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Computer Science - Artificial Intelligence | Recent Advances in Parsing Technology

Recent Advances in Parsing Technology

Bunt, H., Tomita, Masaru (Eds.)

Softcover reprint of the original 1st ed. 1996


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  • About this book

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.

Content Level » Research

Related subjects » Artificial Intelligence - Computational Science & Engineering - Linguistics

Table of contents 

1. Parsing Technologies, and Why We Need Them; H. Bunt. 2. Fully Incremental Parsing; M. Wirén, R. Rönnquist. 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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