waashk/dblp huggingface dataset:
automatic acquisition of the lexical semantics of verbs from sentence frames. this paper presents a computational model of verb acquisition which uses what we will call the principle of structured overcommitment to eliminate the need for negative evidence. the learner escapes from the need to be told that certain possibilities cannot occur (i.e., are "ungrammatical") by one simple expedient: it assumes that all properties it has observed are either obligatory or forbidden until it sees otherwise, at which point it decides that what it thought was either obligatory or forbidden is merely optional. this model is built upon a classification of verbs based upon a simple three-valued set of features which represents key aspects of a verb's syntactic structure, its predicate/argument structure, and the mapping between them.
twicpen: hand-held scanner and translation software for non-native readers. twicpen is a terminology-assistance system for readers of printed (ie. off-line) material in foreign languages. it consists of a hand-held scanner and sophisticated parsing and translation software to provide readers a limited number of translations selected on the basis of a linguistic analysis of the whole scanned text fragment (a phrase, part of the sentence, etc.). the use of a morphological and syntactic parser makes it possible (i) to disambiguate to a large extent the word selected by the user (and hence to drastically reduce the noise in the response), and (ii) to handle expressions (compounds, collocations, idioms), often a major source of difficulty for non-native readers. the system exists for the following language-pairs: english-french, french-english, german-french and italian-french.
translating idioms. this paper discusses the treatment of fixed word expressions developed for our its-2 frenc-english translation system. this treatment makes a clear distinction between compounds-i.e. multiword expressions of x0-level in which the chunks are adjacent-and idiomatic phrases-i.e. multiword expressions of phrasal categories, where the chunks are not necessarily adjacent. in our system, compounds are handled during the lexical analysis, while idioms are treated in the syntax, where they are treated as "specialized lexemes". once recognized, an idiom can be transfered according to the specifications of the bilingual dictionary. we will show several cases of transfer to corresponding idioms in the target language, or to simple lexemes. the complete system, including several hundreds of compounds and idioms can be consulted on the internet (http://latl.unige.ch/itsweb.html).
linear context-free rewriting systems and deterministic tree-walking transducers. we show that the class of string languages generated by linear context-free rewriting systems is equal to the class of output languages of deterministic tree-walking transducers. from equivalences that have previously been established we know that this class of languages is also equal to the string languages generated by context-free hypergraph grammars, multicomponent tree-adjoining grammars, and multiple context-free grammars and to the class of yields of images of the regular tree languages under finite-copying top-down tree transducers.
combinatory categorial grammars: generative power and relationship to linear context-free rewriting systems. recent results have established that there is a family of languages that is exactly the class of languages generated by three independently developed grammar formalisms: tree adjoining grammars, head grammars, and linear indexed grammars. in this paper we show that combinatory categorial grammars also generates the same class of languages. we discuss the structural descriptions produced by combinatory categorial grammars and compare them to those of grammar formalisms in the class of linear context-free rewriting systems. we also discuss certain extensions of combinatory categorial grammars and their effect on the weak generative capacity.
a hybrid approach to representation in the janus natural language processor. in bbn's natural language understanding and generation system (janus), we have used a hybrid approach to representation, employing an intensional logic for the representation of the semantics of utterances and a taxonomic language with formal semantics for specification of descriptive constants and axioms relating them. remarkably, 99.9% of 7,000 vocabulary items in our natural language applications could be adequately axiomatized in the taxonomic language.
empirical lower bounds on the complexity of translational equivalence. this paper describes a study of the patterns of translational equivalence exhibited by a variety of bitexts. the study found that the complexity of these patterns in every bitext was higher than suggested in the literature. these findings shed new light on why "syntactic" constraints have not helped to improve statistical translation models, including finite-state phrase-based models, tree-to-string models, and tree-to-tree models. the paper also presents evidence that inversion transduction grammars cannot generate some translational equivalence relations, even in relatively simple real bitexts in syntactically similar languages with rigid word order. instructions for replicating our experiments are at http://nip.cs.nyu.edu/genpar/acl06
you can't beat frequency (unless you use linguistic knowledge) - a qualitative evaluation of association measures for collocation and term extraction. in the past years, a number of lexical association measures have been studied to help extract new scientific terminology or general-language collocations. the implicit assumption of this research was that newly designed term measures involving more sophisticated statistical criteria would outperform simple counts of co-occurrence frequencies. we here explicitly test this assumption. by way of four qualitative criteria, we show that purely statistics-based measures reveal virtually no difference compared with frequency of occurrence counts, while linguistically more informed metrics do reveal such a marked difference.
