PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research. Current approaches for automatic precedent retrieval map legal documents to a low-dimensional semantic space and compute similarity based on the proximity of their representations. These approaches treat legal documents as monolithic texts, ignoring the rhetorical organization of the legal technicalities. Ergo, they overlook nuanced legal meanings and fail to distinguish the contextual significance of legal entities and concepts that vary based on their rhetorical roles within the document. To address this insufficiency, we propose the PRecG pipeline that computes ...
| Authors | Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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