Reasoning with textual cases

Abstract

This paper presents methods that support automatically finding abstract indexing concepts in textual cases and demonstrates how these cases can be used in an interpretive CBR system to carry out case-based argumentation and prediction from text cases. We implemented and evaluated these methods in SMILE+IBP, which predicts the outcome of legal cases given a textual summary. Our approach uses classification-based methods for assigning indices. In our experiments, we compare different methods for representing text cases, and also consider multiple learning algorithms. The evaluation shows that a text representation that combines some background knowledge and NLP combined with a nearest neighbor algorithm leads to the best performance for our TCBR task. © Springer-Verlag Berlin Heidelberg 2005.