Argument Based Machine Learning Applied to Law

In this paper we discuss the application of a new machine learning approach - Argument Based Machine Learning - to the legal domain. An experiment using a dataset which has also been used in previous experiments with other learning techniques is described, and comparison with previous experiments made. We also tested this method for its robustness to noise in learning data. Argumentation based machine learning is particularly suited to the legal domain as it makes use of the justifications of decisions which are available. Importantly, where a large number of decided cases are available, it provides a way of identifying which need to be considered. Using this technique, only decisions which will have an influence on the rules being learned are examined.

• Type: Journal
• Year: 2005
• Where: AIL
• Authors: Martin Mozina, Jure Zabkar, Trevor J. M. Bench-Capon, Ivan Bratko
• Keywords: AIL 2005, Legal Domain, Machine Learning, Paper