Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation
This work addresses the challenge of automated annotation for legal documents in domains with limited access to comprehensive databases, though it appears incremental in applying existing concepts to a specific legal context.
The paper tackles the problem of limited labeled datasets for legal recommender systems in specialized domains like labor disputes by proposing a method that uses co-citation of legal articles to establish case similarity and enable algorithmic annotation. The evaluation shows that a recommender system with finetuned text embedding models and a BiLSTM module can recommend similar labor cases based on this similarity measure.
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.