the imperfective paradox and trajectory-of-motion events. in the first part of the paper, i present a new treatment of the imperfective paradox (dowty 1979) for the restricted case of trajectory-of-motion events. this treatment extends and refines those of moens and steedman (1988) and jackendoff (1991). in the second part, i describe an implemented algorithm based on this treatment which determines whether a specified sequence of such events is or is not possible under certain situationally supplied constraints and restrictive assumptions.
cues and control in expert-client dialogues. we conducted an empirical analysis into the relation between control and discourse structure. we applied control criteria to four dialogues and identified 3 levels of discourse structure. we investigated the mechanism for changing control between these structures and found that utterance type and not cue words predicted shifts of control. participants used certain types of signals when discourse goals were proceeding successfully but resorted to interruptions when they were not.
empirical study of predictive powers od simple attachment schemes for post-modifier prepositional phrases. this empirical study attempts to find answers to the question of how a natural language (henceforth nl) system could resolve attachment of prepositional phrases (henceforth pps) by examining naturally occurring pp attachments in typed dialogue. examination includes testing predictive powers of existing attachment theories against the data. the result of this effort will be an algorithm for interpreting pp attachment.
event-building through role-filling and anaphora resolution. in this study we map out a way to build event representations incrementally, using information which may be widely distributed across a discourse. an enhanced discourse representation (kamp, 1981) provides the vehicle both for carrying open event roles through the discourse until they can be instantiated by nps, and for resolving the reference of these otherwise problematic nps by binding them to the event roles.
orthogonal negation in vector spaces for modelling word-meanings and document retrieval. standard ir systems can process queries such as "web not internet", enabling users who are interested in arachnids to avoid documents about computing. the documents retrieved for such a query should be irrelevant to the negated query term. most systems implement this by reprocessing results after retrieval to remove documents containing the unwanted string of letters.this paper describes and evaluates a theoretically motivated method for removing unwanted meanings directly from the original query in vector models, with the same vector negation operator as used in quantum logic. irrelevance in vector spaces is modelled using orthogonality, so query vectors are made orthogonal to the negated term or terms.as well as removing unwanted terms, this form of vector negation reduces the occurrence of synonyms and neighbours of the negated terms by as much as 76% compared with standard boolean methods. by altering the query vector itself, vector negation removes not only unwanted strings but unwanted meanings.
development and use of a gold-standard data set for subjectivity classifications. this paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using statistical techniques. bias-corrected tags are formulated and successfully used to guide a revision of the coding manual and develop an automatic classifier.
word sense and subjectivity. subjectivity and meaning are both important properties of language. this paper explores their interaction, and brings empirical evidence in support of the hypotheses that (1) subjectivity is a property that can be associated with word senses, and (2) word sense disambiguation can directly benefit from subjectivity annotations.
a computational theory of perspective and reference in narrative. narrative passages told from a character's perspective convey the character's thoughts and perceptions. we present a discourse process that recognizes characters' thoughts and perceptions in third-person narrative. an effect of perspective on reference in narrative is addressed: references in passages told from the perspective of a character reflect the character's beliefs. an algorithm that uses the results of our discourse process to understand references with respect to an appropriate set of beliefs is presented.
head-driven generation with hpsg. as hpsg is head-driven, with clear semantic heads, semantic head-driven generation should be simple. we adapt van noord's prolog generator for use with an hpsg grammar in profit. however, quantifiers and context factors are difficult to include in head-driven generation. we must adopt recent theoretical proposals for lexicalized scoping and context. with these revisions, head-driven generation with hpsg is not so simple, but it is possible.
phran - a knowledge-base natural language understander. we have developed an approach to natural language processing in which the natural language processor is viewed as a knowledge-based system whose knowledge is about the meanings of the utterances of its language. the approach is oriented around the phrase rather than the word as the basic unit. we believe that this paradigm for language processing not only extends the capabilities of other natural language systems, but handles those tasks that previous systems could perform in a more systematic and extensible manner.we have constructed a natural language analysis program called phran (phrasal analyzer) based in this approach. this model has a number of advantages over existing systems, including the ability to understand a wider variety of language utterances, increased processing speed in some cases, a clear separation of control structure from data structure, a knowledge base that could be shared by a language production mechanism, greater ease of extensibility, and the ability to store some useful forms of knowledge that cannot readily be added to other systems.
