Word-concept clusters in a legal document collection

  • T. D. Gedeon
  • R. A. Bustos
  • B. J. Briedis
  • G. Greenleaf
  • A. Mowbray
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1226)


For very large document collections or high volume streams of documents such as information resources on the web, finding relevant documents is a major information filtering problem. Traditional full text retrieval methods can not locate documents which use specialised synonyms or related concepts to the formal query. This is particularly a problem in legal document collections, since lawyers use normal words with specialised meanings which vary subtly between legal sub-domains. We use a neural network approach to learn synonyms and related clusters of words defining similar concepts from a sample document set. We demonstrate that our clusters of words are qualitatively useful, in the legal domain in particular, and can thus be used for high throughput information filtering to find documents likely to contain concepts relevant to a user's information need.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • T. D. Gedeon
    • 1
  • R. A. Bustos
    • 1
  • B. J. Briedis
    • 1
  • G. Greenleaf
    • 2
  • A. Mowbray
    • 3
  1. 1.Department of Information Engineering School of Computer Science EngineeringThe University of New South WalesSydneyAustralia
  2. 2.School of LawThe University of New South WalesAustralia
  3. 3.School of LawUniversity of TechnologySydney

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