Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations

被引：0

作者：

Liu, Qian ^{[1
,2
]}

Liu, Bing ^{[3
]}

Zhang, Yuanlin ^{[4
]}

Kim, Doo Soon ^{[5
]}

Gao, Zhiqiang ^{[1
,2
]}

机构：

[1] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Jiangsu, Peoples R China

[2] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China

[3] Univ Illinois, Dept Comp Sci, Chicago, IL USA

[4] Texas Tech Univ, Dept Comp Sci, Lubbock, TX 79409 USA

[5] Bosch Res Lab, Sunnyvale, CA USA

来源：

THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Aspect extraction is a key task of fine-grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.

引用

页码：2986 / 2992

页数：7

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