word sense disambiguation using optimised combinations of knowledge sources. word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. we describe a system which performs word sense disambiguation on all content words in free text by combining different knowledge sources: semantic preferences, dictionary definitions and subject/domain codes along with part-of-speech tags, optimised by means of a learning algorithm. we also describe the creation of a new sense tagged corpus by combining existing resources. tested accuracy of our approach on this corpus exceeds 92%, demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample.
two accounts of scope availability and semantic underspecification. we propose a formal system for representing the available readings of sentences displaying quantifier scope ambiguity, in which <i>partial</i> scopes may be expressed. we show that using a theory of scope availability based upon the function-argument structure of a sentence allows a deterministic, polynomial time test for the availability of a reading, while solving the same problem within theories based on the well-formedness of sentences in the meaning language has been shown to be np-hard.
compositional semantics for linguistic formalisms. in what sense is a grammar the union of its rules? this paper adapts the notion of <i>composition</i>, well developed in the context of programming languages, to the domain of linguistic formalisms. we study alternative definitions for the semantics of such formalisms, suggesting a denotational semantics that we show to be compositional and fully-abstract. this facilitates a clear, mathematically sound way for defining grammar modularity.
using bracketed parses to evaluate a grammar checking application. we describe a method for evaluating a grammar checking application with hand-bracketed parses. a randomly-selected set of sentences was submitted to a grammar checker in both bracketed and unbracketed formats. a comparison of the resulting error reports illuminates the relationship between the underlying performance of the parser-grammar system and the error critiques presented to the user.
paragraph-, word-, and coherence-based approaches to sentence ranking: a comparison of algorithm and human performance. sentence ranking is a crucial part of generating text summaries. we compared human sentence rankings obtained in a psycholinguistic experiment to three different approaches to sentence ranking: a simple paragraph-based approach intended as a baseline, two word-based approaches, and two coherence-based approaches. in the paragraph-based approach, sentences in the beginning of paragraphs received higher importance ratings than other sentences. the word-based approaches determined sentence rankings based on relative word frequencies (luhn (1958); salton & buckley (1988)). coherence-based approaches determined sentence rankings based on some property of the coherence structure of a text (marcu (2000); page et al. (1998)). our results suggest poor performance for the simple paragraph-based approach, whereas word-based approaches perform remarkably well. the best performance was achieved by a coherence-based approach where coherence structures are represented in a non-tree structure. most approaches also outperformed the commercially available msword summarizer.
compositional semantics of german prefix verbs. a compositional account of the semantics of german prefix verbs in hpsg is outlined. we consider only those verbs that are formed by productive synchronic rules. rules are fully productive if they apply to all base verbs which satisfy a common description. prefixes can be polysemous and have separate, highly underspecified lexical entries. adequate bases are determined via selection restrictions.
a model for robust processing of spontaneous speech by integrating viable fragments. we describe the design and function of a robust processing component which is being developed for the verbmobil speech translation system. its task consists of collecting partial analyses of an input utterance produced by three parsers and attempting to combine them into more meaningful, larger units. it is used as a fallback mechanism in cases where no complete analysis spanning the whole input can be achieved, owing to spontaneous speech phenomena or speech recognition errors.
computational analysis of move structures in academic abstracts. this paper introduces a method for computational analysis of move structures in abstracts of research articles. in our approach, sentences in a given abstract are analyzed and labeled with a specific move in light of various rhetorical functions. the method involves automatically gathering a large number of abstracts from the web and building a language model of abstract moves. we also present a prototype concordancer, care, which exploits the move-tagged abstracts for digital learning. this system provides a promising approach to web-based computer-assisted academic writing.
a grammatical approach to understanding textual tables using two-dimensional scfgs. we present an elegant and extensible model that is capable of providing semantic interpretations for an unusually wide range of textual tables in documents. unlike the few existing table analysis models, which largely rely on relatively ad hoc heuristics, our linguistically-oriented approach is systematic and grammar based, which allows our model (1) to be concise and yet (2) recognize a wider range of data models than others, and (3) disambiguate to a significantly finer extent the underlying semantic interpretation of the table in terms of data models drawn from relation database theory. to accomplish this, the model introduces viterbi parsing under two-dimensional stochastic cfgs. the cleaner grammatical approach facilitates not only greater coverage, but also grammar extension and maintenance, as well as a more direct and declarative link to semantic interpretation, for which we also introduce a new, cleaner data model. in disambiguation experiments on recognizing relevant data models of unseen web tables from different domains, a blind evaluation of the model showed 60% precision and 80% recall.
