Bidirectional LSTM with attention mechanism and convolutional layer for text classification

作者: Gang Liu , Jiabao Guo

DOI: 10.1016/J.NEUCOM.2019.01.078

关键词:

摘要: … ), attention mechanism and the convolutional layer is proposed in this paper. The proposed architecture is called attention-… Attention mechanism is employed to give different focus to the …

参考文章(73)
Richard Socher, Andrew Y. Ng, Eric H. Huang, Christopher D. Manning, Jeffrey Pennington, Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions empirical methods in natural language processing. pp. 151- 161 ,(2011)
Søren Kaae Sønderby, Casper Kaae Sønderby, Ole Winther, Henrik Nielsen, Convolutional LSTM Networks for Subcellular Localization of Proteins arXiv: Quantitative Methods. ,(2015) , 10.1007/978-3-319-21233-3_6
Brody Huval, Richard Socher, Andrew Y. Ng, Christopher D. Manning, Semantic Compositionality through Recursive Matrix-Vector Spaces empirical methods in natural language processing. pp. 1201- 1211 ,(2012)
Łukasz Brocki, Krzysztof Marasek, Deep Belief Neural Networks and Bidirectional Long-Short Term Memory Hybrid for Speech Recognition Archives of Acoustics. ,vol. 40, pp. 191- 195 ,(2015) , 10.1515/AOA-2015-0021
Akihiko Watanabe, Ryohei Sasano, Hiroya Takamura, Manabu Okumura, Generating Personalized Snippets for Web Page Recommender Systems 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). ,vol. 2, pp. 218- 225 ,(2014) , 10.1109/WI-IAT.2014.101
João Silva, Luísa Coheur, Ana Cristina Mendes, Andreas Wichert, From symbolic to sub-symbolic information in question classification Artificial Intelligence Review. ,vol. 35, pp. 137- 154 ,(2011) , 10.1007/S10462-010-9188-4
Sepp Hochreiter, Jürgen Schmidhuber, Long short-term memory Neural Computation. ,vol. 9, pp. 1735- 1780 ,(1997) , 10.1162/NECO.1997.9.8.1735
Lam Hong Lee, Dino Isa, Wou Onn Choo, Wen Yeen Chue, High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic Expert Systems With Applications. ,vol. 39, pp. 1147- 1155 ,(2012) , 10.1016/J.ESWA.2011.07.116