Dependency based embeddings for sentence classification tasks

2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference

Volumn , Issue , 2016, Pages 1490-1500

  Komninos, Alexandros ^a   Manandhar, Suresh ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY TREES; COMPUTATIONAL LINGUISTICS; EMBEDDINGS; NEURAL NETWORKS; SEMANTICS; SUPPORT VECTOR MACHINES;

CLASSIFICATION METHODS; CONTEXT FEATURES; CONVOLUTIONAL NEURAL NETWORK; DEPENDENCY GRAPHS; SEMANTIC RELATIONS; SENTENCE CLASSIFICATIONS; SENTIMENT CLASSIFICATION; SHORT TERM MEMORY;

CLASSIFICATION (OF INFORMATION);

EID: 84994137854     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/n16-1175     Document Type: Conference Paper

References (32)

1. Marco Baroni and Alessandro Lenci. 2010. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics, 36(4):673-721.
2. Danqi Chen and Christopher D 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), volume 1, pages 740-750.
3. Jianpeng Cheng and Dimitri Kartsaklis. 2015. Syntax-aware multi-sense word embeddings for deep compositional models of meaning. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1531-1542, Lisbon, Portugal, September. Association for Computational Linguistics.
4. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493-2537.
5. Marie-Catherine De Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, and Christopher D Manning. 2014. Universal stanford dependencies: A cross-linguistic typology. In Proceedings of LREC, pages 4585-4592.
6. John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121-2159.
7. Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: The concept revisited. In Proceedings of the 10th international conference on World Wide Web, pages 406-414. ACM.
8. Kazuma Hashimoto, Pontus Stenetorp, Makoto Miwa, and Yoshimasa Tsuruoka. 2014. Jointly learning word representations and composition functions using predicate-argument structures. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1544-1555.
9. Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2009. Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, pages 94-99. Association for Computational Linguistics.
10. Felix Hill, Roi Reichart, and Anna Korhonen. 2015. Simlex-999: Evaluating semantic models with (genuine) similarity estimation. Computational Linguistics.
11. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.
12. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751. Association for Computational Linguistics.
13. Omer Levy and Yoav Goldberg. 2014. Dependencybased word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, volume 2, pages 302-308.
14. Omer Levy, Yoav Goldberg, and Ido Dagan. 2015. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3:211-225.
15. Xin Li and Dan Roth. 2002. Learning question classifiers. In Proceedings of the 19th international conference on Computational linguistics-Volume 1, pages 1-7. Association for Computational Linguistics.
16. Jiwei Li, Thang Luong, Dan Jurafsky, and Eduard Hovy. 2015. When are tree structures necessary for deep learning of representations? In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2304-2314, Lisbon, Portugal, September. Association for Computational Linguistics.
17. Wang Ling, Chris Dyer, Alan Black, and Isabel Trancoso. 2015. Two/too simple adaptations of word2vec for syntax problems. Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), Denver, CO.
18. Oren Melamud, Omer Levy, and Ido Dagan, 2015. Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, chapter A Simple Word Embedding Model for Lexical Substitution, pages 1-7. Association for Computational Linguistics.
19. Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. In Proceedings of ICLR Workshop.
20. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119.
21. Andriy Mnih and Yee Whye Teh. 2012. A fast and simple algorithm for training neural probabilistic language models. In In Proceedings of the International Conference on Machine Learning.
22. Lili Mou, Hao Peng, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin. 2015. Discriminative neural sentence modeling by tree-based convolution. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2315-2325, Lisbon, Portugal, September. Association for Computational Linguistics.
23. Sebastian Padó and Mirella Lapata. 2007. Dependency-based construction of semantic space models. Computational Linguistics, 33(2):161-199.
24. The Nghia Pham, Germán Kruszewski, Angeliki Lazaridou, and Marco Baroni. 2015. Jointly optimizing word representations for lexical and sentential tasks with the c-phrase model. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 971-981. Association for Computational Linguistics.
25. Richard Socher, Brody Huval, Christopher D Manning, and Andrew Y Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1201-1211. Association for Computational Linguistics.
26. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, D. Christopher 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. Association for Computational Linguistics.
27. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929-1958.
28. Sheng Kai Tai, Richard Socher, and D. Christopher Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1556-1566. Association for Computational Linguistics.
29. Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of the 48th annual meeting of the association for computational linguistics, pages 384-394. Association for Computational Linguistics.
30. Peter D Turney, Patrick Pantel, et al. 2010. From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research, 37(1):141-188.
31. Kun Xu, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2015. Semantic relation classification via convolutional neural networks with simple negative sampling. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 536-540, Lisbon, Portugal, September. Association for Computational Linguistics.
32. Matthew D Zeiler. 2012. Adadelta: An adaptive learning rate method. arXiv:1212.5701. arXiv preprint