Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification

doi:10.1016/j.ins.2020.10.021

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification
	Liang, Yunji 1; Li, Huihui 1; Guo, Bin 1; Yu, Zhiwen 1; Zheng, Xiaolong1,2,4 ; Samtani, Sagar 3; Zeng, Daniel D.2,4
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
	2021-02-16
卷号	548 页码:295-312
通讯作者	Liang, Yunji(liangyunji@nwpu.edu.cn) ; Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
摘要	The rapid proliferation of user generated content has given rise to large volumes of text corpora. Increasingly, scholars, researchers, and organizations employ text classification to mine novel insights for high-impact applications. Despite their prevalence, conventional text classification methods rely on labor-intensive feature engineering efforts that are task specific, omit long-term relationships, and are not suitable for the rapidly evolving domains. While an increasing body of deep learning and attention mechanism literature aim to address these issues, extant methods often represent text as a single view and omit multiple sets of features at varying levels of granularity. Recognizing that these issues often result in performance degradations, we propose a novel Spatial View Attention Convolutional Neural Network (SVA-CNN). SVA-CNN leverages an innovative and carefully designed set of multi-view representation learning, a combination of heterogeneous attention mechanisms and CNN-based operations to automatically extract and weight multiple granularities and fine-grained representations. Rigorously evaluating SVA-CNN against prevailing text classification methods on five large-scale benchmark datasets indicates its ability to outperform extant deep learning based classification methods in both performance and training time for document classification, sentiment analysis, and thematic identification applications. To facilitate model reproducibility and extensions, SVA-CNN's source code is also available via GitHub. (c) 2020 Elsevier Inc. All rights reserved.
关键词	View attention Spatial attention Multi-view representation Series and parallel connection Conventional neural network Text classification
DOI	10.1016/j.ins.2020.10.021
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2019YFB2102200] ; ministry of health of China[2017ZX10303401-002] ; ministry of health of China[2017YFC1200302] ; natural science foundation of China[61902320] ; natural science foundation of China[71472175] ; natural science foundation of China[71602184] ; natural science foundation of China[71621002] ; national science foundation[CNS-1850362] ; national science foundation[OAC-1917117] ; fundamental research funds for the central universities[31020180QD140]
项目资助者	National Key Research and Development Program of China ; ministry of health of China ; natural science foundation of China ; national science foundation ; fundamental research funds for the central universities
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000596057300017
出版者	ELSEVIER SCIENCE INC
七大方向——子方向分类	自然语言处理
引用统计	被引频次：37[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42818
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Liang, Yunji; Zheng, Xiaolong
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci, Xian, Shaanxi, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China 3.Indiana Univ, Kelley Sch Business, Operat & Decis Technol Dept, Bloomington, IN 47405 USA 4.Univ Chinese Acad Sci, Beijing, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liang, Yunji,Li, Huihui,Guo, Bin,et al. Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification[J]. INFORMATION SCIENCES,2021,548:295-312.
APA	Liang, Yunji.,Li, Huihui.,Guo, Bin.,Yu, Zhiwen.,Zheng, Xiaolong.,...&Zeng, Daniel D..(2021).Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification.INFORMATION SCIENCES,548,295-312.
MLA	Liang, Yunji,et al."Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification".INFORMATION SCIENCES 548(2021):295-312.