MaltOptimizer: Fast and effective parser optimization

Natural Language Engineering

Volumn 22, Issue 2, 2016, Pages 187-213

  Ballesteros, Miguel ^a   Nivre, Joakim ^b  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b UPPSALA UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; FEATURE EXTRACTION; LEARNING SYSTEMS; OPTIMIZATION; SPECIFICATION LANGUAGES;

APPLICATION DEVELOPERS; DEFAULT SETTING; DEPENDENCY PARSING; INTERACTIVE SYSTEM; NON-TRIVIAL TASKS; PARAMETER-TUNING; PARSING ALGORITHM; STATISTICAL PARSER;

PARAMETER ESTIMATION;

EID: 84958037361     PISSN: 13513249     EISSN: 14698110     Source Type: Journal    
DOI: 10.1017/S1351324914000035     Document Type: Article

References (54)

1. Agirre, E., Atutxa, A., and Sarasola, K. 12/2012. Contribution of complex lexical information to solve syntactic ambiguity in Basque. In Proceedings of the 24th International Conference on Computational Linguistics (COLING), Mumbai, India, pp. 97-113.
2. Agirre, E., Bengoetxea, K., Gojenola, K., and Nivre, J. 2011. Improving dependency parsing with semantic classes. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL), Stroudsburg, PA, pp. 699-703.
3. Ballesteros, M. 2013a. Effective morphological feature selection with MaltOptimizer at the SPMRL 2013 shared task. In Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL), Stroudsburg, PA, pp. 63-70.
4. Ballesteros, M. 2013b. Exploring automatic feature selection for transition-based dependency parsing. Procesamiento del Lenguaje Natural 51: 119-26.
5. Ballesteros, M., and Nivre, J. 2012a. MaltOptimizer: a system for MaltParser optimization. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC), Paris, pp. 2757-63.
6. Ballesteros, M., Gómez-Rodr?guez, C., and Nivre, J. 2012. Optimizing planar and 2-planar parsers with MaltOptimizer. Procesamiento del Lenguaje Natural 49: 171-8.
7. Ballesteros, M., Mille, S., and Burga, A. 2013. Exploring morphosyntactic annotation over a Spanish corpus for dependency parsing. In Proceedings of the Second International Conference on Dependency Linguistics (DEPLING), Prague, pp. 13-22.
8. Ballesteros, M., and Nivre, J. 2012b. 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), Stroudsburg, PA, pp. 58-62.
9. Bohnet, B., and Nivre, J. 2012. A transition-based system for joint part-of-speech tagging and labeled non-projective dependency parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Stroudsburg, PA, pp. 1455-65.
10. Buchholz, S., and Marsi, E. 2006. CoNLL-X shared task on multilingual dependency parsing. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), Stroudsburg, PA, pp. 149-64.
11. Cassel, S. 2009. MaltParser and LIBLINEAR: Transition-Based Dependency Parsing with Linear Classification for Feature Model Optimization. M. Phil. thesis, Uppsala University, Uppsala, Sweden.
12. Chang, C.-C., and Lin, C.-J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2: 1-27. Software available at http://www. csie. ntu. edu. tw/ cjlin/libsvm.
13. Choi, J. D., and McCallum, A. 2013. Transition-based dependency parsing with selectional branching. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 1052-62.
14. Cirik, V., and Sensoy, H. 2013. The AI-KU system at the SPMRL 2013 shared task: unsupervised features for dependency parsing. In Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL), Stroudsburg, PA, pp. 79-85.
15. Covington, M. A. 2001. A fundamental algorithm for dependency parsing. In Proceedings of the 39th Annual ACM Southeast Conference, New York, NY, pp. 95-102.
16. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. 2006. Online passiveaggressive algorithms. Journal of Machine Learning Research, 7, 551-85. MIT Press.
17. Daelemans, W., Hoste, V., Meulder, F. De, and Naudts, B. 2003. Combined optimization of feature selection and algorithm parameters in machine learning of language. Proceedings of the 14th European Conference on Machine Learning (ECML), Berlin, pp. 84-95.
18. Das, A., and Kempe, D. 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), Madison, WI, pp. 1057-64.
19. Doraisamy, S., Golzari, S., Norowi, N. M., Sulaiman, Md N., and Udzir, N. I. 2008. A study on feature selection and classification techniques for automatic genre classification of traditional malay music. Proceedings of the Ninth International Conference on Music Information Retrieval (ISMIR), Philadelphia, PA, pp. 331-6.
20. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. 2008. Liblinear: a library for large linear classification. Journal of Machine Learning Research 9: 1871-4.
21. Goenaga, I., Gojenola, K., and Ezeiza, N. 2013. Exploiting the contribution of morphological information to parsing: the BASQUE TEAM system in the SPMRL 2013 shared task. In Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL), Stroudsburg, PA, pp. 71-7.
22. Gómez-Rodr?guez, C., and Fernández-González, D. 2012. Dependency parsing with undirected graphs. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Stroudsburg, PA, pp. 66-76.
23. Gómez-Rodrguez, C., and Nivre, J. 2010. A transition-based parser for 2-planar dependency structures. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 1492-501.
24. Guyon, I., and Elisseeff, A. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3: 1157-82. MIT Press.
25. Hall, J 2008. Transition-Based Natural Language Parsing with Dependency and Constituency Representations. PhD thesis, Växjö University, Sweden.
26. Hall, J., Nilsson, J., Nivre, J., Eryi? git, G., Megyesi, B., Nilsson, M., and Saers, M. 2007. Single malt or blended? A study in multilingual parser optimization. In Proceedings of the CoNLL Shared Task at the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL), Stroudsburg, PA, pp. 933-9.
