A major problem facing online information services is how to index and supplement large document collections with respect to a rich set of categories. We focus upon the routing of case law summaries to various secondary law volumes in which they should be cited. Given the large number (> 13,000) of closely related categories, this is a challenging task that is unlikely to succumb to a single algorithmic solution. Our fully implemented and recently deployed system shows that a superior classification engine for this task can be constructed from a combination of classifiers. The multi-classifier approach helps us leverage all the relevant textual features and meta data, and appears to generalize to related classification tasks.

We describe a system that processes court opinions and retrieves related cases from a citator database, so that new cases can be linked to earlier ones that they impact. The design of the system combines information extraction, information retrieval and machine learning techniques in a novel way. The fully implemented program is capable of performing prior case retrieval at human levels of recall and acceptable levels of precision.