waashk/dblp huggingface dataset:
deep dependencies from context-free statistical parsers: correcting the surface dependency approximation. we present a linguistically-motivated algorithm for reconstructing nonlocal dependency in broad-coverage context-free parse trees derived from treebanks. we use an algorithm based on loglinear classifiers to augment and reshape context-free trees so as to reintroduce underlying nonlocal dependencies lost in the context-free approximation. we find that our algorithm compares favorably with prior work on english using an existing evaluation metric, and also introduce and argue for a new dependency-based evaluation metric. by this new evaluation metric our algorithm achieves 60% error reduction on gold-standard input trees and 5% error reduction on state-of-the-art machine-parsed input trees, when compared with the best previous work. we also present the first results on non-local dependency reconstruction for a language other than english, comparing performance on english and german. our new evaluation metric quantitatively corroborates the intuition that in a language with freer word order, the surface dependencies in context-free parse trees are a poorer approximation to underlying dependency structure.
flexible guidance generation using user model in spoken dialogue systems. we address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive. specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness. moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. we obtained reasonable classification accuracy for all dimensions. dialogue strategies based on the user modeling are implemented in kyoto city bus information system that has been developed at our laboratory. experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users.
neural network recognition of spelling errors. one area in which artificial neural networks (anns) may strengthen nlp systems is in the identification of words under noisy conditions. in order to achieve this benefit when spelling errors or spelling variants are present, variable-length strings of symbols must be converted to ann input/output form---fixed-length arrays of numbers. a common view in the neural network community has been that different forms of input/output representations have negligible effect on ann performance. this paper, however, shows that input/output representations can in fact affect the performance of anns in the case of natural language words. minimum properties for an adequate word representation are proposed, as well as new methods of word representation.to test the hypothesis that word representations significantly affect ann performance, traditional and new word representations are evaluated for their ability to recognize words in the presence of four types of typographical noise: substitutions, insertions, deletions and reversals of letters. the results indicate that word representations have a significant effect on ann performance. additionally, different types of word representation are shown to perform better on different types of error.
parsing as natural deduction. the logic behind parsers for categorial grammars can be formalized in several different ways. lambek calculus (lc) constitutes an example for a natural deduction1 style parsing method.in natural language processing, the task of a parser usually consists in finding derivations for all different readings of a sentence. the original lambek calculus, when it is used as a parser/theorem prover, has the undesirable property of allowing for the derivation of more than one proof for a reading of a sentence, in the general case.in order to overcome this inconvenience and to turn lambek calculus into a reasonable parsing method, we show the existence of "relative" normal form proof trees and make use of their properties to constrain the proof procedure in the desired way.
a freely available morphological analyzer, disambiguator and context sensitive lemmatizer for german. in this paper we present morphy, an integrated tool for german morphology, part-of-speech tagging and context-sensitive lemmatization. its large lexicon of more than 320, 000 word forms plus its ability to process german compound nouns guarantee a wide morphological coverage. syntactic ambiguities can be resolved with a standard statistical part-of-speech tagger. by using the output of the tagger, the lemmatizer can determine the correct root even for ambiguous word forms. the complete package is freely available and can be downloaded from the world wide web.
parsing and subcategorization data. in this paper, we compare the performance of a state-of-the-art statistical parser (bikel, 2004) in parsing written and spoken language and in generating subcategorization cues from written and spoken language. although bikel's parser achieves a higher accuracy for parsing written language, it achieves a higher accuracy when extracting subcategorization cues from spoken language. additionally, we explore the utility of punctuation in helping parsing and extraction of subcategorization cues. our experiments show that punctuation is of little help in parsing spoken language and extracting subcategorization cues from spoken language. this indicates that there is no need to add punctuation in transcribing spoken corpora simply in order to help parsers.
principle-based parsing without overgeneration. overgeneration is the main source of computational complexity in previous principle-based parsers. this paper presents a message passing algorithm for principle-based parsing that avoids the overgeneration problem. this algorithm has been implemented in c++ and successfully tested with example sentences from (van riemsdijk and williams, 1986).
