Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification
Liang, Yunji1; Li, Huihui1; Guo, Bin1; Yu, Zhiwen1; Zheng, Xiaolong1,2,4; Samtani, Sagar3; Zeng, Daniel D.2,4
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
2021-02-16
Volume548Pages:295-312
Corresponding AuthorLiang, Yunji(liangyunji@nwpu.edu.cn) ; Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
AbstractThe 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.
KeywordView attention Spatial attention Multi-view representation Series and parallel connection Conventional neural network Text classification
DOI10.1016/j.ins.2020.10.021
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000596057300017
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42818
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorLiang, Yunji; Zheng, Xiaolong
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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