Automatic recognition of distinguishing negative indirect history language in judicial opinions

We describe a model-based filtering application that generates candidate case-to-case distinguishing citations. We developed the system to aid editors in identifying indirect relationships among judicial opinions in a database of over 5 million documents. Using a training collection of approximately 30,000 previously edited cases, the filter application provides ranked sets of textual evidence for current case law documents in the editorial process. These sets contain judicial language with a strong probability of containing distinguishing relationships. Integrating this application into the editorial review environment has greatly improved the coverage and efficiency of the work flow to identify and generate new distinguishing relationship entries.

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