automatic classification of verbs in biomedical texts. lexical classes, when tailored to the application and domain in question, can provide an effective means to deal with a number of natural language processing (nlp) tasks. while manual construction of such classes is difficult, recent research shows that it is possible to automatically induce verb classes from cross-domain corpora with promising accuracy. we report a novel experiment where similar technology is applied to the important, challenging domain of biomedicine. we show that the resulting classification, acquired from a corpus of biomedical journal articles, is highly accurate and strongly domain-specific. it can be used to aid bio-nlp directly or as useful material for investigating the syntax and semantics of verbs in biomedical texts.
word clustering and disambiguation based on co-occurence data. we address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. we view this problem as that of estimating a joint probability distribution specifying the joint probabilities of word pairs, such as noun verb pairs. we propose an efficient algorithm based on the minimum description length (mdl) principle for estimating such a probability distribution. our method is a natural extension of those proposed in (brown et al., 1992) and (li and abe, 1996), and overcomes their drawbacks while retaining their advantages. we then combined this clustering method with the disambiguation method of (li and abe, 1995) to derive a disambiguation method that makes use of both automatically constructed thesauruses and a hand-made thesaurus. the overall disambiguation accuracy achieved by our method is 85.2%, which compares favorably against the accuracy (82.4%) obtained by the state-of-the-art disambiguation method of (brill and resnik, 1994).
knowledge-based automatic topic identification. as the first step in an automated text summarization algorithm, this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. to represent and generalize concepts, we use the hierarchical concept taxonomy wordnet. by setting appropriate cutoff values for such parameters as concept generality and child-to-parent frequency ratio, we control the amount and level of generality of concepts extracted from the text.
clustering polysemic subcategorization frame distributions semantically. previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. we describe a new approach which involves clustering subcategorization frame (scf) distributions using the information bottleneck and nearest neighbour methods. in contrast to previous work, we particularly focus on clustering polysemic verbs. a novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a good insight into the potential and limitations of semantically classifying undisambiguated scf data.
parsing and subcategorization data. in this paper, we compare the performance of a state-of-the-art statistical parser (bikel, 2004) in parsing written and spoken language and in generating subcategorization cues from written and spoken language. although bikel's parser achieves a higher accuracy for parsing written language, it achieves a higher accuracy when extracting subcategorization cues from spoken language. our experiments also show that current technology for extracting subcategorization frames initially designed for written texts works equally well for spoken language. additionally, we explore the utility of punctuation in helping parsing and extraction of subcategorization cues. our experiments show that punctuation is of little help in parsing spoken language and extracting subcategorization cues from spoken language. this indicates that there is no need to add punctuation in transcribing spoken corpora simply in order to help parsers.
using syntactic dependency as local context to resolve word sense ambiguity. most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. separate classifiers have to be trained for different words. we present an algorithm that uses the same knowledge sources to disambiguate different words. the algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts.
improving subcategorization acquisition using word sense disambiguation. we investigate the change in performance of automatic subcategorization acquisition when a word sense disambiguation (wsd) system is employed to guide the acquisition process. as a subgoal, this involves creating a probabilistic wsd system, which we evaluate on the senseval-2 english all-words task data. we carry out an evaluation of the enriched subcategorization acquisition system using 29 'difficult' english verbs which shows that wsd helps to improve the acquisition performance.
a test environment for natural language understanding systems. the natural language understanding engine test environment (ete) is a gui software tool that aids in the development and maintenance of large, modular, natural language understanding (nlu) systems. natural language understanding systems are composed of modules (such as part-of-speech taggers, parsers and semantic analyzers) which are difficult to test individually because of the complexity of their output data structures. not only are the output data structures of the internal modules complex, but also many thousands of test items (messages or sentences) are required to provide a reasonable sample of the linguistic structures of a single human language, even if the language is restricted to a particular domain. the ete assists in the management and analysis of the thousands of complex data structures created during natural language processing of a large corpus using relational database technology in a network environment.
