Approaches to text mining arguments from legal cases

Authors:
Adam Wyner;Raquel Mochales-Palau;Marie-Francine Moens;David Milward
Affiliations:
Department of Computer Science, University College London, London, United Kingdom;Computer Science Department, Katholieke Universiteit Leuven, Heverlee, Belgium;Computer Science Department, Katholieke Universiteit Leuven, Heverlee, Belgium;Linguamatics Ltd, St. John’s Innovation Centre, Cambridge, United Kingdom
Venue:
Semantic Processing of Legal Texts
Year:
2010

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Abstract

This paper describes recent approaches using text-mining to automatically profile and extract arguments from legal cases. We outline some of the background context and motivations. We then turn to consider issues related to the construction and composition of corpora of legal cases. We show how a Context-Free Grammar can be used to extract arguments, and how ontologies and Natural Language Processing can identify complex information such as case factors and participant roles. Together the results bring us closer to automatic identification of legal arguments.