Exploring Legislative Textual Data in Brazilian Portuguese: Readability Analysis and Knowledge Graph Generation - Gisliany Lillian Alves de Oliveira; Breno Santana Santos; Marianne Silva; Ivanovitch Silva (Data)
Legislative documents are crucial to democratic societies, defining the legal framework for social life. In Brazil, legislative texts are particularly complex due to extensive technical jargon, intricate sentence structures, and frequent references to prior legislation. The country’s civil law tradition and multicultural context introduce further interpretative and linguistic challenges. Moreover, the study of Brazilian Portuguese legislative texts remains underexplored, lacking legal-specific models and datasets. To address these gaps, this work proposes a data-driven approach utilizing large language models (LLMs) to analyze these documents and extract knowledge graphs (KGs). A case study was conducted using 1869proposals from the Legislative Assembly of Rio Grande do Norte (ALRN), spanning January 2019 to April 2024. The Llama 3.2 3B Instruct model was employed to extract KGs representing entities and their relationships. The findings support the method’s effectiveness in producing coherent graphs faithful to the original content. Nevertheless, challenges remain in resolving entity ambiguity and achieving full relationship coverage. Additionally, readability analyses using metrics for Brazilian Portuguese revealed that ALRN proposals require superior reading skills due to their technical style. Ultimately, this study advances legal artificial intelligence by providing insights into Brazilian legislative texts and promoting transparency and accessibility through natural language processing techniques.