• DocumentCode
    2452293
  • Title

    Syntactic structure feature analysis and classification method research

  • Author

    Zhan, Wenlian ; Shen, Zhulin

  • Author_Institution
    Sch. of Foreign Languages, Hunan Int. Econ. Univ., Changsha, China
  • fYear
    2012
  • fDate
    16-18 July 2012
  • Firstpage
    1135
  • Lastpage
    1140
  • Abstract
    Characteristics and difficulties of Chinese natural language processing, as well as the appropriate language to deal with the basic resources relative lack of some mature foreign technology and research results can not be applied directly to the Chinese question answering system. To this end, the question answering system for Chinese facts, presents a new extraction algorithm based on the characteristics analysis of syntactic structure and classification of the answer, the answer extraction problem as a classification of the candidate answers, upcoming candidate answer classified as correct and error categories. First, the method according to the types of problems corresponding to the type of candidate answers extracted from the text fragments candidate answer in the sentence simple features and syntactic structure characteristics; then take advantage of these features of the training classifier; trained classifier to distinguish the candidate answer for the correct answer.
  • Keywords
    natural language processing; pattern classification; text analysis; Chinese natural language processing; Chinese question answering system; extraction algorithm; syntactic structure feature analysis method research; syntactic structure feature classification method research; text fragments; Classification algorithms; Feature extraction; Semantics; Speech; Standards; Syntactics; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-0173-2
  • Type

    conf

  • DOI
    10.1109/ICALIP.2012.6376787
  • Filename
    6376787