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.

本研究使用文本分类和机器学习方法，研究法律专业人员的支持应用。通过实验研究法国最高法院的判决和案件所属法律领域的高准确度预测，发现判决作出的时间对案例描述的形式和数据蒙版的使用会有影响，研究了多个支持向量机分类器的平均概率集成系统，平均 F1 值为 98％，预测案例判决，96％ F1 值用于预测案件的法律领域，对于估计判决日期的 F1 值为 87.07％。