Fernandes, William Paulo DuccaFrajhof, Isabella ZalcbergGUILHERME DA FRANCA COUTO FERNANDES DE ALMEIDARodrigues, Ariane Moraes BuenoBarbosa, Simone Diniz JunqueiraKonder, Carlos NelsonNasser, Rafael BarbosaCarvalho, Gustavo Robichez deLopes, Hélio Côrtes Vieira2024-06-182024-06-1820220306-4379https://repositorio.insper.edu.br/handle/11224/6694We 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.Digital14 p.enNatural language processingDeep learningRecurrent neural networksLong short-term memoryMachine learningConditional random fieldsInformation extractionLaw10.1016/j.is.2021.101965