Using natural language processing methods to predict judicial outcomes
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | 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. |
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Using natural language processing methods to predict judicial outcomesUsando métodos de processamento de linguagem natural para prever resultados judiciaisClassificador jurídicoLegal classifierLegal predictionNatural language processingPredição jurídicaProcessamento de linguagem naturalNatural 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.Processamento de Linguagem Natural (PLN) e Inteligência Artificial (IA) para a Área Jurídica é uma área em crescimento, com o potencial de mudar radicalmente a rotina diária dos profissionais jurídicos. A quantidade de texto gerada por estes profissionais é imensa, e até o momento inexplorada pela Ciência da Computação. Uma das áreas mais aclamadas é a Predição Jurídica, onde sistemas inteligentes tentam predizer certas características jurídicas, como os pareceres ou a classe jurídica de um dado caso. Esta pesquisa cria classificadores para predizer pareceres jurídicos no sistema legal brasileiro. Para atingir este objetivo, desenvolvemos um rastreador de texto para retirar dados dos sistemas eletrônicos legais do Brasil. Depois, criamos um conjunto de dados composto por casos de Homicídio Simples e Corrupção Ativa, e diferentes classificadores, como máquinas de vetores suporte e redes neurais, foram utilizados com o objetivo de predizer os pareceres através da observação das características textuais. Como um objetivo final, utilizamos os resultados de um dos algoritmos, as Hierarchical Attention Networks, para achar exemplos das palavras que foram mais importantes para absolver ou condenar réus.Biblioteca Digitais de Teses e Dissertações da USPRuiz, Evandro Eduardo SeronBertalan, Vithor Gomes Ferreira2020-11-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/59/59143/tde-04012021-232455/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2021-01-22T20:57:02Zoai:teses.usp.br:tde-04012021-232455Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212021-01-22T20:57:02Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Using natural language processing methods to predict judicial outcomes Usando métodos de processamento de linguagem natural para prever resultados judiciais |
title |
Using natural language processing methods to predict judicial outcomes |
spellingShingle |
Using natural language processing methods to predict judicial outcomes Bertalan, Vithor Gomes Ferreira Classificador jurídico Legal classifier Legal prediction Natural language processing Predição jurídica Processamento de linguagem natural |
title_short |
Using natural language processing methods to predict judicial outcomes |
title_full |
Using natural language processing methods to predict judicial outcomes |
title_fullStr |
Using natural language processing methods to predict judicial outcomes |
title_full_unstemmed |
Using natural language processing methods to predict judicial outcomes |
title_sort |
Using natural language processing methods to predict judicial outcomes |
author |
Bertalan, Vithor Gomes Ferreira |
author_facet |
Bertalan, Vithor Gomes Ferreira |
author_role |
author |
dc.contributor.none.fl_str_mv |
Ruiz, Evandro Eduardo Seron |
dc.contributor.author.fl_str_mv |
Bertalan, Vithor Gomes Ferreira |
dc.subject.por.fl_str_mv |
Classificador jurídico Legal classifier Legal prediction Natural language processing Predição jurídica Processamento de linguagem natural |
topic |
Classificador jurídico Legal classifier Legal prediction Natural language processing Predição jurídica Processamento de linguagem natural |
description |
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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-06 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/59/59143/tde-04012021-232455/ |
url |
https://www.teses.usp.br/teses/disponiveis/59/59143/tde-04012021-232455/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
_version_ |
1809090359196647424 |