Computer-Assisted Classification of Legal Abstracts

  • Bokyung Yang-Stephens
  • M. Charles Swope
  • Jeffrey Locke
  • Isabelle Moulinier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


This paper describes a Memory-Based Reasoning application that generates candidate classifications to aid editors in allocating abstracts of judicial opinions among the 82,000 classes of a legal classi fication scheme. Using a training collection of more than 20 million previously classified abstracts, the application provides ranked lists of candidate classifications for new abstracts. These lists proved to contain highly relevant classes and integrating this application into the editorial environment should materially improve the efficiency of the work of classifying the new abstracts.


Similarity Score Near Neighbor Test Instance Correct Assignment Test Collection 
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 1999

Authors and Affiliations

  • Bokyung Yang-Stephens
    • 1
  • M. Charles Swope
    • 1
  • Jeffrey Locke
    • 1
  • Isabelle Moulinier
    • 1
  1. 1.West GroupEaganUSA

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