MaltOptimizer: A system for maltparser optimization

Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012

Volumn , Issue , 2012, Pages 2757-2763

  Ballesteros, Miguel ^a   Nivre, Joakim ^b  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^b UPPSALA UNIVERSITY   (Sweden)

Author keywords

Dependency Parsing; MaltParser; Optimization

Indexed keywords

COMPUTATIONAL LINGUISTICS; LEARNING ALGORITHMS; OPEN SYSTEMS; OPTIMIZATION; SPECIFICATION LANGUAGES;

APPLICATION DEVELOPERS; DEPENDENCY PARSING; EMPIRICAL EVALUATIONS; INTERACTIVE SYSTEM; MALTPARSER; NON-TRIVIAL TASKS; OPEN SOURCE SYSTEM; STATISTICAL PARSER;

LEARNING SYSTEMS;

EID: 84991464851     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Miguel Ballesteros and Joakim Nivre. 2012. MaltOptimizer: An Optimization Tool for MaltParser. In Proceedings of the System Demonstration Session of the Thirteenth Conference of the European Chapter of the Association for Computational Linguistics (EACL).
2. Sabine Buchholz and Erwin Marsi. 2006. CoNLL-X shared task on multilingual dependency parsing. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), pages 149-164.
3. Chih-Chung Chang and Chih-Jen Lin, 2001. LIBSVM: A Library for Support Vector Machines. Software available at http: //www. csie. ntu. edu. Tw/-cjlin/libsvm.
4. Michael A. Covington. 2001. A fundamental algorithm for dependency parsing. In Proceedings of the 39th Annual ACM Southeast Conference, pages 95-102.
5. Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, and Yoram Singer. 2006. Online passiveaggressive algorithms. Journal of Machine Learning Research, 7: 551-585.
6. Walter Daelemans, Véronique Hoste, Fien De Meulder, and Bart Naudts. 2003. Combined optimization of feature selection and algorithm parameters in machine learning of language. In Proceedings of the 14th European Conference on Machine Learning (ECML), pages 84-95.
7. Abhimanyu Das and David Kempe. 2011. Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection. In Proceedings of the 28th International Conference on Machine Learning (ICML), pages 1057-1064.
8. Shyamala Doraisamy, Shahram Golzari, Noris Mohd. Norowi, Md Nasir Sulaiman, and Nur Izura Udzir. 2008. A study on feature selection and classification techniques for automatic genre classification of traditional malay music. In Proceedings of the Ninth International Conference on Music Information Retrieval (ISMIR), pages 331-336.
9. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9: 1871-1874.
10. Isabelle Guyon and André Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research, 3: 1157-1182.
11. Johan Hall, Jens Nilsson, Joakim Nivre, Gülsen Eryigit, Beáta Megyesi, Mattias Nilsson, and Markus Saers. 2007. Single malt or blended A study in multilingual parser optimization. In Proceedings of the CoNLL Shared Task of EMNLP-CoNLL 2007, pages 933-939.
12. Dan Klein and Christopher D. Manning. 2002. Conditional structure versus conditional estimation in NLP models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9-16.
13. Anne Kool, Jakub Zavrel, and Walter Daelemans. 2000. Simultaneous feature selection and parameter optimization for memory-based natural language processing. In Proceedings of the Tenth Belgian-Dutch Conference on Machine Learning (BENELEARN), pages 93-100.
14. Donna Korycinski, Melba Crawford, J. Wesley Barnes, and Joydeep Ghosh. 2003. Adaptive feature selection for hyperspectral data analysis. In Proceedings of the SPIE Conference on Image and Signal Processing for Remote Sensing IX, pages 213-225.
15. Andrew McCallum. 2003. Efficiently inducing features of conditional random fields. In Proceedings of the Conference on Uncertainty in AI, pages 403-410.
16. Ryan McDonald and Joakim Nivre. 2007. Characterizing the errors of data-driven dependency parsing models. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 122-131.
17. Ryan McDonald, Koby Crammer, and Fernando Pereira. 2005. Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 91-98.
18. Ryan McDonald, Kevin Lerman, and Fernando Pereira. 2006. Multilingual dependency analysis with a twostage discriminative parser. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), pages 216-220.
19. Peter Nilsson and Pierre Nugues. 2010. Automatic discovery of feature sets for dependency parsing. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 824-832.
20. Joakim Nivre and Johan Hall. 2010. A quick guide to Malt-Parser optimization. Technical report, maltparser. org.
21. Joakim Nivre and Jens Nilsson. 2005. Pseudo-projective dependency parsing. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 99-106.
22. Joakim Nivre, Johan Hall, and Jens Nilsson. 2004. Memory-based dependency parsing. In Proceedings of the 8th Conference on Computational Natural Language Learning (CoNLL), pages 49-56.
23. Joakim Nivre, Johan Hall, and Jens Nilsson. 2006. Maltparser: A data-driven parser-generator for dependency parsing. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC), pages 2216-2219.
24. Joakim Nivre, Johan Hall, Jens Nilsson, Gülsen Eryigit, and Svetoslav Marinov. 2006. Labeled pseudoprojective dependency parsing with support vector machines. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), pages 221-225.
25. Joakim Nivre, Johan Hall, Sandra Kübler, Ryan McDonald, Jens Nilsson, Sebastian Riedel, and Deniz Yuret. 2007. The CoNLL 2007 shared task on dependency parsing. In Proceedings of the CoNLL Shared Task of EMNLPCoNLL 2007, pages 915-932.
26. Joakim Nivre, Marco Kuhlmann, and Johan Hall. 2009. An improved oracle for dependency parsing with online reordering. In Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09), pages 73-76.
27. Joakim Nivre. 2003. An efficient algorithm for projective dependency parsing. In Proceedings of the 8th International Workshop on Parsing Technologies (IWPT), pages 149-160.
28. Joakim Nivre. 2004. Incrementality in deterministic dependency parsing. In Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together (ACL), pages 50-57.
29. Joakim Nivre. 2006. Inductive Dependency Parsing. Springer.
30. Joakim Nivre. 2007. Incremental non-projective dependency parsing. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT), pages 396-403.
31. Joakim Nivre. 2008. Algorithms for deterministic incremental dependency parsing. Computational Linguistics, 34: 513-553.
32. Joakim Nivre. 2009. Non-projective dependency parsing in expected linear time. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL-IJCNLP), pages 351-359.
33. Tapio Pahikkala, Antti Airola, and Tapio Salakoski. 2010. Speeding up greedy forward selection for regularized least-squares. In The Ninth International Conference on Machine Learning and Applications, pages 325-330.
34. Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. 2006. Learning accurate, compact, and interpretable tree annotation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pages 433-440.
35. Antal Van den Bosch. 2004. Wrapped progressive sampling search for optimizing learning algorithm parameters. In Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence.