• DocumentCode
    3376812
  • Title

    A hybrid architecture for text classification

  • Author

    Register, Michael S. ; Kannan, Narasimhan

  • Author_Institution
    Digital Equipment Corp., Colorado Springs, CO, USA
  • fYear
    1992
  • fDate
    10-13 Nov 1992
  • Firstpage
    286
  • Lastpage
    292
  • Abstract
    SKIS, a prototype system that allows for the construction and use of text classification applications, is discussed. SKIS uses a combination of knowledge-based techniques, statistical techniques, morphological processing, and relevance feedback learning techniques to perform text classification. SKIS has been used to construct a prototype text classification application for the routing of customer service requests within customer support centers. The SKIS run-time architecture, the development and knowledge maintenance environment, and how SKIS is used are described. The benefits of combining knowledge-based and statistical techniques for text classification are discussed. SKIS is compared with other text classification systems
  • Keywords
    document handling; knowledge based systems; natural languages; SKIS; customer service requests; customer support centers; knowledge maintenance environment; knowledge-based techniques; morphological processing; relevance feedback learning techniques; run-time architecture; statistical techniques; text classification; Application software; Customer service; Feedback; Natural languages; Prototypes; Routing; Runtime; Springs; Text categorization; Text processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 1992. TAI '92, Proceedings., Fourth International Conference on
  • Conference_Location
    Arlington, VA
  • Print_ISBN
    0-8186-2905-3
  • Type

    conf

  • DOI
    10.1109/TAI.1992.246417
  • Filename
    246417