• DocumentCode
    3048418
  • Title

    Study on key technology of topic tracking based on VSM

  • Author

    Li, Shengdong ; Lv, Xueqiang ; Zhou, Qiang ; Shi, Shuicai

  • Author_Institution
    Chinese Inf. Process. Res. Center, Beijing Inf. Sci. & Technol. Univ., Beijing, China
  • fYear
    2010
  • fDate
    20-23 June 2010
  • Firstpage
    2419
  • Lastpage
    2423
  • Abstract
    Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective models for topics representation. On the basis of 2 information gain algorithm and chi square ιY in VSM, we have studied how feature selection algorithm and feature dimension in VSM affect topic tracking. And then we get the variation law that they affect topic tracking, and add up their optimal values in topic tracking. Finally, TDT evaluation method proves that their optimal values can make topic tracking gain very good tracking performance. In addition, we also prove in 2 the experiment that chi square ιY in VSM has better performance for topic tracking than information gain algorithm.
  • Keywords
    learning (artificial intelligence); pattern classification; text analysis; VSM; feature dimension; feature selection algorithm; information gain algorithm; key technology study; text classification; topic representation model; topic tracking; vector space model; Automation; Classification algorithms; Information processing; Information science; Multimedia databases; Performance gain; Prototypes; Space technology; Testing; Text categorization; KNN; TDT Evaluation; Topic Tracking; VSM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation (ICIA), 2010 IEEE International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4244-5701-4
  • Type

    conf

  • DOI
    10.1109/ICINFA.2010.5512284
  • Filename
    5512284