27. Kool, A., Zavrel, J., and Daelemans, W. 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), Tilburg, pp. 93-100.
28. Korycinski, D., Crawford, M., Barnes, J. W., and Ghosh, J. 2003. Adaptive feature selection for hyperspectral data analysis. In Proceedings of the SPIE Conference on Image and Signal Processing for Remote Sensing IX, Bellingham, WA, pp. 213-25.
29. Mambrini, F., and Passarotti, M. C. 2012. Will a parser overtake Achilles? First experiments on parsing the ancient Greek dependency treebank. In Proceedings of the Eleventh International Workshop on Treebanks and Linguistic Theories (TLT11), Sofia, pp. 133-44.
30. McCallum, A 2003. Efficiently inducing features of conditional random fields. In Proceedings of the Conference on Uncertainty in AI, Burlington, MA, pp. 403-10.
31. McDonald, R 2006. Discriminative Learning and Spanning Tree Algorithms for Dependency Parsing. PhD thesis, University of Pennsylvania, Philadelphia, PA.
32. McDonald, R., Lerman, K., and Pereira, F. 2006. Multilingual dependency analysis with a two-stage discriminative parser. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), Stroudsburg, PA, pp. 216-20.
33. McDonald, R., and Nivre, J. 2007. Characterizing the errors of data-driven dependency parsing models. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Stroudsburg, PA, pp. 122-31.
34. Mitchell, T. M. 1997. Machine Learning. McGraw-Hill, New York, NY.
35. Nilsson, P., and Nugues, P. 2010. Automatic discovery of feature sets for dependency parsing. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING), Beijing, China, pp. 824-32.
36. Nivre, J. 2003. An efficient algorithm for projective dependency parsing. In Proceedings of the 8th International Workshop on Parsing Technologies (IWPT), pp. 149-60.
37. Nivre, J. 2004. Incrementality in deterministic dependency parsing. In Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together at the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 50-7.
38. Nivre, J. 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), Stroudsburg, PA, pp. 396-403.
39. Nivre, J. 2008. Algorithms for deterministic incremental dependency parsing. Computational Linguistics 34(4): 513-53. MIT Press.
40. Nivre, J. 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), Stroudsburg, PA, pp. 351-9.
41. Nivre, J., and Hall, J. 2010. A Quick Guide to MaltParser Optimization. Tech. rept. 1. MaltParser (maltparser. org).
42. Nivre, J., Hall, J., Kübler, S., McDonald, R., Nilsson, J., Riedel, S., and Yuret, D. 2007a. The CoNLL 2007 shared task on dependency parsing. In Proceedings of the CoNLL Shared Task at the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL), Stroudsburg, PA, pp. 915-32.
43. Nivre, J., Hall, J., and Nilsson, J. 2006b. Maltparser: a data-driven parser-generator for dependency parsing. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC), Paris, pp. 2216-9.
44. Nivre, J., Hall, J., Nilsson, J., Chanev, A., Eryi? git, G., Kübler, S., Marinov, S., and Marsi, E. 2007b. Maltparser: a language-independent system for data-driven dependency parsing. Natural Language Engineering 13: 95-135.
45. Nivre, J., Hall, J., Nilsson, J., Eryi ? git, G., and Marinov, S. 2006a. Labeled pseudo-projective dependency parsing with support vector machines. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), Stroudsburg, PA, pp. 221-5.
46. Nivre, J., Kuhlmann, M., and Hall, J. 2009. An improved oracle for dependency parsing with online reordering. In Proceedings of the 11th International Conference on Parsing Technologies (IWPT), Stroudsburg, PA, pp. 73-6.
47. Nivre, J., and Nilsson, J. 2005. Pseudo-projective dependency parsing. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 99-106.
48. Prudhvi Kosaraju, Kesidi, S. R., Ainavolu, V. B. R. and Kukkadapu, P. 2010. Experiments on Indian language dependency parsing. In Proceedings of the ICON-2010 Tools Contest on Indian Language Dependency Parsing, Kharagpur, India, pp. 40-45.
49. Seddah, D., Tsarfaty, R., Kübler, S., Candito, M., Choi, J. D., Farkas, R., Foster, J., Goenaga, I., Galletebeitia, K. G., Goldberg, Y., Green, S., Habash, N., Kuhlmann, M., Maier, W., Marton, Y., Nivre, J., Przepiórkowski, A., Roth, R., Seeker, W., Versley, Y., Vincze, V., Wolinski, M., and Wróblewska, A. 2013. Overview of the SPMRL 2013 shared task: a cross-framework evaluation of parsing morphologically rich languages. In Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL), Stroudsburg, PA, pp. 146-82.
50. Seraji, M., Megyesi, B., and Nivre, J. 2012. Dependency parsers for persian. In Proceedings of 10th Workshop on Asian Language Resources, at 24th International Conference on Computational Linguistics (COLING), Mumbai, India, pp. 35-43.
51. Tsarfaty, R., Nivre, J., and Andersson, E. 2012. Cross-framework evaluation for statistical parsing. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL), Stroudsburg, PA, pp. 44-54.
52. van den Bosch, A. 2004. Wrapped progressive sampling search for optimizing learning algorithm parameters. In Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence, Groningen, The Netherlands, pp. 219-26.
53. Yli-Jyrä, A. M. 2003. Multiplanarity-a model for dependency structures in treebanks. In Proceedings of the Second Workshop on Treebanks and Linguistic Theories (TLT), Mathematical Modelling in Physics, Engineering and Cognitive Sciences, vol. 9, pp. 189-200. Växjö, Sweden: Växjö University Press.
54. Zhang, Y., and Nivre, J. 2011. Transition-based dependency parsing with rich non-local features. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 188-93.