提出检测韩国民法条文竞争关系的新方法，提升法律起草与适用效率。
A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph
- 构建案例增强的提及图，通过语义关联发现条文间竞争关系。
- 新方法在精度@5上提升98.2%，误报率降20.8%，漏报率降8.3%。
- 适用于法律智能系统研发者及立法、司法人员参考。
随着社会系统日益复杂，法律条文也日趋繁复，人类越来越难以识别条文间的潜在竞争关系，尤其是在制定新法或适用现行法时。然而，目前尚无有效方法用于检测此类竞争。本文提出一项新的法律AI任务——法律条文竞争检测（LACD），旨在识别给定法律中相互竞争的条文。我们提出一种新型检索方法CAM-Re2，其在LACD任务中表现优异：相比现有方法，误报率降低20.8%，漏报率降低8.3%，精度@5提升98.2%。代码已开源至https://github.com/asmath472/LACD-public。
原文摘要 · Abstract (English)
As social systems become increasingly complex, legal articles are also growing more intricate, making it progressively harder for humans to identify any potential competitions among them, particularly when drafting new laws or applying existing laws. Despite this challenge, no method for detecting such competitions has been proposed so far. In this paper, we propose a new legal AI task called Legal Article Competition Detection (LACD), which aims to identify competing articles within a given law. Our novel retrieval method, CAM-Re2, outperforms existing relevant methods, reducing false positives by 20.8% and false negatives by 8.3%, while achieving a 98.2% improvement in precision@5, for the LACD task. We release our codes at https://github.com/asmath472/LACD-public.
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