An ensemble model that combines syntactic and semantic clustering for discriminative dependency parsing

ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

Volumn 2, Issue , 2011, Pages 710-714

  Haffari, Gholamreza ^a   Razavi, Marzieh ^b   Sarkar, Anoop ^b  

^a MONASH UNIVERSITY   (Australia)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER-BASED; DEPENDENCY PARSING; ENSEMBLE METHODS; ENSEMBLE MODELS; MC-DONALD; SEMANTIC CLUSTERING; WORD REPRESENTATIONS;

COMPUTATIONAL LINGUISTICS; SEMANTICS;

SYNTACTICS;

EID: 84859039245     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. G. Attardi and F. Dell'Orletta. 2009. Reverse revision and linear tree combination for dependency parsing. In Proc. of NAACL-HLT.
2. P. F. Brown, P. V. deSouza, R. L. Mercer, T. J. Watson, V. J. Della Pietra, and J. C. Lai. 1992. Class-based n-gram models of natural language. Computational Linguistics, 18(4).
3. X. Carreras. 2007. Experiments with a higher-order projective dependency parser. In Proc. of EMNLP-CoNLL Shared Task.
4. E. Charniak, D. Blaheta, N. Ge, K. Hall, and M. Johnson. 2000. BLLIP 1987-89 WSJ Corpus Release 1, LDC No. LDC2000T43, Linguistic Data Consortium.
5. M. Collins. 2002. Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. In Proc. of EMNLP.
6. K. Crammer and Y. Singer. 2003. Ultraconservative online algorithms for multiclass problems. J. Mach. Learn. Res., 3:951-991.
7. T. Dietterich. 2002. Ensemble learning. In The Handbook of Brain Theory and Neural Networks, Second Edition.
8. J. Eisner. 1996. Three new probabilistic models for dependency parsing: an exploration. In COLING.
9. J. Hall, J. Nilsson, J. Nivre, G. Eryigit, B. Megyesi, M. Nilsson, and M. Saers. 2007. Single malt or blended? a study in multilingual parser optimization. In Proc. of CoNLL Shared Task.
10. T. Koo and M. Collins. 2010. Efficient third-order dependency parsers. In Proc. of ACL.
11. T. Koo, X. Carreras, and M. Collins. 2008. Simple semisupervised dependency parsing. In Proc. of ACL/HLT.
12. T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag. 2010. Dual decomposition for parsing with nonprojective head automata. In Proc. of EMNLP.
13. R. McDonald and J. Nivre. 2007. Characterizing the errors of data-driven dependency parsing models. In Proc. of EMNLP-CONLL.
14. R. McDonald and F. Pereira. 2006. Online learning of approximate dependency parsing algorithms. In Proc. of EACL.
15. R. McDonald, K. Crammer, and F. Pereira. 2005. Online large-margin training of dependency parsers. In Proc. of ACL.
16. I. Mel'čuk. 1987. Dependency syntax: theory and practice. State University of New York Press.
17. J. Nivre and R. McDonald. 2008. Integrating graphbased and transition-based dependency parsers. In Proc. of ACL.
18. S. Petrov, L. Barrett, R. Thibaux, and D. Klein. 2006. Learning accurate, compact, and interpretable tree annotation. In Proc. COLING-ACL.
19. S. Petrov. 2010. Products of random latent variable grammars. In Proc. of NAACL-HLT.
20. A. Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In Proc. of EMNLP.
21. K. Sagae and A. Lavie. 2006. Parser combination by reparsing. In Proc. of NAACL-HLT.
22. D. A. Smith and J. Eisner. 2008. Dependency parsing by belief propagation. In Proc. of EMNLP.
23. M. Surdeanu and C. Manning. 2010. Ensemble models for dependency parsing: Cheap and good? In Proc. of NAACL.
24. D. West. 2001. Introduction to Graph Theory. Prentice Hall, 2nd editoin.
25. H. Yamada and Y. Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proc. of IWPT.