Exploring the Use of Text Classification in the Legal Domain

TL;DR

This paper explores applying machine learning-based text classification to legal documents, achieving high accuracy in predicting case rulings, law areas, and ruling dates, thereby supporting legal professionals.

Contribution

It introduces an ensemble SVM approach and analyzes factors like case description masking and temporal influences on classification performance.

Findings

98% F1 score in ruling prediction

96% F1 score in law area classification

87.07% F1 score in ruling date estimation

Abstract

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.