Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation

• URL: http://arxiv.org/abs/2504.20323v1
• Date: Tue, 29 Apr 2025 00:26:37 GMT
• Title: Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation
• Authors: Chao-Lin Liu, Po-Hsien Wu, Yi-Ting Yu,
• Abstract summary: We propose a new approach leveraging the co-citation of legal articles within cases to establish similarity and enable algorithmic annotation.<n>We employ a system that recommends similar cases based on plaintiffs' accusations, defendants' rebuttals, and points of disputes.<n>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.
• Score: 0.9902389530203038
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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