Supervised and semi-supervised text categorization using LSTM for region embeddings

33rd International Conference on Machine Learning, ICML 2016

Volumn 2, Issue , 2016, Pages 794-802

  Johnson, Rie ^a   Zhang, Tong ^b  

^a RJ Research Consulting   (United States)

^b BAIDU INC   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; CONVOLUTION; NEURAL NETWORKS; TEXT PROCESSING; VIDEO STREAMING;

BENCHMARK DATASETS; CONVOLUTIONAL NEURAL NETWORK; EMBEDDING METHOD; LINEAR MODELING; LONG SHORT TERM MEMORY; NONLINEAR FEATURES; SEMI-SUPERVISED; TEXT CATEGORIZATION;

LEARNING SYSTEMS;

EID: 84998953478     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Ando, Rie K. and Zhang, Tong. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853, 2005.
2. Ando, Rie K. and Zhang, Tong. Two-view feature generation model for semi-supervised learning. In Proceedings of ICML, 2007.
3. Cho, Kyunghyun, van Merriënboer, Bart, Gulcehre, Caglar, Bahdanau, Dzmitry, Bougares, Fethi, Schwenk, Holger, and Bengio, Yoshua. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of EMNLP, 2014.
4. Dai, Andrew M. and Le, Quoc V. Semi-supervised sequence learning. In NIPS, 2015.
5. Gers, Felix A., Schmidhuder, Jürgen, and Cummins, Fred. Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10):2451-2471, 2000.
6. Hinton, Geoffrey E., Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv: 1207.0580, 2012.
7. Hochreiter, Sepp and Schmidhuder, Jürgen. Long shortterm memory. Neural Computation, 9(8):1735-1780, 1997.
8. Joachims, Thorsten. Text categorization with support vector machines: Learning with many relevant features. In ECML, 1998.
9. Johnson, Rie and Zhang, Tong. Effective use of word order for text categorization with convolutional neural networks. In NAACL HLT, 2015a.
10. Johnson, Rie and Zhang, Tong. Semi-supervised convolutional neural networks for text categorization via region embedding. In NIPS, 2015b.
11. Lai, Siwei, Xu, Liheng, Liu, Kang, and Zhao, Jun. Recurrent convolutional neural networks for text classification. In Proceedings of AAAI, 2015.
12. Le, Phong and Zuidema, Willem. Compositional distributional semantics with long short-term memory. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, 2015.
13. Le, Quoc and Mikolov, Tomas. Distributed representations of sentences and documents. In Proceedings of ICML, 2014.
14. LeCun, Yann, Bottou, León, Bengio, Yoshua, and Haffner, Patrick. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, 1986.
15. Lewis, David D., Yang, Yiming, Rose, Tony G., and Li, Fan. RCV1: A new benchmark collection for text categorization research. Journal of Marchine Learning Research, 5:361-397, 2004.
16. Mikolov, Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg, and Dean, Jeffrey. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
17. Sutskever, Hya, Vinyals, Oriol, and Le, Quoc V. Sequence to sequence learning with neural netowkrs. In NIPS, 2014.
18. Tai, Kai Sheng, Socher, Richard, and Manning, Christopher D. Improved semantic representations from treestructured long short-term memory networks. In Proceedings of ACL, 2015.
19. Tang, Duyu, Qin, Bing, and Liu, Ting. Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of EMNLP, 2015.
20. Tieleman, Tijman and Hinton, Geoffrey. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012.
21. Wang, Sida and Manning, Christopher D. Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of ACL, pp. 90-94, 2012.
22. Zaremba, Wojciech and Sutskever, Iiya. Learning to execute. arXiv. 14W.4615, 2014.
23. Zhang, Xiang, Zhao, Junbo, and LeCunn, Yann. Characterlevel convolutional networks for text classification. In NIPS, 2015.
24. Zhu, Xiaodan, Sobhani, Parinaz, and Guo, Hongyu. Long short-term memory over recursive structures. In Proceedings of ICML, 2015.