The PRecG pipeline improves legal precedent retrieval by decomposing documents into semantic units based on rhetorical roles rather than treating them as monolithic texts.
- It constructs knowledge graphs for each rhetorical segment to capture legal entities and relationships.
- Contextual entity representations are aggregated to create segment-level embeddings.
- These embeddings are integrated to produce a unified document-level representation for similarity computation.
- The approach was validated on a benchmark Indian legal dataset against state-of-the-art baselines.