• DocumentCode
    1585036
  • Title

    Web sites thematic classification using hidden Markov models

  • Author

    Serradura, Lyonel ; Slimane, Mohamed ; Vincent, Nicole

  • Author_Institution
    Lab. d´´Inf., Tours Univ., France
  • fYear
    2001
  • fDate
    6/23/1905 12:00:00 AM
  • Firstpage
    1094
  • Lastpage
    1098
  • Abstract
    There is more and more information available on the Internet. We need tools to help us extract the right piece of information. We have developed a classification algorithm tackling this issue in French. It distinguishes web pages classifying their text content into themes. We use Hidden Markov Models (HMM) to build this method named STCoL (Supervised Thematic Corpus Learning). Once themes are modeled with HMMs, STCoL is able to classify documents from different sources. This method is not only efficient but is also robust
  • Keywords
    Internet; classification; hidden Markov models; information resources; French; Hidden Markov Models; Internet; STCoL; Supervised Thematic Corpus Learning; classification algorithm; text content; thematic classification; web pages; Data mining; Hidden Markov models; Internet; Law; Legal factors; Portals; Robustness; Waste materials; Web pages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7695-1263-1
  • Type

    conf

  • DOI
    10.1109/ICDAR.2001.953955
  • Filename
    953955