Recurrent convolutional neural networks for text classification

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2015, Pages 2267-2273

  Lai, Siwei ^a   Xu, Liheng ^a   Liu, Kang ^a   Zhao, Jun ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CONVOLUTION; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS;

CONTEXTUAL INFORMATION; CONVOLUTIONAL NEURAL NETWORK; KNOWLEDGE BASIS; STATE-OF-THE-ART METHODS; TEXT CLASSIFICATION; TEXT CLASSIFIERS; WINDOW-BASED; WORD REPRESENTATIONS;

TEXT PROCESSING;

EID: 84959872385     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Aggarwal, C. C, and Zhai, C. 2012. A survey oftext classification algorithms. In Mining text data. Springer. 163-222.
2. Baroni, M.; Dinu, G.; and Kruszewski, G. 2014. Don't count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In ACL, 238-247.
3. Bengio, Y.; Ducharme, R.; Vincent, P.; and Jauvin, C. 2003. A Neural Probabilistic Language Model. JMLR 3:1137-1155.
4. Bengio, Y; Courville, A.; and Vincent, P. 2013. Representation learning: A review and new perspectives. IEEE TPAMI 35(8): 1798-1828.
5. Bottou, L. 1991. Stochastic gradient learning in neural networks. In Proceedings of Neuro-Nimes, volume 91.
6. Cai, L., and Hofmann, T. 2003. Text categorization by boosting automatically extracted concepts. In SIGIR, 182-189.
7. Charniak, E., and Johnson, M. 2005. Coarse-to-fine n-best parsing and maxent discriminative reranking. In ACL, 173-180.
8. Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; and Kuksa, P. 2011. Natural language processing (almost) from scratch. JMLR 12:2493-2537.
9. Cover, T. M., and Thomas, J. A. 2012. Elements of information theory. John Wiley & Sons.
10. Elman, J. L. 1990. Finding structure in time. Cognitive science 14(2): 179-211.
11. Erhan, D.; Bengio, Y; Courville, A.; Manzagol, P.-A.; Vincent, P.; and Bengio, S. 2010. Why does unsupervised pretraining help deep learning? JMLR 11:625-660.
12. Hingmire, S.; Chougule, S.; Palshikar, G. K.; and Chakraborti, S. 2013. Document classification by topic labeling. In SIGIR, 877-880.
13. Hinton, G. E., and Salakhutdinov, R. R. 2006. Reducing the dimensionality of data with neural networks. Science 313(5786):504-507.
14. Huang, E. H.; Socher, R.; Manning, C. D.; and Ng, A. Y 2012. Improving word representations via global context and multiple word prototypes. In ACL, 873-882.
15. Kalchbrenner, N., and Blunsom, P. 2013. Recurrent convo-lutional neural networks for discourse compositionality. In Workshop on CVSC, 119-126.
16. Klementiev, A.; Titov, I.; and Bhattarai, B. 2012. Inducing crosslingual distributed representations of words. In Coling, 1459-1474.
17. Le, Q. V, and Mikolov, T. 2014. Distributed representations of sentences and documents. In ICML.
18. Lewis, D. D. 1992. An evaluation of phrasal and clustered representations on a text categorization task. In SIGIR, 37-50.
19. Li, W.; Sun, L.; and Zhang, D. 2008. Text classification based on labeled-Ida model. Chinese Journal of Computers 31(4):620-627.
20. Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013. Distributed representations of words and phrases and their compositionality. In NIPS, 3111-3119.
21. Mikolov, T.; Yih, W.-t.; and Zweig, G. 2013. Linguistic regularities in continuous space word representations. In NAACL-HLT, 746-751.
22. Mikolov, T. 2012. Statistical language models based on neural networks. Ph.D. Dissertation, Brno University of Technology.
23. Mnih, A., and Hinton, G. 2007. Three new graphical models for statistical language modelling. In ICML, 641-648.
24. Morin, R, and Bengio, Y. 2005. Hierarchical probabilistic neural network language model. In AISTATS, 246-252.
25. Ng, A. Y 2004. Feature selection, 11 vs. 12 regularization, and rotational invariance. In ICML, 78.
26. Petrov, S.; Barrett, L.; Thibaux, R.; and Klein, D. 2006. Learning accurate, compact, and interpretable tree annotation. In Coling-ACL, 433-440.
27. Plaut, D. C, and Hinton, G. E. 1987. Learning sets of filters using back-propagation. Computer Speech & Language 2(1):35-61.
28. Post, M., and Bergsma, S. 2013. Explicit and implicit syntactic features for text classification. In ACL, 866-872.
29. Socher, R.; Huang, E. H.; Pennington, J.; Ng, A. Y.; and Manning, C. D. 2011a. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In NIPS, volume 24, 801-809.
30. Socher, R.; Pennington, J.; Huang, E. H.; Ng, A. Y; and Manning, C. D. 2011b. Semi-supervised recursive autoencoders for predicting sentiment distributions. In EMNLP, 151-161.
31. Socher, R.; Perelygin, A.; Wu, J. Y; Chuang, J.; Manning, C. D.; Ng, A. Y; and Potts, C. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In EMNLP, 1631-1642.
32. Turian, J.; Ralinov, L.; and Bengio, Y. 2010. Word representations: a simple and general method for semi-supervised learning. In ACL, 384-394.
33. Wang, S., and Manning, C. D. 2012. Baselines and bigrams: Simple, good sentiment and topic classification. In ACL, 90-94.