Abstract
This study addresses the growing need to convert unstructured legal and policy documents into formal computational models that support transparent decision-making. The purpose of the work is to develop an applied framework that connects Legal NLP and policy information extraction with canonical combinatorial decision models, including set cover, set packing, subset sum, vertex cover, and independent set. The proposed method combines MPNet-based dense semantic retrieval for locating relevant legal passages, a Legal NLP layer for extracting obligations, prohibitions, exceptions, thresholds, and eligibility conditions, and a formal modeling stage that maps the extracted constraints to NP-complete formulations, including set cover, set packing, subset sum, vertex cover, and independent set. The framework is designed to transform regulatory text into machine-interpretable structures suitable for constraint-aware reasoning and policy analysis. The results show that the integration of semantic retrieval and structured legal information extraction improves the consistency, interpretability, and practical usability of formal problem construction from legal and policy documents. The proposed approach provides a reproducible bridge between legal text analytics and combinatorial decision modeling and supports legal decision support, compliance analysis, and policy-oriented intelligent systems.