Abstract
Neural network models with attention mechanism have shown their efficiencies on various tasks. However, there is little research work on attention mechanism for text classification and existing attention model for text classification lacks of cognitive intuition and mathematical explanation. In this paper, we propose a new architecture of neural network based on the attention model for text classification. In particular, we show that the convolutional neural network (CNN) is a reasonable model for extracting attentions from text sequences in mathematics. We then propose a novel attention model base on CNN and introduce a new network architecture which combines recurrent neural network with our CNN-based attention model. Experimental results on five datasets show that our proposed models can accurately capture the salient parts of sentences to improve the performance of text classification.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China 61370165, U1636103, 61632011, 61528302, Shenzhen Foundational Research Funding JCYJ20150625142543470, Guangdong Provincial Engineering Technology Research Center for Data Science 2016KF09 and grant from the Hong Kong Polytechnic University (G-YBJP).
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Du, J., Gui, L., Xu, R., He, Y. (2018). A Convolutional Attention Model for Text Classification. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_16
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DOI: https://doi.org/10.1007/978-3-319-73618-1_16
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