We propose and evaluate an automated pipeline for discovering significant
topics from legal decision texts by passing features synthesized with topic
models through penalised regressions and post-selection significance tests. The
method identifies case topics significantly correlated with outcomes,
topic-word distributions which can be manually-interpreted to gain insights
about significant topics, and case-topic weights which can be used to identify
representative cases for each topic. We demonstrate the method on a new dataset
of domain name disputes and a canonical dataset of European Court of Human
Rights violation cases. Topic models based on latent semantic analysis as well
as language model embeddings are evaluated. We show that topics derived by the
pipeline are consistent with legal doctrines in both areas and can be useful in
other related legal analysis tasks.

通过自动化流程从法律决策文本中发现相关主题，方法包括使用主题模型生成特征，并通过惩罚回归和后选择显著性检验进行评估；实验证明该方法在域名争议和欧洲人权法院侵权案例数据集上识别出与结果显著相关的案例主题、可以手动解释的主题 - 词分布以及可以用于找出每个主题的代表性案例的案例 - 主题权重；该方法产生的主题与两个领域的法律原则一致，可在其他相关法律分析任务中发挥作用。