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GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification

文献类型：期刊论文

作者Zhu, Xiaofei1; Zhu, Ling1; Guo, Jiafeng2; Liang, Shangsong3; Dietze, Stefan4,5
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2021-12-30
卷号186页码:11
关键词Graph convolutional networks Aspect-based sentiment classification Attention mechanism Sentiment analysis
ISSN号0957-4174
DOI10.1016/j.eswa.2021.115712
英文摘要Aspect-based sentiment classification, which aims at identifying the sentiment polarity of a sentence towards the specified aspect, has become a crucial task for sentiment analysis. Existing methods have proposed effective models and achieved satisfactory results, but they mainly focus on exploiting local structure information of a given sentence, such as locality, sequentiality or syntactical dependency constraints within the sentence. Recently, some research works, which utilizes global dependency information, has attracted increasing interest and significantly boosts the performance of text classification. In this paper, we simultaneously introduce both global structure information and local structure information into the task of aspect-based sentiment classification, and propose a novel aspect-based sentiment classification approach, i.e., Global and Local Dependency Guided Graph Convolutional Networks (GL-GCN). In particular, we exploit the syntactic dependency structure as well as sentence sequential information (e.g., the output of BiLSTM) to mine the local structure information of a sentence. On the other hand, we construct a word-document graph using the entire corpus to reveal the global dependency information between words. In addition, an attention mechanism is leveraged to effectively fuse both global and local dependency structure signals. Extensive experiments are conducted on five benchmark datasets in terms of both Accuracy and F1-Score, and the results illustrate that our proposed framework outperforms state-of-the-art methods for aspect-based sentiment classification. The model is implemented using PyTorch and is trained on GPU GeForce GTX 2080 Ti.
资助项目National Natural Science Foundation of China[61722211] ; Federal Ministry of Education and Research, Germany[01LE1806A] ; Beijing Academy of Artificial Intelligence, China[BAAI2019ZD0306] ; Technology Innovation and Application Development of Chongqing, China[cstc2020jscx-dxwtBX 0014]
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
WOS记录号WOS:000704349700009
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/16971]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhu, Xiaofei
作者单位1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China
4.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany
5.Heinrich Heine Univ Dusseldorf, Inst Comp Sci, D-40225 Dusseldorf, Germany
推荐引用方式
GB/T 7714		 Zhu, Xiaofei,Zhu, Ling,Guo, Jiafeng,et al. GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,186:11.
APA Zhu, Xiaofei,Zhu, Ling,Guo, Jiafeng,Liang, Shangsong,&Dietze, Stefan.(2021).GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification.EXPERT SYSTEMS WITH APPLICATIONS,186,11.
MLA Zhu, Xiaofei,et al."GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification".EXPERT SYSTEMS WITH APPLICATIONS 186(2021):11.

入库方式： OAI收割

来源：计算技术研究所

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