An effective neural network model for graph-based dependency parsing

ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference

Volumn 1, Issue , 2015, Pages 313-322

  Pei, Wenzhe ^a   Ge, Tao ^a   Chang, Baobao ^b  

^a Peking University   (China)

^b Jiangsu Collaborative Innovation Center for Language Competence   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; NATURAL LANGUAGE PROCESSING SYSTEMS;

ACTIVATION FUNCTIONS; DEPENDENCY PARSING; FEATURE COMBINATION; GENERALIZATION ABILITY; GRAPH-BASED; HIGH-ORDER; NEURAL NETWORK MODEL; TREEBANKS;

GRAPHIC METHODS;

EID: 84943760366     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p15-1031     Document Type: Conference Paper

References (21)

1. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137-1155.
2. Xavier Carreras. 2007. Experiments with a higherorder projective dependency parser. In EMNLPCoNLL, pages 957-961.
3. Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 740-750, Doha, Qatar, October. Association for Computational Linguistics.
4. Wenliang Chen, Yue Zhang, and Min Zhang. 2014. Feature embedding for dependency parsing. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 816-826, Dublin, Ireland, August. Dublin City University and Association for Computational Linguistics.
5. Ronan Collobert, Jason Weston, Ĺeon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493-2537.
6. John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 999999:2121-2159.
7. Jason M Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In Proceedings of the 16th conference on Computational linguistics-Volume 1, pages 340-345. Association for Computational Linguistics.
8. Jason Eisner. 2000. Bilexical grammars and their cubic-Time parsing algorithms. In Advances in probabilistic and other parsing technologies, pages 29-61. Springer.
9. David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003. English gigaword. Linguistic Data Consortium, Philadelphia.
10. Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov. 2012. Improving neural networks by preventing coadaptation of feature detectors. arXiv preprint arXiv:1207.0580.
11. Terry Koo and Michael Collins. 2010. Efficient thirdorder dependency parsers. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1-11. Association for Computational Linguistics.
12. Phong Le and Willem Zuidema. 2014. The insideoutside recursive neural network model for dependency parsing. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 729-739, Doha, Qatar, October. Association for Computational Linguistics.
13. Ryan T McDonald and Fernando CN Pereira. 2006. Online learning of approximate dependency parsing algorithms. In EACL. Citeseer.
14. Ryan McDonald, Koby Crammer, and Fernando Pereira. 2005. Online large-margin training of dependency parsers. In Proceedings of the 43rd annual meeting on association for computational linguistics, pages 91-98. Association for Computational Linguistics.
15. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
16. Wenzhe Pei, Tao Ge, and Baobao Chang. 2014. Maxmargin tensor neural network for chinese word segmentation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 293-303, Baltimore, Maryland, June. Association for Computational Linguistics.
17. Holger Schwenk, Anthony Rousseau, and Mohammed Attik. 2012. Large, pruned or continuous space language models on a gpu for statistical machine translation. In Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model On the Future of Language Modeling for HLT, pages 11-19. Association for Computational Linguistics.
18. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631-1642, Seattle, Washington, USA, October. Association for Computational Linguistics.
19. Kristina Toutanova, Dan Klein, Christopher D Manning, and Yoram Singer. 2003. Feature-rich part-ofspeech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, pages 173-180. Association for Computational Linguistics.
20. Hiroyasu Yamada and Yuji Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proceedings of IWPT, volume 3, pages 195-206.
21. Yue Zhang and Joakim Nivre. 2011. Transition-based dependency parsing with rich non-local features. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 188-193, Portland, Oregon, USA, June. Association for Computational Linguistics