Character-level convolutional networks for text classification

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 649-657

  Zhang, Xiang ^a   Zhao, Junbo ^a   Lecun, Yann ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; INFORMATION SCIENCE; TEXT PROCESSING;

BAG OF WORDS; CHARACTER LEVEL; CONVOLUTIONAL NETWORKS; DEEP LEARNING; LARGE-SCALE DATASETS; STATE OF THE ART; TEXT CLASSIFICATION; TRADITIONAL MODELS;

RECURRENT NEURAL NETWORKS;

EID: 84965162393     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. L. Bottou, F. Fogelman Soulié, P. Blanchet, and J. Lienard. Experiments with time delay networks and dynamic time warping for speaker independent isolated digit recognition. In Proceedings of EuroSpeech 89, volume 2, pages 537-540, Paris, France, 1989.
2. Y.-L. Boureau, F. Bach, Y. Le Cun, and J. Ponce. Learning mid-level features for recognition. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2559-2566. IEEE, 2010.
3. Y.-L. Boureau, J. Ponce, and Y. Le Cun. A theoretical analysis of feature pooling in visual recognition. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 111-118, 2010.
4. R. Collobert, K. Kavukcuoglu, and C. Farabet. Torch7: A matlab-like environment for machine learning. In BigLearn, NIPS Workshop, number EPFL-CONF-192376, 2011.
5. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. Natural language processing (almost) from scratch. J. Mach. Learn. Res., 12:2493-2537, Nov. 2011.
6. C. dos Santos and M. Gatti. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 69-78, Dublin, Ireland, August 2014. Dublin City University and Association for Computational Linguistics.
7. C. Fellbaum. Wordnet and wordnets. In K. Brown, editor, Encyclopedia of Language and Linguistics, pages 665-670, Oxford, 2005. Elsevier.
8. A. Graves and J. Schmidhuber. Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Networks, 18(5):602-610, 2005.
9. K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber. LSTM: A search space odyssey. CoRR, abs/1503.04069, 2015.
10. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
11. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Comput., 9(8):1735-1780, Nov. 1997.
12. T. Joachims. Text categorization with suport vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, pages 137-142. Springer-Verlag, 1998.
13. R. Johnson and T. Zhang. Effective use of word order for text categorization with convolutional neural networks. CoRR, abs/1412.1058, 2014.
14. K. S. Jones. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1):11-21, 1972.
15. I. Kanaris, K. Kanaris, I. Houvardas, and E. Stamatatos. Words versus character n-grams for anti-spam filtering. International Journal on Artificial Intelligence Tools, 16(06):1047-1067, 2007.
16. Y. Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751, Doha, Qatar, October 2014. Association for Computational Linguistics.
17. Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, Winter 1989.
18. Y. Le Cun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
19. J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, and C. Bizer. DBpedia-A large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web Journal, 2014.
20. G. Lev, B. Klein, and L. Wolf. In defense of word embedding for generic text representation. In C. Biemann, S. Handschuh, A. Freitas, F. Meziane, and E. Mtais, editors, Natural Language Processing and Information Systems, volume 9103 of Lecture Notes in Computer Science, pages 35-50. Springer International Publishing, 2015.
21. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research, 5:361-397, 2004.
22. J. McAuley and J. Leskovec. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM Conference on Recommender Systems, RecSys'13, pages 165-172, New York, NY, USA, 2013. ACM.
23. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 3111-3119. 2013.
24. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 807-814, 2010.
25. R. Pascanu, T. Mikolov, and Y. Bengio. On the difficulty of training recurrent neural networks. In ICML 2013, volume 28 of JMLR Proceedings, pages 1310-1318. JMLR.org, 2013.
26. B. Polyak. Some methods of speeding up the convergence of iteration methods. fUSSRg Computational Mathematics and Mathematical Physics, 4(5):1-17, 1964.
27. D. Rumelhart, G. Hintont, and R. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533-536, 1986.
28. C. D. Santos and B. Zadrozny. Learning character-level representations for part-of-speech tagging. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1818-1826, 2014.
29. Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 101-110. ACM, 2014.
30. I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton. On the importance of initialization and momentum in deep learning. In S. Dasgupta and D. Mcallester, editors, Proceedings of the 30th International Conference on Machine Learning (ICML-13), volume 28, pages 1139-1147. JMLR Workshop and Conference Proceedings, May 2013.
31. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. J. Lang. Phoneme recognition using time-delay neural networks. Acoustics, Speech and Signal Processing, IEEE Transactions on, 37(3):328-339, 1989.
32. C. Wang, M. Zhang, S. Ma, and L. Ru. Automatic online news issue construction in web environment. In Proceedings of the 17th International Conference on World Wide Web, WWW'08, pages 457-466, New York, NY, USA, 2008. ACM.