We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized 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.

This article is part of the theme issue ‘A complexity science approach to law and governance’.

我们提出并评估了一种自动化管道，用于通过惩罚回归和选择后显着性测试传递与主题模型合成的特征，从法律决策文本中发现重要主题。该方法识别与结果显着相关的案例主题、可以手动解释以获取有关重要主题的见解的主题词分布以及可用于识别每个主题的代表性案例的案例主题权重。我们在新的域名争议数据集和欧洲人权法院侵权案件的规范数据集上演示了该方法。评估基于潜在语义分析和语言模型嵌入的主题模型。我们表明，管道衍生的主题与这两个领域的法律原则一致，并且可用于其他相关的法律分析任务。

本文是主题“法律和治理的复杂性科学方法”的一部分。