Exploring the Use of Text Classification in the Legal Domain

In this paper, we investigate the application of text classi€cation methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the inƒuence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classi€ers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling. ACM Reference format: Octavia-Maria Şulea, Marcos Zampieri, Shervin Malmasi, Mihaela Vela, Liviu P. Dinu, Josef van Genabith. 2016. Exploring the Use of Text Classi€cation in the Legal Domain. In Proceedings of 2nd Workshop on Automated Semantic Analysis of Information in Legal Texts, London, United Kingdom, June 2017 (ASAIL’2017), 5 pages.