Knowledge Acquisition for Categorization of Legal Case Reports

  • Filippo Galgani
  • Paul Compton
  • Achim Hoffmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7457)


Natural language processing in complex domains, such as law, requires elaborate features, and their interaction is often difficult to model: thus traditional machine learning approaches might fail to perform satisfactorily. This paper describes our approach to assign categories and generate catchphrases for legal case reports. We describe our knowledge acquisition framework which lets us quickly build classification rules, using a small number of features, to assign general labels to cases. We show how the resulting knowledge base outperforms machine learning models which use both the designed features or a traditional bag of word representation. We also describe how to extend this approach to extract from the full text a list of more specific catchphrases that describe the content of the case.


Machine Learning Knowledge Acquisition Machine Translation Legal Text Word Representation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashley, K.D., Brüninghaus, S.: Automatically classifying case texts and predicting outcomes. Artif. Intell. Law 17(2), 125–165 (2009)CrossRefGoogle Scholar
  2. 2.
    Compton, P., Jansen, R.: Knowledge in context: a strategy for expert system maintenance. In: AI 1988: Proceedings of the Second Australian Joint Conference on Artificial Intelligence, pp. 292–306. Springer, Adelaide (1990)Google Scholar
  3. 3.
    Farzindar, A., Lapalme, G.: Letsum, an automatic legal text summarizing system. In: The Seventeenth Annual Conference on Legal Knowledge and Information Systems, JURIX 2004, p. 11. Ios Pr. Inc. (2004)Google Scholar
  4. 4.
    Farzindar, A., Lapalme, G.: Machine Translation of Legal Information and Its Evaluation. In: Gao, Y., Japkowicz, N. (eds.) AI 2009. LNCS, vol. 5549, pp. 64–73. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. J. Intell. Inf. Syst. 5, 211–228 (1995)CrossRefGoogle Scholar
  6. 6.
    Galgani, F., Compton, P., Hoffmann, A.: Combining different summarization techniques for legal text. In: Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, pp. 115–123. Association for Computational Linguistics, Avignon (2012)Google Scholar
  7. 7.
    Galgani, F., Compton, P., Hoffmann, A.: Towards Automatic Generation of Catchphrases for Legal Case Reports. In: Gelbukh, A. (ed.) CICLing 2012, Part II. LNCS, vol. 7182, pp. 414–425. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Galgani, F., Hoffmann, A.: LEXA: Towards Automatic Legal Citation Classification. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 445–454. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Gonçalves, T., Quaresma, P.: Is linguistic information relevant for the text legal classification problem? In: ICAIL 2005, pp. 168–176 (2005)Google Scholar
  10. 10.
    Greenleaf, G., Mowbray, A., King, G., Van Dijk, P.: Public Access to Law via Internet: The Australian Legal Information Institute. Journal of Law and Information Science 6 49 (1995)Google Scholar
  11. 11.
    Hachey, B., Grover, C.: Extractive summarisation of legal texts. Artif. Intell. Law 14(4), 305–345 (2006)CrossRefGoogle Scholar
  12. 12.
    Kim, M.H., Compton, P., Kim, Y.S.: Rdr-based open ie for the web document. In: Proceedings of the Sixth International Conference on Knowledge Capture, K-CAP 2011, pp. 105–112. ACM, New York (2011)CrossRefGoogle Scholar
  13. 13.
    Krzywicki, A., Wobcke, W.: Incremental E-Mail Classification and Rule Suggestion Using Simple Term Statistics. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 250–259. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81. Association for Computational Linguistics, Barcelona (2004)Google Scholar
  15. 15.
    de Maat, E., Krabben, K., Winkels, R.: Machine learning versus knowledge based classification of legal texts. In: Proceedings of the 2010 Conference on Legal Knowledge and Information Systems, pp. 87–96. IOS Press, Amsterdam (2010)Google Scholar
  16. 16.
    Moens, M.F.: Summarizing court decisions. Inf. Process. Manage. 43(6), 1748–1764 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  18. 18.
    Thompson, P.: Automatic categorization of case law. In: ICAIL 2001: Proceedings of the 8th International Conference on Artificial Intelligence and Law, pp. 70–77. ACM, New York (2001)CrossRefGoogle Scholar
  19. 19.
    Xu, H., Hoffmann, A.: RDRCE: Combining Machine Learning and Knowledge Acquisition. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS, vol. 6232, pp. 165–179. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Zhang, P., Koppaka, L.: Semantics-based legal citation network. In: ICAIL 2007: Proceedings of the 11th International Conference on Artificial Intelligence and Law, pp. 123–130. ACM Press, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Filippo Galgani
    • 1
  • Paul Compton
    • 1
  • Achim Hoffmann
    • 1
  1. 1.School of Computer Science and EngineeringThe University of New South WalesSydneyAustralia

Personalised recommendations