Online large-margin training of dependency parsers

ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volumn , Issue , 2005, Pages 91-98

  McDonald, Ryan ^a   Crammer, Koby ^a   Pereira, Fernando ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

E-LEARNING; TREES (MATHEMATICS);

DEPENDENCY PARSER; DEPENDENCY TREES; LARGE MARGIN TRAININGS; TRAINING ALGORITHMS;

COMPUTATIONAL LINGUISTICS;

EID: 84859925125     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1219840.1219852     Document Type: Conference Paper

References (30)

1. D.M. Bikel. 2004. Intricacies of Collins parsing model. Computational Linguistics.
2. Y. Censor and S.A. Zenios. 1997. Parallel optimization : theory, algorithms, and applications. Oxford University Press.
3. E. Charniak. 2000. A maximum-entropy-inspired parser. In Proc. NAACL.
4. S. Clark and J.R. Curran. 2004. Parsing the WSJ using CCG and log-linear models. In Proc. ACL.
5. M. Collins and B. Roark. 2004. Incremental parsing with the perceptron algorithm. In Proc. ACL.
6. M. Collins, J. Hajič, L. Ramshaw, and C. Tillmann. 1999. A statistical parser for Czech. In Proc. ACL.
7. M. Collins. 1999. Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania.
8. M. Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proc. EMNLP.
9. K. Crammer and Y. Singer. 2001. On the algorithmic implementation of multiclass kernel based vector machines. JMLR.
10. K. Crammer and Y. Singer. 2003. Ultraconservative online algorithms for multiclass problems. JMLR.
11. K. Crammer, O. Dekel, S. Shalev-Shwartz, and Y. Singer. 2003. Online passive aggressive algorithms. In Proc. NIPS.
12. A. Culotta and J. Sorensen. 2004. Dependency tree kernels for relation extraction. In Proc. ACL.
13. Y. Ding and M. Palmer. 2005. Machine translation using probabilistic synchronous dependency insertion grammars. In Proc. ACL.
14. J. Eisner and G. Satta. 1999. Efficient parsing for bilexical context-free grammars and head-automaton grammars. In Proc. ACL.
15. J. Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In Proc. COLING.
16. J. Hajič. 1998. Building a syntactically annotated corpus: The Prague dependency treebank. Issues of Valency and Meaning.
17. L. Huang and D. Chiang. 2005. Better k-best parsing. Technical Report MS-CIS-05-08, University of Pennsylvania.
18. Richard Hudson. 1984. Word Grammar. Blackwell.
19. T. Joachims. 2002. Learning to Classify Text using Support Vector Machines. Kluwer.
20. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML.
21. M. Marcus, B. Santorini, and M. Marcinkiewicz. 1993. Building a large annotated corpus of english: the penn treebank. Computational Linguistics.
22. J. Nivre and M. Scholz. 2004. Deterministic dependency parsing of english text. In Proc. COLING.
23. A. Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In Proc. EMNLP.
24. A. Ratnaparkhi. 1999. Learning to parse natural language with maximum entropy models. Machine Learning.
25. S. Riezler, T. King, R. Kaplan, R. Crouch, J. Maxwell, andM. Johnson. 2002. Parsing theWall Street Journal using a lexical-functional grammar and discriminative estimation techniques. In Proc. ACL.
26. F. Sha and F. Pereira. 2003. Shallow parsing with conditional random fields. In Proc. HLT-NAACL.
27. Y. Shinyama, S. Sekine, K. Sudo, and R. Grishman. 2002. Automatic paraphrase acquisition from news articles. In Proc. HLT.
28. B. Taskar, C. Guestrin, and D. Koller. 2003. Max-margin Markov networks. In Proc. NIPS.
29. B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning. 2004. Max-margin parsing. In Proc. EMNLP.
30. H. Yamada and Y. Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proc. IWPT.