1997 ThreeGenerativeLexModelsForStatParsing

(Collins, 1997) ⇒ Michael Collins. (1997). “Three Generative, Lexicalised Models for Statistical Parsing.” In: Proceedings of ACL 1997. doi:10.3115/979617.979620

Subject Headings:

Notes

Cited By

Quotes

Abstract

In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both sub-categorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (Collins 96).

References

1. Hiyan Alshawi, Head automata and bilingual tiling: translation with minimal representations, Proceedings of the 34th annual meeting on Association for Computational Linguistics, p.167-176, June 24-27, 1996, Santa Cruz, California doi:10.3115/981863.981886
2. E. Black, Steven P. Abney, S. Flickenger, C. Gdaniec, C. Grishman, P. Harrison, D. Hindle, R. Ingria, F. Jelinek, J. Klavans, M. Liberman, M. Marcus, S. Roukos, B. Santorini, T. Strzalkowski, Procedure for quantitatively comparing the syntactic coverage of English grammars, Proceedings of the workshop on Speech and Natural Language, p.306-311, February 19-22, 1991, Pacific Grove, California doi:10.3115/112405.112467
3. T. L. Booth and R. A. Thompson. 1973. Applying Probability Measures to Abstract Languages. IEEE Transactions on Computers, C-22(5), pages 442--450.
4. Eugene Charniak, Parsing with Context-Free Grammars and Word Statistics, Brown University, Providence, RI, 1995
5. Noam Chomsky. 1957. Syntactic Structures, Mouton, The Hague.
6. Michael Collins, A new statistical parser based on bigram lexical dependencies, Proceedings of the 34th annual meeting on Association for Computational Linguistics, p.184-191, June 24-27, 1996, Santa Cruz, California doi:10.3115/981863.981888
7. Jason M. Eisner, Three new probabilistic models for dependency parsing: an exploration, Proceedings of the 16th conference on Computational linguistics, August 05-09, 1996, Copenhagen, Denmark doi:10.3115/992628.992688
8. G. Gazdar, E. H. Klein, G. K. Pullum, I. A. Sag. (1985). Generalized Phrase Structure Grammar. Harvard University Press.
9. F. Jelinek, John D. Lafferty, D. Magerman, R. Mercer, Adwait Ratnaparkhi, S. Roukos, Decision tree parsing using a hidden derivation model, Proceedings of the workshop on Human Language Technology, March 08-11, 1994, Plainsboro, NJ doi:10.3115/1075812.1075873
10. David M. Magerman, Statistical decision-tree models for parsing, Proceedings of the 33rd annual meeting on Association for Computational Linguistics, p.276-283, June 26-30, 1995, Cambridge, Massachusetts doi:10.3115/981658.981695
11. Mitchell P. Marcus, Mary Ann Marcinkiewicz, Beatrice Santorini, Building a large annotated corpus of English: the penn treebank, Computational Linguistics, v.19 n.2, June 1993
12. Mitchell Marcus, Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Mark Ferguson, Karen Katz, Britta Schasberger, The Penn Treebank: annotating predicate argument structure, Proceedings of the workshop on Human Language Technology, March 08-11, 1994, Plainsboro, NJ doi:10.3115/1075812.1075835
13. Scott Miller, David Stallard, Robert Bobrow, Richard Schwartz, A fully statistical approach to natural language interfaces, Proceedings of the 34th annual meeting on Association for Computational Linguistics, p.55-61, June 24-27, 1996, Santa Cruz, California doi:10.3115/981863.981871
14. Adwait Ratnaparkhi. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. Conference on Empirical Methods in Natural Language Processing.
15. A. Stolcke. (1996). Linguistic Dependency Modeling. Proceedings of ICSLP 96, Fourth International Conference on Spoken Language Processing.

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1997 ThreeGenerativeLexModelsForStatParsing	Michael Collins			Three Generative, Lexicalised Models for Statistical Parsing			http://portal.acm.org/citation.cfm?id=979620	10.3115/976909.979620