DocumentCode
467639
Title
Effective Entity Resolution in Product Review Domain
Author
Liu, Jing-Jing ; Cao, Yun-Bo ; Huang, Ya-lou
Author_Institution
Nankai Univ., Tianjin
Volume
1
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
106
Lastpage
111
Abstract
This paper is concerned with the problem of entity resolution in the product review domain. Specifically, given many references to product features, we would like to classify references related to one feature into a group. The product feature resolution is important to product review study, such as review ranking. To solve the problem, we propose an approach which combines two types of similarity characteristics: edit distance and context similarity. Experimental results indicate that the proposed approach resolves product features effectively and improves the performance of review ranking significantly.
Keywords
electronic commerce; retail data processing; context similarity characteristics; edit distance similarity characteristics; entity resolution; online shopping; product review domain; Batteries; Cybernetics; Data mining; Digital cameras; Feature extraction; Focusing; Learning systems; Machine learning; Ontologies; Power supplies; Context similarity; Edit distance; Entity resolution; Product feature; Review ranking;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370124
Filename
4370124
Link To Document