A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph
This addresses the challenge for legal professionals in identifying conflicts among complex legal articles, particularly in drafting or applying laws, though it is domain-specific and incremental in nature.
The paper tackles the problem of detecting competing legal articles in Korean criminal law, proposing a new task called Legal Article Competition Detection (LACD) and introducing a retrieval method, CAM-Re2, that reduces false positives by 20.8% and false negatives by 8.3% while improving precision@5 by 98.2%.
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.