Simple training of dependency parsers via structured boosting

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 1756-1762

  Wang, Qin Iris ^a   Lin, Dekang ^b   Schuurmans, Dale ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL RANDOM FIELD; DEPENDENCY PARSING; LEARNING DEPENDENCIES; LOGISTIC REGRESSIONS; SPECIALIZED TRAINING; STRUCTURED PREDICTION; SUPERVISED TRAININGS; TRAINING ALGORITHMS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LOGISTICS; REGRESSION ANALYSIS;

FORECASTING;

EID: 84880907398     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (37)

1. Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden Markov support vector machines. In Proc. ICML, 2003.
2. Shane Bergsma and Dekang Lin. Bootstrapping path-based pronoun resolution. In Proc. COLING/ACL-06, 2006.
3. Dan Bikel. Intricacies of collins' parsing model. Computational Linguistics, 30(4), 2004.
4. E. Charniak. A maximum entropy inspired parser. In Proc. NAACL, pages 132-139, 2000.
5. C. Cherry and D. Lin. A probability model to improve word alignment. In Proc. ACL, pages 88-95, 2003.
6. Michael Collins, Robert E. Schapire, and Yoram Singer. Logistic regression, adaboost and bregman distances. In Machine Learning 48, 2002.
7. M. J. Collins. Three generative, lexicalized models for statistical parsing. In Proc. ACL, pages 16-23, 1997.
8. S. Corston-Oliver,Aue A., Duh K., and Ringger E. Multilingual dependency parsing using bayes' point machines. In Proceedings HLT/NAACL, 2006.
9. K. Crammer and Y. Singer. On the algorithmic interpretation of multiclass kernel-based vector machines. JMLR, 2, 2001.
10. A. Culotta and J. Sorensen. Dependency tree kernels for relation extraction. In Pro. ACL, 2004.
11. Yuan Ding and Martha Palmer. Machine translation using probabilistic synchronous dependency insertion grammars. In Proc. ACL, 2005.
12. J. Eisner. Three new probabilistic models for dependency parsing: An exploration. In Proc. of COLING, 1996.
13. Heidi J. Fox. Phrasal cohesion and statistical machine translation. In Proceedings of EMNLP-02, pages 304-311, 2002.
14. Y. Freund and R. Schapire. Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, 1996.
15. Y. Freund and R. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Computer and System Sciences, 55(1), 1997.
16. Joshua Goodman. Exponential priors for maximum entropy models. In Proc. NAACL, 2004.
17. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2001.
18. D. Jurafsky and J. Martin. Speech and Language Processing. Prentice Hall, 2000.
19. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of ICML, 2001.
20. David Magerman. Statistical decision-tree model for parsing. In Proc. ACL, pages 276-283, 1995.
21. M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313-330, 1993.
22. Andrew McCallum, Dayne Freitag, and Fernando Pereira. Maximum entropymarkov methods for information extraction and segmentation. In Proc. of ICML, 2000.
23. R. McDonald and F. Pereira. Online learning of approximate dependency parsing algorithmsx. In Proceedings of EACL, 2006.
24. Ryan McDonald, Koby Crammer, and Fernando Pereira. Online large-margin training of dependency parsers. In Proceedings of ACL, Ann Arbor, Michigan, 2005.
25. J. Nivre and M. Scholz. Deterministic dependency parsing of english text. In Proc. COLING, 2004.
26. M. Palmer et al. Chinese Treebank 4.0. Linguistic Data Consortium, 2004.
27. M. Palmer et al. Chinese Treebank 5.0. Linguistic Data Consortium, 2005.
28. Adwait Ratnaparkhi. A maximum entropy model for part-of-speech tagging. In Proceedings of EMNLP-96, 1996.
29. Adwait Ratnaparkhi. Learning to parse natural language with maximum entropy models. Machine Learning, 34(1-3):151-175, 1999.
30. Robert E. Schapire and Yoram Singer. Improved boosting algorithms using confidence-rated predictions. In Machine Learning 37, 1999.
31. B. Taskar, C. Guestrin, and D. Koller. Max-margin Markov networks. In Proceedings NIPS 16, 2003.
32. B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning. Max-margin parsing. In Proc. EMNLP, 2004.
33. K. Toutanova, D. Klein, C. D. Manning, and Y. Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of HLT-NAACL, 2003.
34. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In Proc. ICML, 2004.
35. Q. Wang, D. Schuurmans, and D. Lin. Strictly lexical dependency parsing. In Proc. IWPT, pages 152-159, 2005.
36. Q. Wang, C. Cherry, D. Lizotte, and D. Schuurmans. Improved large margin dependency parsing via local constraints and laplacian regularization. In Proc. of CoNLL, pages 21-28, 2006.
37. H. Yamada and Y. Matsumoto. Statistical dependency analysis with support vector machines. In Proc. of IWPT, 2003.