A convolutional attention model for text classification

Jiachen Du, Lin Gui, Ruifeng Xu, Yulan He

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

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.
Original languageEnglish
Title of host publicationEnglish
Pages183-195
DOIs
Publication statusPublished - 5 Jan 2018
Event6th CCF International Conference - Dalian, China
Duration: 8 Nov 201712 Nov 2017

Publication series

NameNatural Language Processing and Chinese Computing
Volume10619
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th CCF International Conference
CountryChina
CityDalian
Period8/11/1712/11/17

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  • Cite this

    Du, J., Gui, L., Xu, R., & He, Y. (2018). A convolutional attention model for text classification. In English (pp. 183-195). [Chapter 16] (Natural Language Processing and Chinese Computing; Vol. 10619). https://doi.org/10.1007/978-3-319-73618-1_16