1000 c2000 and ark
the rows labeled context statistics give the state after the rules are applied and context statistics are used as described earlier to remove additional parses
we maintain score thresholds associated with each context specificity group the threshold of a less specific group being higher than that of a more specific group
some morphological features are only meaningful or relevant for disambiguation only when they appear to the left or to the right of the token to be disambiguated
thus in the example above for geldi imdeki the following feature structure is generated finally each such feature structure is then projected on a subset of its features
conversation acts however are not assumed to be understood without some positive evidence by the receiver such as an acknowledgment
in example NUM even if mother knew who was going she could still be asking russ a question albeit insincerely
metaplans include actions such as introduce continue or clarify and are recognized in part by identifying cue phrases
but these intentions are inconsistent because knowref m whoisgoing and not knowref m whoisgoing are incompatible
fact active do s1 a ts d acceptance sl a ts
NUM our theory supports this flexibility by having each speaker evaluate the coherence of all utterances within her own view of the discourse
first it is closely related to the binomial distribution model of text
experiment in section NUM also showed that it could lead to better performance
this research presents a chi square method based approach to semi automatically generate terminology dictionaries
