Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. W. Aha, D. Kibler, and M. K. Albert. Instance-based learning algorithms. Machine Learning, (6):37–66, 1991.Google Scholar
  2. 2.
    J. P. Callan, W. B. Croft, and S. M. Harding. The inquery retrieval system. In Proceedings of the Third International Conference on Database and Expert Systems Applications, pages 78–83, Valencia, Spain, 1992. Springer-Verlag.Google Scholar
  3. 3.
    W. Cohen and Y. Singer. Context-sensitive learning methods for text categorization. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zürich, Switzerland, 1996.Google Scholar
  4. 4.
    W. Cohen and H. Hirsh. Joins that generalize: Text Classification using whirl. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pages 169–173, New York City, New York, 1998. AAAI Press.Google Scholar
  5. 5.
    R. H. Creecy, B. M. Masand, S. J. Smith, and D. L. Waltz. Trading MIPS and memory for knowledge engineering: Classifying census returns on the connection machine. Communication of the ACM, (35):45–63, July 1992.Google Scholar
  6. 6.
    B. V. Dasrathy,editor. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, 1990.Google Scholar
  7. 7.
    P. Hayes and S. Weinstein. CONSTRUE/TIS: a system for content-based indexing of a database of news stories. In Second Annual Conference on Innovative Applications of Artificial Intelligence, 1990.Google Scholar
  8. 8.
    L. S. Larkey and W. B. Croft. Combining Classifiers in text categorization. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zürich, 1996.Google Scholar
  9. 9.
    D. D. Lewis. Feature selection and feature extraction for text categorization. In Proceedings of Speech and Natural Language Workshop, Arden House, 1992.Google Scholar
  10. 10.
    B. Masand, G. Linoff, and D. Waltz. Classifying News Stories using Memory Based Reasoning. Copenhagen, 1992.Google Scholar
  11. 11.
    M. F. Porter. An algorithm for suffix stripping. Program, 14(3):130–137, July 1980.Google Scholar
  12. 12.
    G. Salton. Automatic Text Processing: the Transformation, Analysis and Retrieval of Information by Computer. Addison Wesley, 1989.Google Scholar
  13. 13.
    C. Standfill and D. Waltz. Toward memory-based reasoning. Communications of the ACM, 29(12):1213–1228, December 1986.CrossRefGoogle Scholar
  14. 14.
    H. Turtle. Inference Networks for Document Retrieval. PhD thesis, Computer and Information Science Department, University of Massachussetts, October 1990.Google Scholar
  15. 15.
    Y. Yang. Expert network: Effective and Efficient learning from human decisions in text categorization and retrieval. In Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1994.Google Scholar

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

Personalised recommendations