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Convolution-based neural attention with applications to sentiment classification
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Du, Jiachen, Gui, Lin, He, Yulan, Xu, Ruifeng and Wang, Xuan (2019) Convolution-based neural attention with applications to sentiment classification. IEEE Access, 7 . 27983 -27992. doi:10.1109/ACCESS.2019.2900335 ISSN 2169-3536.
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Official URL: https://doi.org/10.1109/ACCESS.2019.2900335
Abstract
Neural attention mechanism has achieved many successes in various tasks in natural language processing. However, existing neural attention models based on a densely connected network are loosely related to the attention mechanism found in psychology and neuroscience. Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism, we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model. We then introduce a new network architecture, which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model. The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level.
Item Type: | Journal Article | |||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Natural language processing (Computer science), Neural networks (Computer science) | |||||||||||||||||||||
Journal or Publication Title: | IEEE Access | |||||||||||||||||||||
Publisher: | IEEE | |||||||||||||||||||||
ISSN: | 2169-3536 | |||||||||||||||||||||
Official Date: | 18 March 2019 | |||||||||||||||||||||
Dates: |
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Volume: | 7 | |||||||||||||||||||||
Page Range: | 27983 -27992 | |||||||||||||||||||||
DOI: | 10.1109/ACCESS.2019.2900335 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
Date of first compliant deposit: | 1 May 2019 | |||||||||||||||||||||
Date of first compliant Open Access: | 7 May 2019 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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