Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research

Abstract:Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. Designing the extraction schema and then extracting the data remain a manual, expertise-heavy bottleneck. We present schematize, an open-source multi-agent system that interactively turns a researcher's problem statement into a validated extraction schema that can later be used for autonomous extraction. Schematize couples (i) a clarification dialogue that elicits implicit expert intent, (ii) iterative schema generation, (iii) data-grounded refinement that tests the schema against documents, and (iv) chat-based post-editing. We evaluated the system with human legal professional, introducing our novel methodology, and schematize achieves top performance in most of tested configurations. While the system is designed to be domain-agnostic and applicable to any document collection, we tailor and evaluate it on legal research problems. We release schematize as a pip-installable Python package with full documentation.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.22209 [cs.CL]

(or arXiv:2609.22209v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.22209 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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