Advertisement

Toward a Comprehensive Legal Information Retrieval System

  • Daphne Gelbart
  • J. C. Smith

Abstract

The construction of comprehensive legal information systems require at least three different kinds of mechanisms of information retrieval and knowledge representation, ranging from domain-specific and knowledge-based to fully automatic and general purpose. This paper describes how different information needs are satisfied by different architectures, and in particular how the FLEXICON System[1] being developed by the FLAIR team can replace Boolean word searches with a system of information retrieval using weighted conceptual profiles to search automatically indexed databases.

Keywords

Information Retrieval Expert System Relevance Feedback Information Retrieval System Inverted Index 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Copyright 1989, Daphne Gelbart and J.C. Smith.Google Scholar
  2. [2]
    Smith, J.C. and C. Deedman. The Application of Expert Systems Technology to Case-Based Reasoning. Proc. 1st International Conference on Artificial Intelligence and Law (Boston) A.C.M. Press, New York, 1987, p. 84.Google Scholar
  3. [3]
    MacCrimmon, Marilyn T. Expert Systems in Case-Based Law: The Hearsay Rule Advisor. Proc. 2nd International Conference on Artificial Intelligence and Law (Vancouver) A.C.M. Press, New York, 1989Google Scholar
  4. [4]
    Rissland, Edwina L. and Kevin D. Ashley. A Case-Based System for Trade Secrets Law. Proc. 1st International Conference on Artificial Intelligence and Law (Boston) A.C.M. Press, New York, 1987, p. 60.Google Scholar
  5. [5]
    Ashley, Kevin D. Modelling Legal Arguments: Reasoning with Cases and Hvpotheticals. MIT Press, Cambridge, Mass., 1990 (in press).Google Scholar
  6. [6]
    Susskind, Richard E. Expert Systems in Law. Oxford University Press, Oxford, 1987.Google Scholar
  7. [7]
    Belew, Richard K. A Connectionist Approach to Conceptual Information Retrieval. Proc. 1st International Conference on Artificial Intelligence and Law (Boston) A.C.M. Press, New York, 1987, p. 116.Google Scholar
  8. [8]
    Rose, Daniel E. and Richard K. Belew. Legal Information Retrieval: A Hybrid Approach. Proc. 2nd International Conference on Artificial Intelligence and Law (Vancouver) A.C.M. Press, New York, 1989, p. 138.Google Scholar
  9. [9]
    Salton, G. and M.J. McGill. Introduction to Modern Information Retrieval McGraw-Hill, 1983.Google Scholar
  10. [10]
    Salton, G. Automatic Text Processing Addison-Wesley, 1989.Google Scholar
  11. [11]
    Salton, G. and C. Buckley. Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management Vol 24 (1988), p 513.Google Scholar
  12. [12]
    Buckley C. Optimization of Inverted Vector Searches. Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Montreal, 1985.Google Scholar
  13. [13]
    A prime example of a menu-driven legal information system is the Sentencing Database developed by John Hogarth as part of the UBC Faculty of Law-IBM Canada Cooperative Project, now marketed by the LIST Foundation.Google Scholar

Copyright information

© Springer-Verlag/Wien 1990

Authors and Affiliations

  • Daphne Gelbart
    • 1
  • J. C. Smith
    • 1
  1. 1.Faculty of Law Artificial Intelligence Research ProjectUniversity of British ColumbiaVancouverCanada

Personalised recommendations