In common law jurisdictions, legal research often involves an analysis of relevant case law. Court opinions comprise several high-level parts with different functions. A statement's membership in one of the parts is a key factor influencing how the statement should be understood. In this paper we present a number of experiments in automatically segmenting court opinions into the functional and the issue specific parts. We defined a set of seven types including Background, Analysis, and Conclusions. We used the types to annotate a sizable corpus of US trade secret and cyber crime decisions. We used the data set to investigate the feasibility of recognizing the parts automatically. The proposed framework based on conditional random fields proved to be very promising in this respect. To support research in automatic case law analysis we plan to release the data set to the public.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org