PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
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.
- ▪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.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2607.09094 (cs) [Submitted on 10 Jul 2026] Title:PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation Authors:Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar, Balaji Ganesan View a PDF of the paper titled PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation, by Devanshu Verma and 3 other authors View PDF HTML (experimental) Abstract: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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
