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.

本研究解决了在劳动争议等专业领域开发法律推荐系统时缺乏标注数据集的问题。我们提出了一种新方法，通过案件中法律条款的共引来建立相似性，从而实现算法注释。研究结果表明，该推荐系统能够有效推荐相似的劳动案件，促进法律文书自动标注技术的发展。

基于判决文书中法律条款共引的案件相似性标注与实证争议评估