用法律条文共引关系自动标注判例相似性，提升劳动争议案件推荐效果。
Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation
- 基于判决书中法律条文的共引用关系构建案例相似度
- 在劳动争议场景下，推荐结果与条文共引相似度相关性达0.68
- 适合需要自动化法律文档标注的研究者和司法科技开发者
本报告针对劳动争议等专业领域法律推荐系统因标注数据匮乏带来的挑战，提出一种新方法：利用判决书中法律条文的共引用关系来衡量案例相似性，并实现算法化标注。该方法借鉴案例共引概念，将被共同引用的判例视为具有共享法律问题的证据。为评估标注效果，设计了一套基于原告指控、被告抗辩及争议焦点的案例推荐系统。实验表明，结合微调文本嵌入模型与合理配置的BiLSTM模块，该推荐系统可有效识别出法律条文共引相似的劳动争议案例。研究推动了法律文书自动化标注技术的发展，尤其适用于缺乏全面法律数据库的场景。
原文摘要 · Abstract (English)
This report addresses the challenge of limited labeled datasets for developing legal recommender systems, particularly in specialized domains like labor disputes. We propose a new approach leveraging the co-citation of legal articles within cases to establish similarity and enable algorithmic annotation. This method draws a parallel to the concept of case co-citation, utilizing cited precedents as indicators of shared legal issues. To evaluate the labeled results, we employ a system that recommends similar cases based on plaintiffs' accusations, defendants' rebuttals, and points of disputes. The evaluation demonstrates that the recommender, with finetuned text embedding models and a reasonable BiLSTM module can recommend labor cases whose similarity was measured by the co-citation of the legal articles. This research contributes to the development of automated annotation techniques for legal documents, particularly in areas with limited access to comprehensive legal databases.
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