In this paper, we investigate the application of text classification methods
to predict the law area and the decision of cases judged by the French Supreme
Court. We also investigate the influence of the time period in which a ruling
was made over the textual form of the case description and the extent to which
it is necessary to mask the judge's motivation for a ruling to emulate a
real-world test scenario. We report results of 96% f1 score in predicting a
case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1
score in estimating the time span when a ruling has been issued using a linear
Support Vector Machine (SVM) classifier trained on lexical features.

本文研究了如何将文本分类方法应用于法国最高法院判决案件的法律领域和判决预测，并调查了判决描述的文本形式在时间段上对判决的影响以及模拟真实世界测试情景需要遮盖法官动机的程度。我们使用基于词汇特征训练的线性支持向量机分类器报告了 96％的案件判决预测 f1 得分，90％的法律领域预测 f1 得分和 75.9％的发布判决时间跨度估计 f1 得分。