on the intonation of mono- and di-syllabic words within the discourse framework of conversational games. recent studies on the analysis of intonational function examine a range of materials from cue phrases in monologue (litman and hirschberg, 1990) and dialogue (hirschberg and litman, 1987; hockey, 1991) to longer utterances in both monologue and dialogue (mclemore, 1991). results match specific intonational tunes to certain discourse functions which are more or less well defined. although these results make a convincing case that intonation does signal a change in discourse structure, the specification of discourse function remains vague. a suitable taxonomy is needed to fine-tune the relationship between intonation and discourse function. a recent analysis of dialogue (kowtko et al., 1991) provides a framework of conversational games which allows more fine-grained examination of prosodic function. the current paper introduces an intonational analysis of mono-and di-syllabic words based upon such a framework and compares results in progress with previous work on intonation.
word translation disambiguation using bilingual bootstrapping. this paper proposes a new method for word translation disambiguation using a machine learning technique called 'bilingual bootstrapping'. bilingual bootstrapping makes use of in learning, a small number of classified data and a large number of unclassified data in the source and the target languages in translation. it constructs classifiers in the two languages in parallel and repeatedly boosts the performances of the classifiers by further classifying data in each of the two languages and by exchanging between the two languages information regarding the classified data. experimental results indicate that word translation disambiguation based on bilingual bootstrapping consistently and significantly outperforms the existing methods based on 'monolingual bootstrapping'.
automatic retrieval and clustering of similar words. bootstrapping semantics from text is one of the greatest challenges in natural language learning. we first define a word similarity measure based on the distributional pattern of words. the similarity measure allows us to construct a thesaurus using a parsed corpus. we then present a new evaluation methodology for the automatically constructed thesaurus. the evaluation results show that the thesaurus is significantly closer to wordnet than roget thesaurus is.
large scale collocation data and their application to japanese word processor technology. word processors or computers used in japan employ japanese input method through keyboard stroke combined with kana (phonetic) character to kanji (ideographic, chinese) character conversion technology. the key factor of kana-to-kanji conversion technology is how to raise the accuracy of the conversion through the homophone processing, since we have so many homophonic kanjis. in this paper, we report the results of our kana-to-kanji conversion experiments which embody the homophone processing based on large scale collocation data. it is shown that approximately 135,000 collocations yield 9.1 % raise of the conversion accuracy compared with the prototype system which has no collocation data.
identifying syntactic role of antecedent in korean relative clause using corpus and thesaurus information. this paper describes an approach to identifying the syntactic role of an antecedent in a korean relative clause, which is essential to structural disambiguation and semantic analysis. in a learning phase, linguistic knowledge such as conceptual co-occurrence patterns and syntactic role distribution of antecedents is extracted from a large-scale corpus. then, in an application phase, the extracted knowledge is applied in determining the correct syntactic role of an antecedent in relative clauses. unlike previous research based on co-occurrence patterns at the lexical level, we represent co-occurrence patterns with concept types in a thesaurus. in an experiment, the proposed method showed a high accuracy rate of 90.4% in resolving ambiguities of syntactic role determination of antecedents.
text segmentation based on similarity between words. this paper proposes a new indicator of text structure, called the lexical cohesion profile (lcp), which locates segment boundaries in a text. a text segment is a coherent scene; the words in a segment are linked together via lexical cohesion relations. lcp records mutual similarity of words in a sequence of text. the similarity of words, which represents their cohesiveness, is computed using a semantic network. comparison with the text segments marked by a number of subjects shows that lcp closely correlates with the human judgments. lcp may provide valuable information for resolving anaphora and ellipsis.
a phonotactic language model for spoken language identification. we have established a phonotactic language model as the solution to spoken language identification (lid). in this framework, we define a single set of acoustic tokens to represent the acoustic activities in the world's spoken languages. a voice tokenizer converts a spoken document into a text-like document of acoustic tokens. thus a spoken document can be represented by a count vector of acoustic tokens and token n-grams in the vector space. we apply latent semantic analysis to the vectors, in the same way that it is applied in information retrieval, in order to capture salient phonotactics present in spoken documents. the vector space modeling of spoken utterances constitutes a paradigm shift in lid technology and has proven to be very successful. it presents a 12.4% error rate reduction over one of the best reported results on the 1996 nist language recognition evaluation database.
