Constraint Logic Programming for Natural Language Processing

This paper proposes an evaluation of the adequacy of the constraint logic programming paradigm for natural language processing. Theoretical aspects of this question have been discussed in several works. We adopt here a pragmatic point of view and our argumentation relies on concrete solutions. Using actual contraints (in the CLP sense) is neither easy nor direct. However, CLP can improve parsing techniques in several aspects such as concision, control, efficiency or direct representation of linguistic formalism. This discussion is illustrated by several examples and the presentation of an HPSG parser.Comment: 15 pages, uuencoded and compressed postscript to appear in Proceedings of the 5th Int. Workshop on Natural Language Understanding and Logic Programming. Lisbon, Portugal. 199

Criteria for Designing Computer Facilities for Linguistic Analysis

Abstract: In the natural-language-processing research community, the usefulness of computer tools for testing linguistic analyses is often taken for granted. Linguists, on the other hand, have generally been unaware of or ambivalent about such devices. We discuss several aspects of computer use that are preeminent in establishing the utility for linguistic research of computer tools and describe several factors that must be considered in designing such computer tools to aid in testing linguistic analyses of grammatical phenomena. A series of design alternatives, some theoretically and some practically motivated, is then based on the resultant criteria. We present one way of pinning down these choices which culminates in a description of a particular grammar formalism for use in computer linguistic tools. The PATR-II formalism this serves to exemplify our general perspective.Engineering and Applied Science

Towards a unified framework for sub-lexical and supra-lexical linguistic modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 171-178).Conversational interfaces have received much attention as a promising natural communication channel between humans and computers. A typical conversational interface consists of three major systems: speech understanding, dialog management and spoken language generation. In such a conversational interface, speech recognition as the front-end of speech understanding remains to be one of the fundamental challenges for establishing robust and effective human/computer communications. On the one hand, the speech recognition component in a conversational interface lives in a rich system environment. Diverse sources of knowledge are available and can potentially be beneficial to its robustness and accuracy. For example, the natural language understanding component can provide linguistic knowledge in syntax and semantics that helps constrain the recognition search space. On the other hand, the speech recognition component also faces the challenge of spontaneous speech, and it is important to address the casualness of speech using the knowledge sources available. For example, sub-lexical linguistic information would be very useful in providing linguistic support for previously unseen words, and dynamic reliability modeling may help improve recognition robustness for poorly articulated speech. In this thesis, we mainly focused on the integration of knowledge sources within the speech understanding system of a conversational interface. More specifically, we studied the formalization and integration of hierarchical linguistic knowledge at both the sub-lexical level and the supra-lexical level, and proposed a unified framework for integrating hierarchical linguistic knowledge in speech recognition using layered finite-state transducers (FSTs).(cont.) Within the proposed framework, we developed context-dependent hierarchical linguistic models at both sub-lexical and supra-lexical levels. FSTs were designed and constructed to encode both structure and probability constraints provided by the hierarchical linguistic models. We also studied empirically the feasibility and effectiveness of integrating hierarchical linguistic knowledge into speech recognition using the proposed framework. We found that, at the sub-lexical level, hierarchical linguistic modeling is effective in providing generic sub-word structure and probability constraints. Since such constraints are not restricted to a fixed system vocabulary, they can help the recognizer correctly identify previously unseen words. Together with the unknown word support from natural language understanding, a conversational interface would be able to deal with unknown words better, and can possibly incorporate them into the active recognition vocabulary on-the-fly. At the supra-lexical level, experimental results showed that the shallow parsing model built within the proposed layered FST framework with top-level n-gram probabilities and phrase-level context-dependent probabilities was able to reduce recognition errors, compared to a class n-gram model of the same order. However, we also found that its application can be limited by the complexity of the composed FSTs. This suggests that, with a much more complex grammar at the supra-lexical level, a proper tradeoff between tight knowledge integration and system complexity becomes more important ...by Xiaolong Mou.Ph.D

Neural Combinatory Constituency Parsing

東京都立大学Tokyo Metropolitan University博士（情報科学）doctoral thesi

Corpus-consulting probabilistic approach to parsing: the CCPX parser and its complementary components

Corpus linguistics is now a major field in the study of language. In recent years corpora that are syntactically analysed have become available to researchers, and these clearly have great potential for use in the field of parsing natural language. This thesis describes a project that exploits this possibility. It makes four distinct contributions to these two fields. The first is an updated version of a corpus that is (a) analysed in terms of the rich syntax of Systemic Functional Grammar (SFG), and (b) annotated using the extensible Mark-up Language (XML). The second contribution is a native XML corpus database, and the third is a sophisticated corpus query tool for accessing it. The fourth contribution is a new type of parser that is both corpus-consulting and probabilistic. It draws its knowledge of syntactic probabilities from the corpus database, and it stores its working data within the database, so that it is strongly database-oriented. SFG has been widely used in natural language generation for approaching two decades, but it has been used far less frequently in parsing (the first stage in natural language understanding). Previous SFG corpus-based parsers have utilised traditional parsing algorithms, but they have experienced problems of efficiency and coverage, due to (a) the richness of the syntax and (b) the challenge of parsing unrestricted spoken and written texts. The present research overcomes these problems by introducing a new type of parsing algorithm that is 'semi-deterministic' (as human readers are), and utilises its knowledge of the rules—including probabilities—of English syntax. A language, however, is constantly evolving. New words and uses are added, while others become less frequent and drop out altogether. The new parsing system seeks to replicate this. As new sentences are parsed they are added to the corpus, and this slowly changes the frequencies of the words and the syntactic patterns. The corpus is in this sense dynamic, and so simulates a human's changing knowledge of words and syntax