K-best combination of syntactic parsers

EMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009

Volumn , Issue , 2009, Pages 1552-1560

  Zhang, Hui ^a,b   Zhang, Min ^a   Tan, Chew Lim ^b   Li, Haizhou ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; FORESTRY; NATURAL LANGUAGE PROCESSING SYSTEMS; SIMULATED ANNEALING;

LINEAR MODELING; PERCEPTRON ALGORITHMS; RE-RANKING TECHNIQUES; SIMULATED ANNEALING ALGORITHMS; STATE OF THE ART; SYNTACTIC PARSERS; SYNTACTIC PARSING; SYSTEM COMBINATION;

SYNTACTICS;

EID: 78649295943     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1699648.1699702     Document Type: Conference Paper

References (25)

1. Dan Bikel. 2004. On the Parameter Space of Generative Lexicalized Statistical Parsing Models. Ph.D. Thesis, University of Pennsylvania 2004.
2. Rens Bod. 2003. An efficient implementation of a new DOP model. EACL-04.
3. David Burkett and Dan Klein. 2008. Two Languages are Better than One (for Syntactic Parsing). EMNLP-08.
4. V Černý 1985. Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41-51.1985.
5. Eugene Charniak. 1997. Statistical parsing with a context-free grammar and word statistics. AAAI-97, pages 598-603.
6. Eugene Charniak. 2000. A maximum-entropy-inspired parser. NAACL-2000.
7. Eugene Charniak and Mark Johnson. 2005. Coarseto-fine-grained n-best parsing and discriminative reranking. ACL-05, Pages 173-180.
8. Michael Collins. 1997. Three generative, lexicalised models for statistical parsing. ACL-97, pages 16-23.
9. Michael Collins.1999. Head-drivenstatistical models for natural language parsing. Doctoral Dissertation, Dept. of Computer and Information Science, University of Pennsylvania, Philadelphia 1999.
10. Michael Collins. 2000. Discriminative reranking for natural language parsing. ICML-00, pages 175-182.
11. Michael Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. EMNLP-02.
12. Liang Huang. 2008. Forest Reranking: Discriminative Parsing with Non-Local Features. ACL-HLT-08, pages 586-594.
13. Liang Huang and David Chiang. 2005. Better k-best Parsing. IWPT-05.
14. S. Kirkpatrick, C. D. Gelatt, Jr. and M. P. Vecchi. 1983. Optimization by Simulated Annealing. Science. New Series 220 (4598): 671-680.
15. Dan Klein and Christopher D. Manning. 2001. Parsing and Hypergraphs. IWPT-01.
16. Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. ACL-03, pages 423-430.
17. John Henderson and Eric Brill. 1999. Exploiting diversity in natural language processing: combining parsers. EMNLP-99.
18. Mitchell P. Marcus, Beatrice Santorini and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19:313-330.
19. Takuya Matsuzaki. Yusuke Miyao and Jun'ichi Tsujii. 2005. Probabilistic CFG with latent annotations. ACL-05, pages 75-82.
20. David McClosky, Eugene Charniak and Mark Johnson. 2006. Effective self-training for parsing. NAACL-06, pages 152-159.
21. Slav Petrov, Leon Barrett, Romain Thibaux and Dan Klein. 2006. Learning accurate, compact, and interpretable tree annotation. COLING-ACL-06, pages 443-440.
22. Slav Petrov and Dan Klein. 2007. Improved inference for unlexicalized parsing. HLT-NAACL-07, pages 401-411.
23. Stefan Riezler, Tracy H. King, Ronald M. Kaplan, Richard Crouch, John T. III Maxwell and Mark Johnson. 2002. Parsing the wall street journal using a lexical-functional grammar and discriminative estimation techniques. ACL-02, pages 271-278.
24. Kenji Sagae and Alon Lavie. 2006. Parser combination by reparsing. HLT-NAACL-06, pages 129-132.
25. Daniel Zeman and Zdeněk Žabokrtský. Improving Parsing Accuracy by Combining Diverse Dependency Parsers. IWPT-05.