Progress in Textual Case-Based Reasoning: Predicting the Outcome of Legal Cases from Text

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 21

New Scientific and Technical Advances in Research (Nectar) Papers

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.