• 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