A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph
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| Format: | Preprint |
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2024
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| _version_ | 1866912158915231744 |
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| author | An, Seonho Rhim, Young Yik Kim, Min-Soo |
| author_facet | An, Seonho Rhim, Young Yik Kim, Min-Soo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_11787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph An, Seonho Rhim, Young Yik Kim, Min-Soo Computation and Language Artificial Intelligence Information Retrieval Machine Learning I.2.7 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. |
| title | A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning I.2.7 |
| url | https://arxiv.org/abs/2412.11787 |