@inproceedings{liu-etal-2025-jurex,
title = "{JUREX}-4{E}: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning",
author = "Liu, Huanghai and
Huang, Quzhe and
Chen, Qingjing and
Hu, Yiran and
Ma, Jiayu and
Liu, Yun and
Shen, Weixing and
Feng, Yansong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.188/",
doi = "10.18653/v1/2025.emnlp-main.188",
pages = "3794--3814",
ISBN = "979-8-89176-332-6",
abstract = "In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning.To address these issues, we present JUREX-4E, an expert-annotated four-element knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority. We evaluate JUREX-4E on the Similar Charge Disambiguation task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: \url{https://github.com/THUlawtech/JUREX}"
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<abstract>In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning.To address these issues, we present JUREX-4E, an expert-annotated four-element knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority. We evaluate JUREX-4E on the Similar Charge Disambiguation task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: https://github.com/THUlawtech/JUREX</abstract>
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%0 Conference Proceedings
%T JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning
%A Liu, Huanghai
%A Huang, Quzhe
%A Chen, Qingjing
%A Hu, Yiran
%A Ma, Jiayu
%A Liu, Yun
%A Shen, Weixing
%A Feng, Yansong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-jurex
%X In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning.To address these issues, we present JUREX-4E, an expert-annotated four-element knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority. We evaluate JUREX-4E on the Similar Charge Disambiguation task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: https://github.com/THUlawtech/JUREX
%R 10.18653/v1/2025.emnlp-main.188
%U https://aclanthology.org/2025.emnlp-main.188/
%U https://doi.org/10.18653/v1/2025.emnlp-main.188
%P 3794-3814
Markdown (Informal)
[JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning](https://aclanthology.org/2025.emnlp-main.188/) (Liu et al., EMNLP 2025)
ACL
- Huanghai Liu, Quzhe Huang, Qingjing Chen, Yiran Hu, Jiayu Ma, Yun Liu, Weixing Shen, and Yansong Feng. 2025. JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 3794–3814, Suzhou, China. Association for Computational Linguistics.