信息网络安全

信息网络安全 ›› 2026, Vol. 26 ›› Issue (3): 471-481.doi: 10.3969/j.issn.1671-1122.2026.03.013

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标签语义增强的低资源案件关键要素识别

肖文1,2(), 涂敏1,2   

  1. 1.江西警察学院网络安全学院，南昌 330100
    2.电子数据管控与取证江西省重点实验室，南昌 330100
  • 收稿日期:2025-08-10 出版日期:2026-03-10 发布日期:2026-03-30
  • 通讯作者: 肖文 E-mail:shauven@126.com
  • 作者简介:肖文（1976—），男，江西，讲师，博士，主要研究方向为信息内容安全、电子取证|涂敏（1967—），女，江西，教授，本科，主要研究方向为电子取证、网络信息安全
  • 基金资助:
    江西省教育厅科学技术研究项目(GJJ212203);电子数据管控与取证江西省重点实验室开放基金项目(2025JXJYKFJJ003)

Key Element Identification of Low-Resource Cases with Label Semantic Enhancement

XIAO Wen1,2(), TU Min1,2   

  1. 1. School of Cyber Security, Jiangxi Police College, Nanchang 330100, China
    2. Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics, Nanchang 330100, China
  • Received:2025-08-10 Online:2026-03-10 Published:2026-03-30

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摘要：

案件关键要素识别是司法文本智能分析的核心任务，在类案检索、裁判辅助等场景中具有重要价值。然而，司法领域标注数据稀缺的“低资源”特性，导致依赖大规模标注数据的传统命名实体识别方法性能受限。文章提出一种融合标签语义信息的识别模型，将实体类型标签作为提示信息嵌入文本编码过程，通过构建标签锚点向量与上下文文本向量的交互机制，显式建模标签与文本之间的语义关联，增强模型对要素类型语义的理解能力和低资源场景下的要素边界定位能力。实验结果表明，该方法在低资源案件数据集上的识别性能优于对比的基线模型，验证了标签语义对关键要素识别的增强作用，为司法领域低资源信息抽取任务提供了新的解决方案。

关键词: 案件关键要素识别, 低资源, 标签语义, 命名实体识别, 司法文本分析

Abstract:

The identification of key elements in cases is a core task in intelligent analysis of judicial texts, and has significant value in scenarios such as case retrieval and judicial decision support. However, the “low-resource” nature of scarce labeled data in the judicial domain limits the performance of traditional named entity recognition methods that rely on large-scale labeled data. This paper proposed a recognition model that integrated label semantic information, embedding entity type labels as prompt information into the text encoding process. By constructing an interaction mechanism between label anchor vectors and contextual text vectors, the model explicitly captured the semantic associations between labels and text, enhancing its understanding of element type semantics and its ability to locate element boundaries in low-resource scenarios. Experimental results show that the proposed method outperforms baseline models on low-resource case datasets, demonstrating the enhancement effect of label semantics on key element identification and providing a new solution for low-resource information extraction tasks in the judicial domain.

Key words: identification of key case elements, low resources, label semantics, named entity recognition, judicial text analysis

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