automatic identification of non-compositional phrases. non-compositional expressions present a special challenge to nlp applications. we present a method for automatic identification of non-compositional expressions using their statistical properties in a text corpus. our method is based on the hypothesis that when a phrase is non-composition, its mutual information differs significantly from the mutual informations of phrases obtained by substituting one of the word in the phrase with a similar word.
feature-based allomorphy. morphotactics and allomorphy are usually modeled in different components, leading to interface problems. to describe both uniformly, we define finite automata (fa) for allomorphy in the same feature description language used for morphotactics. nonphonologically conditioned allomorphy is problematic in fa models but submits readily to treatment in a uniform formalism.
concept unification of terms in different languages for ir. due to the historical and cultural reasons, english phases, especially the proper nouns and new words, frequently appear in web pages written primarily in asian languages such as chinese and korean. although these english terms and their equivalences in the asian languages refer to the same concept, they are erroneously treated as independent index units in traditional information retrieval (ir). this paper describes the degree to which the problem arises in ir and suggests a novel technique to solve it. our method firstly extracts an english phrase from asian language web pages, and then unifies the extracted phrase and its equivalence(s) in the language as one index unit. experimental results show that the high precision of our conceptual unification approach greatly improves the ir performance.
quantifying lexical influence: giving direction to context. the relevance of context in disambiguating natural language input has been widely acknowledged in the literature. however, most attempts at formalising the intuitive notion of context tend to treat the word and its context symmetrically. 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. we also propose metrics for measuring directed lexical influence and compare performances.
a comparison and semi-quantitative analysis of words and character-bigrams as features in chinese text categorization. words and character-bigrams are both used as features in chinese text processing tasks, but no systematic comparison or analysis of their values as features for chinese text categorization has been reported heretofore. we carry out here a full performance comparison between them by experiments on various document collections (including a manually word-segmented corpus as a golden standard), and a semi-quantitative analysis to elucidate the characteristics of their behavior; and try to provide some preliminary clue for feature term choice (in most cases, character-bigrams are better than words) and dimensionality setting in text categorization systems.
an effective two-stage model for exploiting non-local dependencies in named entity recognition. this paper shows that a simple two-stage approach to handle non-local dependencies in named entity recognition (ner) can outperform existing approaches that handle non-local dependencies, while being much more computationally efficient. ner systems typically use sequence models for tractable inference, but this makes them unable to capture the long distance structure present in text. we use a conditional random field (crf) based ner system using local features to make predictions and then train another crf which uses both local information and features extracted from the output of the first crf. using features capturing non-local dependencies from the same document, our approach yields a 12.6% relative error reduction on the f1 score, over state-of-the-art ner systems using local-information alone, when compared to the 9.3% relative error reduction offered by the best systems that exploit non-local information. our approach also makes it easy to incorporate non-local information from other documents in the test corpus, and this gives us a 13.3% error reduction over ner systems using local-information alone. additionally, our running time for inference is just the inference time of two sequential crfs, which is much less than that directly model the dependencies and do approximate inference.
applying machine learning to chinese temporal relation resolution. temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. this information is often inferred from a variety of interactive grammatical and lexical cues, especially in chinese. for this purpose, inter-clause relations (temporal or otherwise) in a multiple-clause sentence play an important role. in this paper, a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a chinese multiple-clause sentence. the model makes use of the fact that events are represented in different temporal structures. it takes into account the effects of linguistic features such as tense/aspect, temporal connectives, and discourse structures. a set of experiments has been conducted to investigate how linguistic features could affect temporal relation resolution.
conversationally relevant descriptions. conversationally relevant descriptions are definite descriptions that are not merely tools for the identification of a referent, but are also crucial to the discourse in other respects. i analyse the uses of such descriptions in assertions as conveying a particular type of conversational implicatures. such implicatures can be represented within the framework of possible world semantics. the analysis is extended to non-assertive illocutionary acts on the one hand, and to indefinite descriptions on the other.