Development and Evaluation of an Intelligence and Learning System in Jurisprudence Text Mining in the Field of Competition Defense

Abstract

A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. In the similarity of legal decisions, jurisprudence seeks subsidies that provide stability, uniformity, and some predictability in the analysis of a case decided. This paper presents a proposed solution architecture for the jurisprudence search system of the Brazilian Administrative Council for Economic Defense (CADE), with a view to building and expanding the knowledge generated regarding the economic defense of competition to support the agency’s final procedural business activities. We conducted a literature review and a survey to investigate the characteristics and functionalities of the jurisprudence search systems used by Brazilian public administration agencies. Our findings revealed that the prevailing technologies of Brazilian agencies in developing jurisdictional search systems are Java programming language and Apache Solr as the main indexing engine. Around 87% of the jurisprudence search systems use machine learning classification. On the other hand, the systems do not use too many artificial intelligence and morphological construction techniques. No agency participating in the survey claimed to use ontology to treat structured and unstructured data from different sources and formats.