Incorporating explicit syntactic dependency for aspect level sentiment classification

doi:10.1016/j.neucom.2021.05.078

	Incorporating explicit syntactic dependency for aspect level sentiment classification
	Ke, Wenjun 1,2; Gao, Jinhua 1; Shen, Huawei 1,2; Cheng, Xueqi 1
	2021-10-07
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	456 页码:394-406
摘要	Aspect level sentiment classification aims to extract fine-grained sentiment expressed towards specific aspects from a sentence. The key to this task lies in connecting aspects and their respective sentiment contexts. Existing methods measure the dependency weights between aspects and context words via either the semantic similarity between words captured by attention mechanism or the structural proximity between words in syntactic structures. However, methods in both groups fail to fully exploit explicit syntactic dependency, which we argue should be critical to identify sentiment contexts. In this paper, we propose a novel syntactic-dependency-based attention network (SDATT) to incorporate explicit syntactic dependency for aspect level sentiment classification. SDATT first models the dependency path between each word and the aspect to characterize aspect-oriented syntactic representation of each word. The generated syntactic representations are later fed into the attention layer to help infer the dependency weights for sentiment prediction. Experimental results on five benchmark datasets show the superior performance of the proposed model over state-of-the-art baselines. (c) 2021 Elsevier B.V. All rights reserved.
关键词	Sentiment classification Syntactic dependency Attention network
DOI	10.1016/j.neucom.2021.05.078
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[62002347] ; Natural Science Foundation of China[91746301] ; Beijing Academy of Artificial Intelligence (BAAI) ; K.C. Wong Education Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000684998100017
出版者	ELSEVIER
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17237
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gao, Jinhua
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Ke, Wenjun,Gao, Jinhua,Shen, Huawei,et al. Incorporating explicit syntactic dependency for aspect level sentiment classification[J]. NEUROCOMPUTING,2021,456:394-406.
APA	Ke, Wenjun,Gao, Jinhua,Shen, Huawei,&Cheng, Xueqi.(2021).Incorporating explicit syntactic dependency for aspect level sentiment classification.NEUROCOMPUTING,456,394-406.
MLA	Ke, Wenjun,et al."Incorporating explicit syntactic dependency for aspect level sentiment classification".NEUROCOMPUTING 456(2021):394-406.