Publikation
Exploring the Use of Text Classification in the Legal Domain.
Octavia-Maria Şulea; Marcos Zampieri; Shervin Malmasi; Mihaela Vela; Liviu P. Dinu; Josef van Genabith
In: Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Text. Automated Semantic Analysis of Information in Legal Text (ASAIL-2017), Workshop on Automated Semantic Analysis of Information in Legal Text, located at ICAIL 2017: XVI International Conference on AI and Law, June 16, London, United Kingdom, http://ceur-ws.org, 10/2017.
Zusammenfassung
In this paper, we investigate the application of text classification 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 influence 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 classifiers. 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.