RECENT ADVANCES IN PARSING TECHNOLOGY

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.

Contents and Contributors:

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.
21. Index

Discipline codes:

• COMPUTER AND INFORMATION SCIENCE: Natural Language
• HUMANITIES: Computational Linguistics
• MATHEMATICS: Algorithms