Using natural language processing methods to predict judicial outcomes

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Bertalan, Vithor Gomes Ferreira
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://www.teses.usp.br/teses/disponiveis/59/59143/tde-04012021-232455/
Resumo: Natural Language Processing (NLP) and Artificial Intelligence (AI) for the field of Law is a growing area, with the potential of radically changing the daily routine of legal professionals. The amount of text generated by those professionals is outstanding, and to this point, it is a knowledge area to be more explored by Computer Science. One of the most acclaimed fields for the combined area of NLP, AI, and Law is Legal Prediction, in which intelligent systems try to predict specific judicial characteristics, such as the judicial outcome or the judicial class or a given case. This research creates classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. Afterward, we developed a dataset of Second Degree Murder and Active Corruption cases, and different classifiers, such as Support Vector Machines and Neural Networks, were used to predict judicial outcomes by analyzing textual features. As a final goal, we used the findings of one of the algorithms, Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants.