# A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph

arXiv: https://arxiv.org/abs/2412.11787 · HTML: https://mlsanity.com/paper/2412.11787/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 26 of 26)

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- [Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach](https://mlsanity.com/paper/2312.09591/) (2023)
- [Complex Societies and the Growth of the Law](https://mlsanity.com/paper/2005.07646/) (2020)
- [Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity](https://mlsanity.com/paper/2007.03225/) (2020)
- [Re3val: Reinforced and Reranked Generative Retrieval](https://mlsanity.com/paper/2401.16979/) (2024)
- [Law Article-Enhanced Legal Case Matching: a Causal Learning Approach](https://mlsanity.com/paper/2210.11012/) (2022)

## Abstract (arXiv)

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.