Abstract Case-based reasoning systems have shown great protnise for legal argumentation, but their development and wider availability are still slowed by the cost of manually representing cases. In this paper, we present our recent progress toward automatically indexing legal opinion texts for a CBR system. Our system SMILE uses a classijication-based approach tojnd abstract fact situations in legal texts. To reduce the cotnple.rity irzherent in legal texts, we take the individud sentences from a marked-up collection of case sumtwries as examples. We illustrate how integrating a legal thesaurus a& linguistic information with a machine learning algorithm can help to overcome the diSJiculties creuted by legal language. The paper discusses results from a preliminary experiment with a decision tree learning algorithm. Experiments indicate that learning on the basis of sentences, rather than full documents, is effective. They also confirm that adding a legal thesaurus to the learning algorithm leads to improved pet$ormance for some, but not all. indexing concepts
