Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77485
Title: A convolutional attention model for text classification
Authors: Du, J 
Gui, L
Xu, R
He, Y
Issue Date: 2018
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2018, v. 10619, p. 183-195 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
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.
Description: 6th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2017, Dalian, China, 8-12 Nov 2017
URI: http://hdl.handle.net/10397/77485
ISBN: 9.78332E+12
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-73618-1_16
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