• DocumentCode
    1578367
  • Title

    Classification of characteristic words of electronic newspaper based on the directed relation

  • Author

    Nakashima, Takuo

  • Author_Institution
    Fac. of Eng., Kumamoto Univ., Japan
  • Volume
    2
  • fYear
    2001
  • fDate
    6/23/1905 12:00:00 AM
  • Firstpage
    591
  • Abstract
    Newspaper articles are gradually opened to Web system. But the categories of articles are remained in conventional category types. So users of these system have to consider the appropriate searching keywords and categories when they search some articles. In this paper, we propose the new classification method for the characteristic words based on directed relation. We think this classification can change categories of articles and accessing keywords of users. First, we introduce the word vector and directed relation degree. Second, we define the classification method, reference degree and abstraction method of cluster. Finally, we experimented using one month data and can get the following results. (1)Both directional tightly connected words are suitable base words of clusters of articles. (2)Level-0 abstraction method can draw the rough border of clusters. (3)One directional tightly connected words represent the main general words or proper words
  • Keywords
    classification; information retrieval; text analysis; abstraction method; characteristic words classification; classification method; directed relation; directed relation degree; electronic newspaper; searching categories; searching keywords; word vector; Clustering algorithms; Computer science; Data mining; Dictionaries; Frequency; Internet; Large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Computers and signal Processing, 2001. PACRIM. 2001 IEEE Pacific Rim Conference on
  • Conference_Location
    Victoria, BC
  • Print_ISBN
    0-7803-7080-5
  • Type

    conf

  • DOI
    10.1109/PACRIM.2001.953702
  • Filename
    953702