JURIS：基于理解增强型指令微调的司法命名实体识别方法

彭晗; 阮日青; 胡颖; 刘琼林; 张震

doi:10.12263/DZXB.20250656

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JURIS：基于理解增强型指令微调的司法命名实体识别方法

大模型与互联网 | 更新时间：2025-12-27

- JURIS：基于理解增强型指令微调的司法命名实体识别方法
- JURIS: Judical Understanding-Enhanced Reasoning via Instruction-Tuned Strategies for Named Entity Recognition
- 电子学报 2025年53卷第9期页码：3117-3133
- 作者机构：
  
  1.湖南工商大学智能机器人学院，湖南长沙 410000
  2.湘江实验室，湖南长沙 410000
  3.湖南工商大学计算机学院，湖南长沙 410000
  4.湖南工商大学人工智能与先进计算学院，湖南长沙 410000
- 作者简介：
  
  [ "彭晗男，1992年8月出生于湖南省湘西土家族苗族自治州.现为湖南工商大学智能机器人学院讲师.主要研究方向为大语言模型优化、多模态融合、具身智能等.E-mail: Han.Peng@hutb.edu.cn" ]
  [ "阮日青男，1999年9月出生于福建省莆田市.现为湖南工商大学计算机学院硕士研究生.主要研究方向为大语言模型优化.E-mail: 230720835001@stu.hutb.edu.cn" ]
  [ "胡颖女，2004年3月出生于湖北省咸宁市.现为湖南工商大学人工智能与先进计算学院本科生.主要研究方向为大语言模型优化. E-mail: 2223030063@stu.hutb.edu.cn" ]
  [ "刘琼林男，2002年10月出生于湖南省岳阳市.现为湖南工商大学人工智能与先进计算学院硕士研究生.主要研究方向为大语言模型优化.E-mail: 240120854118@stu.hutb.edu.cn" ]
  [ "张震男，1994年8月出生于内蒙古自治区呼伦贝尔市.现为湖南工商大学人工智能与先进计算学院副教授.主要研究方向为算法分析与设计、人工智能理论基础.E-mail: zz@hutb.edu.cn" ]
- 基金信息：
  
  湘江实验室重大项目(25XJ01001)
- DOI：10.12263/DZXB.20250656
  中图分类号： TP391;
- 收稿：2025-07-28，
  
  录用：2025-09-24，
  
  纸质出版：2025-09-25
- 稿件说明：
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彭晗, 阮日青, 胡颖, 等. JURIS：基于理解增强型指令微调的司法命名实体识别方法[J]. 电子学报, 2025, 53(09): 3117-3133.

PENG Han, RUAN Ri-qing, HU Ying, et al. JURIS: Judical Understanding-Enhanced Reasoning via Instruction-Tuned Strategies for Named Entity Recognition[J]. Acta Electronica Sinica, 2025, 53(09): 3117-3133.
彭晗, 阮日青, 胡颖, 等. JURIS：基于理解增强型指令微调的司法命名实体识别方法[J]. 电子学报, 2025, 53(09): 3117-3133. DOI：10.12263/DZXB.20250656

PENG Han, RUAN Ri-qing, HU Ying, et al. JURIS: Judical Understanding-Enhanced Reasoning via Instruction-Tuned Strategies for Named Entity Recognition[J]. Acta Electronica Sinica, 2025, 53(09): 3117-3133. DOI：10.12263/DZXB.20250656

摘要

命名实体识别（Named Entity Recognition，NER）是法律文本结构分析和语义理解的基础任务，能够极大提高司法效率，促进司法公正.然而，受限于法律文本的高度复杂性与专业性，传统NER方法难以充分理解法律文书中的上下文关联，较多依赖于浅层的词级预测，缺乏实体角色解析与深层语境推理能力，尤其在面对司法文本中频繁出现的嵌套实体、细粒度实体以及模糊的实体边界时存在明显的局限性.为解决上述问题，本文基于理解增强的建模范式，提出了一种面向中文法律场景的新型命名实体识别框架——JURIS（Judicial Understanding-enhanced Reasoning via Instruction-tuned Strategies for named entity recognition）.该框架将实体识别重新建模为基于语境理解的条件生成任务，通过采用创新性的上下文感知的嵌入式标注策略，在保留文本原始语义结构的同时有效增强上下文信息建模能力，从而提升复杂语境下的实体识别效果.同时，JURIS构建了一个由规范模块、知识引导模块和类比学习模块组成的三元理解增强模块（Tri-aspect Understanding Enhancement Module，Tri-UEM），分别从输出一致性、领域知识注入与语境类比迁移3个维度协同提升模型对法律领域实体语义的深层理解与判别能力.实证结果表明，JURIS在CAIL2021、Drug和CSKS2019等多个领域数据集上均超过现有强基线模型，取得了当前最佳性能，改善了嵌套实体处理与细粒度识别表现，并展现出其在垂直领域信息抽取任务中的广泛适用性与推广潜力.

Abstract

Named entity recognition (NER) serves as a fundamental task in the structural analysis and semantic understanding of legal texts

with the potential to greatly enhance judicial efficiency and promote fairness. However

due to the high complexity and domain specificity of legal language

traditional NER methods struggle to adequately capture contextual dependencies in legal documents. They often rely on shallow token-level predictions

lacking both role-based entity interpretation and deeper contextual reasoning. These limitations are particularly pronounced when dealing with nested entities

fine-grained entity categories

and ambiguous boundaries that frequently occur in judicial texts. To address these challenges

this paper introduces a novel NER framework for Chinese legal scenarios

termed JURIS (judicial understanding-enhanced reasoning via instruction-tuned strategies for named entity recognition). JURIS reformulates entity recognition as a context-driven conditional generation task and adopts an innovative context-aware embedded annotation strategy

which preserves the original semantic structure of the text while effectively enhancing contextual modeling. In addition

JURIS incorporates a tri-aspect understanding enhancement module (Tri-UEM)

consisting of a standardization module

a knowledge-guided module

and an analogy-based learning module. These components jointly strengthen the model’s semantic understanding and discrimination ability in the legal domain by improving output consistency

injecting domain-specific knowledge

and enabling contextual analogy transfer. Experimental results demonstrate that JURIS consistently outperforms strong baseline models on multiple datasets

including CAIL2021

Drug

and CSKS2019

achieving state-of-the-art performance. It significantly improves recognition of nested and fine-grained entities while showing strong generalizability and applicability in domain-specific information extraction tasks.

关键词

Keywords

references

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