ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints
ARGUS builds event knowledge graphs from U.S. employment-discrimination complaints to improve claim classification and legal QA.
ARGUS is a source-grounded pipeline that builds document-level event knowledge graphs from CourtListener U.S. employment-discrimination complaints. It combines a 5W1H-inspired schema, legal-domain models, and LLM structured generation to capture participants, time, and causal links, then merges chunk graphs into document graphs. A graph-structured classifier outperformed raw and linearized baselines on held-out claim classification, and EKG-only retrieval improved document-scoped legal QA, while open-retrieval gains were limited by low first-stage recall.
- ARGUS builds document-level event graphs from discrimination complaints.
- It combines a 5W1H schema, legal models, and LLM generation.
- A graph classifier beat raw and linearized baselines on claim classification.
- EKG retrieval improved document-scoped QA; open retrieval stayed recall-limited.
- Organizations
- CourtListener
- Countries
- United States
From the original article
1 min read at arxiv.orgU. S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone.
By Sriram Kannan · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.