通过选择性推理从法律判决中发现重要主题
Discovering Significant Topics from Legal Decisions with Selective Inference
AI总结：
本文提出一种从法律判决中发现重要主题的自动化流程，通过主题模型、惩罚回归和选择后显著性检验识别与结果显著相关的主题，并在域名纠纷和欧洲人权法院数据集上验证了其与法律学说的一致性及实用性。
摘要：
我们提出并评估了一个自动化流程，用于从法律判决文本中发现重要主题。该流程将主题模型合成的特征通过惩罚回归和选择后显著性检验进行处理。该方法能识别与判决结果显著相关的案件主题、可人工解读以获取重要主题洞见的主题-词分布，以及可用于识别每个主题代表性案件的主题-案件权重。我们在一个新的域名纠纷数据集和一个经典的欧洲人权法院违规案件数据集上演示了该方法。评估了基于潜在语义分析以及语言模型嵌入的主题模型。我们表明，该流程得出的主题与这两个领域的法律学说一致，且在其他相关法律分析任务中也具有实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
