• DocumentCode
    1660471
  • Title

    The Field of Automatic Entity Relation Extraction Based on Binary Classifier and Reasoning

  • Author

    Lei, Chun-ya ; Guo, Jian-yi ; Yu, Zheng-tao ; Zhang, Shao-min ; Mao, Cun-li ; Zhang, Chao-shen

  • Author_Institution
    Sch. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China
  • fYear
    2010
  • Firstpage
    327
  • Lastpage
    331
  • Abstract
    To solve the difficulty of the field of Automatic Entity Relation Extraction, in this paper, a method that used binary classification thinking, meanwhile combined with reasoning rules to extract the field of entity relation is proposed. considering comprehensively the context information of entity, entity type and their combination of characteristics to construct the feature set, which in order to build the Binary Classifier of entity relation extraction, then taking full advantage of the field characteristics of entity relation, further combine reasoning rules to obtain the type of the field of entity relation. Doing our experiment on the artificial collection of 600 corpuses for tourism field, experimental result shows the method of Binary Classifier combining Reasoning is better than Multiple Classifiers, the F-score is improved 3%.
  • Keywords
    feature extraction; inference mechanisms; information retrieval; pattern classification; automatic entity relation extraction; binary classifier; feature set; reasoning rules; Classification algorithms; Cognition; Data mining; Entropy; Feature extraction; Snow; Training; binary classifier; features; filed of entity relation; multiple classifier; reasoning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Processing (ISIP), 2010 Third International Symposium on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4244-8627-4
  • Type

    conf

  • DOI
    10.1109/ISIP.2010.40
  • Filename
    5669065