Bibliographic Information

Recent advances in parsing technology

edited by Harry Bunt and Masaru Tomita

(Text, speech, and language technology, v. 1)

Kluwer Academic Publishers, c1996

  • : hbk
  • : pbk

Available at  / 35 libraries

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Note

Includes bibliographical references and index

Description and Table of Contents

Volume

: hbk ISBN 9780792341529

Description

Parsing technologies are concerned with the automatic decomposition of complex structures into their constituent parts, with structures in formal or natural languages as their main, but certainly not their only, domain of application. The focus of Recent Advances in Parsing Technology is on parsing technologies for linguistic structures, but it also contains chapters concerned with parsing two or more dimensional languages. New and improved parsing technologies are important not only for achieving better performance in terms of efficiency, robustness, coverage, etc., but also because the developments in areas related to natural language processing give rise to new requirements on parsing technologies. Ongoing research in the areas of formal and computational linguistics and artificial intelligence lead to new formalisms for the representation of linguistic knowledge, and these formalisms and their application in such areas as machine translation and language-based interfaces call for new, effective approaches to parsing. Moreover, advances in speech technology and multimedia applications cause an increasing demand for parsing technologies where language, speech, and other modalities are fully integrated. Recent Advances in Parsing Technology presents an overview of recent developments in this area with an emphasis on new approaches for parsing modern, constraint-based formalisms on stochastic approaches to parsing, and on aspects of integrating syntactic parsing in further processing.

Table of Contents

  • 1. Parsing Technologies, and Why We Need Them
  • H. Bunt. 2. Fully Incremental Parsing
  • M. Wiren, R. Ronnquist. 3. Increasing the Applicability of LR Parsing
  • M.-J. Nederhof, J. Sarbo. 4. Towards a Formal Understanding of the Determinism Hypothesis in D-Theory
  • J. Rogers, K. Vijay-Shanker. 5. Varieties of Heuristics in Sentence Parsing
  • M. Nagao. 6. Parsing as Dynamic Interpretation of Feature Structures
  • H. Bunt, K. van der Sloot. 7. Proof Theory for HPSG Parsing
  • S. Raaijmakers. 8. Efficient Parsing of Compiled Typed Attribute-Value Logic Grammars
  • B. Carpenter, G. Penn. 9. Predictive Head-Corner Chart Parsing
  • K. Sikkel, R. op den Akker. 10. GLR* - An Efficient Noise-Skipping Parsing Algorithm for Context-Free Grammars
  • A. Lavie, M. Tomita. 11. Evaluation of the Tagged Text Parser, A Preliminary Report
  • T. Strzalkowski, P. Scheyen. 12. Learning to Parse with Transformations
  • E. Brill. 13. Estimation of Verb Subcategorization Frame Frequencies Based on Syntactic and Multidimensional Statistical Analysis
  • A. Ushioda, et al. 14. Monte Carlo Parsing
  • R. Bod. 15. Stochastic Lexicalized Tree-Insertion Grammar
  • Y. Schabes, R. Waters. 16. The Interplay of Syntactic and Semantic Node Labels in Parsing
  • D. McDonald. 17. Integration of Morphological and Syntactic Analysis based on GLR Parsing
  • H. Tanaka, et al. 18. Structural Disambiguation in Japanese by Case Structure Evaluation Based on Examples in a Case Frame Dictionary
  • S. Kurohashi, M. Nagao. 19. Flowgraph Parsing
  • R. Lutz. 20. Predictive Parsing for Unordered Relational Languages
  • K. Wittenburg. Index.
Volume

: pbk ISBN 9781402003714

Description

In Marcus (1980), deterministic parsers were introduced. These are parsers which satisfy the conditions of Marcus's determinism hypothesis, i.e., they are strongly deterministic in the sense that they do not simulate non determinism in any way. In later work (Marcus et al. 1983) these parsers were modified to construct descriptions of trees rather than the trees them selves. The resulting D-theory parsers, by working with these descriptions, are capable of capturing a certain amount of ambiguity in the structures they build. In this context, it is not clear what it means for a parser to meet the conditions of the determinism hypothesis. The object of this work is to clarify this and other issues pertaining to D-theory parsers and to provide a framework within which these issues can be examined formally. Thus we have a very narrow scope. We make no ar guments about the linguistic issues D-theory parsers are meant to address, their relation to other parsing formalisms or the notion of determinism in general. Rather we focus on issues internal to D-theory parsers themselves.

Table of Contents

  • 1. Parsing Technologies, and Why We Need Them
  • H. Bunt. 2. Fully Incremental Parsing
  • M. Wiren, R. Roennquist. 3. Increasing the Applicability of LR Parsing
  • M.-J. Nederhof, J. Sarbo. 4. Towards a Formal Understanding of the Determinism Hypothesis in D-Theory
  • J. Rogers, K. Vijay-Shanker. 5. Varieties of Heuristics in Sentence Parsing
  • M. Nagao. 6. Parsing as Dynamic Interpretation of Feature Structures
  • H. Bunt, K. van der Sloot. 7. Proof Theory for HPSG Parsing
  • S. Raaijmakers. 8. Efficient Parsing of Compiled Typed Attribute-Value Logic Grammars
  • B. Carpenter, G. Penn. 9. Predictive Head-Corner Chart Parsing
  • K. Sikkel, R. op den Akker. 10. GLR* - An Efficient Noise-Skipping Parsing Algorithm for Context-Free Grammars
  • A. Lavie, M. Tomita. 11. Evaluation of the Tagged Text Parser, A Preliminary Report
  • T. Strzalkowski, P. Scheyen. 12. Learning to Parse with Transformations
  • E. Brill. 13. Estimation of Verb Subcategorization Frame Frequencies Based on Syntactic and Multidimensional Statistical Analysis
  • A. Ushioda, et al. 14. Monte Carlo Parsing
  • R. Bod. 15. Stochastic Lexicalized Tree-Insertion Grammar
  • Y. Schabes, R. Waters. 16. The Interplay of Syntactic and Semantic Node Labels in Parsing
  • D. McDonald. 17. Integration of Morphological and Syntactic Analysis based on GLR Parsing
  • H. Tanaka, et al. 18. Structural Disambiguation in Japanese by Case Structure Evaluation Based on Examples in a Case Frame Dictionary
  • S. Kurohashi, M. Nagao.19. Flowgraph Parsing
  • R. Lutz. 20. Predictive Parsing for Unordered Relational Languages
  • K. Wittenburg. Index.

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Details

  • NCID
    BA28259250
  • ISBN
    • 079234152X
    • 1402003714
  • LCCN
    96025047
  • Country Code
    ne
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    Dordrecht
  • Pages/Volumes
    xv, 415 p.
  • Size
    25 cm
  • Classification
  • Subject Headings
  • Parent Bibliography ID
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