• DocumentCode
    690251
  • Title

    An improved method of statistical model for text segmentation

  • Author

    Xiaojin Li ; Aili Han

  • Author_Institution
    Dept. of Comput. Sci., Shandong Univ., Weihai, China
  • fYear
    2013
  • fDate
    15-17 Nov. 2013
  • Firstpage
    282
  • Lastpage
    285
  • Abstract
    Every document contains multiple topics, and the task of text segmentation is to segment a text into several parts that each part represents one topic. On the base of statistical model of Masao Utiyama whose experiment showed that the method was more accurate than or at least as accurate as a state-of-art text segmentation system, this paper proposes an improvement suggestion trying to improve the existing problem. The experiment results showed that the improved algorithm improved both the efficiency and the accuracy.
  • Keywords
    statistical analysis; text analysis; Masao Utiyama; statistical model; text segmentation system; Accuracy; Semantics; artificial intelligence; dynamic programming; text segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics Information and Emergency Communication (ICEIEC), 2013 IEEE 4th International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ICEIEC.2013.6835506
  • Filename
    6835506