Extracting value from Brazilian Court decisions

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dc.contributor.authorFernandes, William Paulo Ducca
dc.contributor.authorFrajhof, Isabella Zalcberg
dc.contributor.authorGUILHERME DA FRANCA COUTO FERNANDES DE ALMEIDA
dc.contributor.authorRodrigues, Ariane Moraes Bueno
dc.contributor.authorBarbosa, Simone Diniz Junqueira
dc.contributor.authorKonder, Carlos Nelson
dc.contributor.authorNasser, Rafael Barbosa
dc.contributor.authorCarvalho, Gustavo Robichez de
dc.contributor.authorLopes, Hélio Côrtes Vieira
dc.creatorFernandes, William Paulo Ducca
dc.creatorFrajhof, Isabella Zalcberg
dc.creatorRodrigues, Ariane Moraes Bueno
dc.creatorBarbosa, Simone Diniz Junqueira
dc.creatorKonder, Carlos Nelson
dc.creatorNasser, Rafael Barbosa
dc.creatorCarvalho, Gustavo Robichez de
dc.creatorLopes, Hélio Côrtes Vieira
dc.date.accessioned2024-06-18T20:33:19Z
dc.date.available2024-06-18T20:33:19Z
dc.date.issued2022
dc.description.abstractWe propose a methodology to extract value from Brazilian Court decisions to support judges and lawyers in their decision-making. We instantiate our methodology in one information system we have developed. Such system (i) extracts plaintiff’s legal claims and each specific provision on legal opinions enacted by lower and Appellate Courts, and (ii) connects each legal claim with the corresponding judicial provision. The information system presents the results through visualizations. Information Extraction for legal texts has been previously approached in the literature for different languages, using different methods. Our proposal is different from previous work, since our corpora comprise Brazilian lower and Appellate Court decisions, in which we look for a set of plaintiff’s legal claims and judicial provisions commonly judged by the Court. We use the following methods to tackle the information extraction tasks: Bidirectional Long Short-Term Memory network; Conditional Random Fields; and a combination of Bidirectional Long Short-Term Memory network and Conditional Random Fields. In addition to the well-known distributed representation of words in word embeddings, we use character-level representation of words in character embeddings. We have built three corpora – Kauane Insurance Report, Kauane Insurance Lower, and Kauane Insurance Upper – to train and evaluate the system, using public data from the State Court of Rio de Janeiro. Our methods achieved good quality for Kauane Insurance Lower and Kauane Insurance Upper, and promising results for Kauane Insurance Report.en
dc.formatDigital
dc.format.extent14 p.
dc.identifier.doi10.1016/j.is.2021.101965
dc.identifier.issn0306-4379
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/6694
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofInformation Systems
dc.subjectNatural language processingen
dc.subjectDeep learningen
dc.subjectRecurrent neural networksen
dc.subjectLong short-term memoryen
dc.subjectMachine learningen
dc.subjectConditional random fieldsen
dc.subjectInformation extractionen
dc.subjectLawen
dc.titleExtracting value from Brazilian Court decisions
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.sciencedirect.com/science/article/pii/S030643792100154X?via%3Dihub
local.publisher.countryNão Informado
local.subject.cnpqCIENCIAS SOCIAIS APLICADAS
publicationvolume.volumeNumber106
relation.isAuthorOfPublication8575f912-24df-44e3-8512-a288b848e951
relation.isAuthorOfPublication.latestForDiscovery8575f912-24df-44e3-8512-a288b848e951
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