• DocumentCode
    458863
  • Title

    Automatic Entity Relation Extraction Based on Maximum Entropy

  • Author

    Zhang Suxiang ; Wen Juan ; Wang Xiaojie ; Li Lei

  • Author_Institution
    Sch. of Inf. Eng., Beijing Univ. of Posts & Telecommun.
  • Volume
    1
  • fYear
    2006
  • fDate
    16-18 Oct. 2006
  • Firstpage
    540
  • Lastpage
    544
  • Abstract
    Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity relation extraction, which includes morphology, grammar and semantic feature. The system architecture for RE has been constructed. Experiment shows that the performance is promising. So it is useful for ME-based machine learning to solve RE problem
  • Keywords
    grammars; learning (artificial intelligence); maximum entropy methods; natural language processing; text analysis; Chinese language; Chinese texts; automatic entity relation extraction; classification problem; feature selection; grammar; information extraction; machine learning; maximum entropy; morphology; semantic feature; Data mining; Design engineering; Entropy; Kernel; Learning systems; Machine learning; Machine learning algorithms; Natural language processing; Natural languages; Power engineering and energy; Maximum Entropy; entity relation extraction and evaluation; feature selection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    0-7695-2528-8
  • Type

    conf

  • DOI
    10.1109/ISDA.2006.115
  • Filename
    4021496