Progress in textual case-based reasoning: predicting the outcome of legal cases from text

  • Authors:

  • Stefanie Brüninghaus;Kevin D. Ashley

  • Affiliations:

  • Learning Research and Development Center, Intelligent Systems Program, and School of Law, University of Pittsburgh, Pittsburgh, PA;Learning Research and Development Center, Intelligent Systems Program, and School of Law, University of Pittsburgh, Pittsburgh, PA

  • Venue:
  • AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
  • Year:
  • 2006

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Abstract

This paper reports on a project that explored reasoning with textual cases in the context of legal reasoning. The work is anchored in both Case-Based Reasoning (CBR) and AI and Law. It introduces the SMILE+IBP framework that generates a case-based analysis and prediction of the outcome of a legal case given a brief textual summary of the case facts. The focal research question in this work was to find a good text representation for text classification. An evaluation showed that replacing case-specific names by roles and adding NLP lead to higher performance for assigning CBR indices. The NLP-based representation produced the best results for reasoning with the automatically indexed cases.