Exploring the Use of Text Classification in the Legal Domain
This work addresses the need for automated legal analysis tools for law professionals, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of applying text classification to legal documents, achieving high accuracy in predicting French Supreme Court rulings (98% F1 score), law areas (96% F1 score), and ruling dates (87.07% F1 score).
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.