learning source-target surface patterns for web-based terminology translation. this paper introduces a method for learning to find translation of a given source term on the web. in the approach, the source term is used as query and part of patterns to retrieve and extract translations in web pages. the method involves using a bilingual term list to learn source-target surface patterns. at runtime, the given term is submitted to a search engine then the candidate translations are extracted from the returned summaries and subsequently ranked based on the surface patterns, occurrence counts, and transliteration knowledge. we present a prototype called termmine that applies the method to translate terms. evaluation on a set of encyclopedia terms shows that the method significantly outperforms the state-of-the-art online machine translation systems.
a kernel pca method for superior word sense disambiguation. we introduce a new method for disambiguating word senses that exploits a nonlinear kernel principal component analysis (kpca) technique to achieve accuracy superior to the best published individual models. we present empirical results demonstrating significantly better accuracy compared to the state-of-the-art achieved by either na&iuml;ve bayes or maximum entropy models, on senseval-2 data. we also contrast against another type of kernel method, the support vector machine (svm) model, and show that our kpca-based model outperforms the svm-based model. it is hoped that these highly encouraging first results on kpca for natural language processing tasks will inspire further development of these directions.
using machine learning techniques to interpret wh-questions. we describe a set of supervised machine learning experiments centering on the construction of statistical models of wh-questions. these models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals. we report on different aspects of the predictive performance of our models, including the influence of various training and testing factors on predictive performance, and examine the relationships among the target variables.
boosting statistical word alignment using labeled and unlabeled data. this paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. the proposed approach modifies the supervised boosting algorithm to a semi-supervised learning algorithm by incorporating the unlabeled data. in this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. then we build a pseudo reference set for the unlabeled data, and calculate the error rate of each word aligner using only the labeled data. based on this semi-supervised boosting algorithm, we investigate two boosting methods for word alignment. in addition, we improve the word alignment results by combining the results of the two semi-supervised boosting methods. experimental results on word alignment indicate that semi-supervised boosting achieves relative error reductions of 28.29% and 19.52% as compared with supervised boosting and unsupervised boosting, respectively.
machine translation between turkic languages. we present an approach to mt between turkic languages and present results from an implementation of a mt system from turkmen to turkish. our approach relies on ambiguous lexical and morphological transfer augmented with target side rule-based repairs and rescoring with statistical language models.
a phonetic-based approach to chinese chat text normalization. chatting is a popular communication media on the internet via icq, chat rooms, etc. chat language is different from natural language due to its anomalous and dynamic natures, which renders conventional nlp tools inapplicable. the dynamic problem is enormously troublesome because it makes static chat language corpus outdated quickly in representing contemporary chat language. to address the dynamic problem, we propose the phonetic mapping models to present mappings between chat terms and standard words via phonetic transcription, i.e. chinese pinyin in our case. different from character mappings, the phonetic mappings can be constructed from available standard chinese corpus. to perform the task of dynamic chat language term normalization, we extend the source channel model by incorporating the phonetic mapping models. experimental results show that this method is effective and stable in normalizing dynamic chat language terms.
centrality measures in text mining: prediction of noun phrases that appear in abstracts. in this paper, we study different centrality measures being used in predicting noun phrases appearing in the abstracts of scientific articles. our experimental results show that centrality measures improve the accuracy of the prediction in terms of both precision and recall. we also found that the method of constructing noun phrase network significantly influences the accuracy when using the centrality heuristics itself, but is negligible when it is used together with other text features in decision trees.
maximum entropy based phrase reordering model for statistical machine translation. we propose a novel reordering model for phrase-based statistical machine translation (smt) that uses a maximum entropy (maxent) model to predicate reorderings of neighbor blocks (phrase pairs). the model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. we present an algorithm to extract all reordering events of neighbor blocks from bilingual data. in our experiments on chinese-to-english translation, this maxent-based reordering model obtains significant improvements in bleu score on the nist mt-05 and iwslt-04 tasks.