Title :
Effective Entity Resolution in Product Review Domain
Author :
Liu, Jing-Jing ; Cao, Yun-Bo ; Huang, Ya-lou
Author_Institution :
Nankai Univ., Tianjin
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;
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
DOI :
10.1109/ICMLC.2007.4370124