@inproceedings{an-etal-2025-grex,
title = "{GR}e{X}: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in {K}orean Criminal Law",
author = "An, Seonho and
Rhim, Young-Yik and
Kim, Min-Soo",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nllp-1.30/",
pages = "408--423",
ISBN = "979-8-89176-338-8",
abstract = "As social systems become more complex, legal articles have grown increasingly intricate, making it harder for humans to identify potential conflicts among them, particularly when drafting new laws or applying existing ones. Despite its importance, no method has been proposed to detect such conflicts. We introduce a new legal NLP task, Legal Article Conflict Detection (LACD), which aims to identify conflicting articles within a given body of law. To address this task, we propose GReX, a novel graph neural network-based retrieval method. Experimental results show that GReX significantly outperforms existing methods, achieving improvements of 44.8{\%} in nDCG@50, 32.8{\%} in Recall@50, and 39.8{\%} in Retrieval F1@50. Our codes are in github.com/asmath472/LACD-public."
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%0 Conference Proceedings
%T GReX: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in Korean Criminal Law
%A An, Seonho
%A Rhim, Young-Yik
%A Kim, Min-Soo
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-338-8
%F an-etal-2025-grex
%X As social systems become more complex, legal articles have grown increasingly intricate, making it harder for humans to identify potential conflicts among them, particularly when drafting new laws or applying existing ones. Despite its importance, no method has been proposed to detect such conflicts. We introduce a new legal NLP task, Legal Article Conflict Detection (LACD), which aims to identify conflicting articles within a given body of law. To address this task, we propose GReX, a novel graph neural network-based retrieval method. Experimental results show that GReX significantly outperforms existing methods, achieving improvements of 44.8% in nDCG@50, 32.8% in Recall@50, and 39.8% in Retrieval F1@50. Our codes are in github.com/asmath472/LACD-public.
%U https://aclanthology.org/2025.nllp-1.30/
%P 408-423
Markdown (Informal)
[GReX: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in Korean Criminal Law](https://aclanthology.org/2025.nllp-1.30/) (An et al., NLLP 2025)
ACL