• DocumentCode
    2349157
  • Title

    Negation disambiguation using the maximum entropy model

  • Author

    Zhang, Chunliang ; Fei, Xiaoxu ; Zhu, Jingbo

  • Author_Institution
    Natural Language Lab., Northeastern Univ., Shenyang, China
  • fYear
    2010
  • fDate
    21-23 Aug. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Handling negation issue is of great significance for sentiment analysis. Most previous studies adopted a simple heuristic rule for sentiment negation disambiguation within a fixed context window. In this paper we present a supervised method to disambiguate which sentiment word is attached to the negator such as “(not)” in an opinionated sentence. Experimental results show that our method can achieve better performance than traditional methods.
  • Keywords
    learning (artificial intelligence); maximum entropy methods; natural language processing; fixed context window; maximum entropy model; sentiment analysis; sentiment negation disambiguation; supervised learning method; Classification algorithms; Entropy; Gold; Machine learning; Manuals; Natural languages; Pragmatics; Negator; relation pair; sentiment negation disambiguation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Language Processing and Knowledge Engineering (NLP-KE), 2010 International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-6896-6
  • Type

    conf

  • DOI
    10.1109/NLPKE.2010.5587857
  • Filename
    5587857