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A Method of Extracting Related Words Using Standardized Mutual Information

  • Tomohiko Sugimachi
  • Akira Ishino
  • Masayuki Takeda
  • Fumihiro Matsuo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

Techniques of automatic extraction of related words are of great importance in many applications such as query expansion and automatic thesaurus construction. In this paper, a method of extracting related words is proposed basing on the statistical information about the co-occurrences of words from huge corpora. The mutual information is one of such statistical measures and has been used for application mainly in natural language processing. A drawback is, however, the mutual information depends mainly on frequencies of words. To overcome this difficulty, we propose as a new measure a normalize deviation of mutual information. We also reveal a correspondence between word ambiguity and related words using word relation graphs constructed using this measure.

Keywords

Mutual Information Word Frequency Natural Language Processing Related Word Query Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tomohiko Sugimachi
    • 1
  • Akira Ishino
    • 1
  • Masayuki Takeda
    • 1
  • Fumihiro Matsuo
    • 1
  1. 1.Department of InformaticsKyushu UniversityFukuokaJAPAN

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