A decade of legal argumentation mining: datasets and approaches

Zhang, G. and Nulty, Paul and Lillis, D. (2022) A decade of legal argumentation mining: datasets and approaches. In: Rosso, P. and Basile, V. and Martínez, R. and Métais, E. and Meziane, F. (eds.) Natural Language Processing and Information Systems - 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022, Valencia, Spain, June 15–17, 2022, Proceedings. Lecture Notes in Computer Science 13286. Springer, pp. 240-252. ISBN 9783031084720.

Abstract

The growing research field of argumentation mining (AM) in the past ten years has made it a popular topic in Natural Language Processing. However, there are still limited studies focusing on AM in the context of legal text (Legal AM), despite the fact that legal text analysis more generally has received much attention as an interdisciplinary field of traditional humanities and data science. The goal of this work is to provide a critical data-driven analysis of the current situation in Legal AM. After outlining the background of this topic, we explore the availability of annotated datasets and the mechanisms by which these are created. This includes a discussion of how arguments and their relationships can be modelled, as well as a number of different approaches to divide the overall Legal AM task into constituent sub-tasks. Finally we review the dominant approaches that have been applied to this task in the past decade, and outline some future directions for Legal AM research.

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