用可解释的NLP方法从4万+判例中自动识别住房与驱逐案件
An explainable approach to detect case law on housing and eviction issues within the HUDOC database
- 基于关键词和可解释模型识别住房相关判例
- 在4万+案例库中实现高精度分类且可解释决策依据
- 适合法律科技研究者及人权领域数据挖掘者
判例对理解人权，包括适当住房权具有关键作用。HUDOC数据库提供了欧洲人权法院（ECtHR）超过40,000个案件的文本内容及部分元数据，但这些元数据通常缺乏具体案件所涉问题的详细信息。因此，亟需深入分析以提取实质性内容。鉴于数据库规模庞大，自动化解决方案必不可少。本研究聚焦于适当住房权，构建模型用于检测与住房及驱逐相关的案件。实验表明，所提模型不仅性能媲美更复杂方法，还具备可解释性，能通过突出关键词汇说明判断依据。该方法成功识别出此前数据收集阶段被遗漏的新案件，表明NLP可有效按具体议题对判例进行分类。
原文摘要 · Abstract (English)
Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.
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