The Use of Machine Learning in the Classification of Electronic Lawsuits: An Application in the Court of Justice of Minas Gerais

Silva, Adriano Capanema; Maia, Luiz Cláudio Gomes

doi:10.1007/978-3-030-61377-8_43

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…In [ 14 ], an approach for classifying articles of law for the Italian legal system based on a learning framework based on Bidirectional Encoder Representations from Transformers (BERT) was presented. Other examples are presented in [ 15 ], which performed experiments to detect through the analysis of judgments of the European Court of Human Rights whether the case was judged as a violation of someone’s right or not, and [ 16 ] used Glove word vectors generated for the Portuguese Language and Convolutional Neural Network (CNN) to perform the classification of types of documents in judicial proceedings of the Court of Justice of Minas Gerais (TJMG).…”

Section: State Of the Artmentioning

confidence: 99%

Predicting the number of days in court cases using artificial intelligence

Oliveira¹,

Reis

Nascimento

2022

PLoS ONE

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Brazilian legal system prescribes means of ensuring the prompt processing of court cases, such as the principle of reasonable process duration, the principle of celerity, procedural economy, and due legal process, with a view to optimizing procedural progress. In this context, one of the great challenges of the Brazilian judiciary is to predict the duration of legal cases based on information such as the judge, lawyers, parties involved, subject, monetary values of the case, starting date of the case, etc. Recently, there has been great interest in estimating the duration of various types of events using artificial intelligence algorithms to predict future behaviors based on time series. Thus, this study presents a proof-of-concept for creating and demonstrating a mechanism for predicting the amount of time, after the case is argued in court (time when a case is made available for the magistrate to make the decision), for the magistrate to issue a ruling. Cases from a Regional Labor Court were used as the database, with preparation data in two ways (original and discretization), to test seven machine learning techniques (i) Multilayer Perceptron (MLP); (ii) Gradient Boosting; (iii) Adaboost; (iv) Regressive Stacking; (v) Stacking Regressor with MLP; (vi) Regressive Stacking with Gradient Boosting; and (vii) Support Vector Regression (SVR), and determine which gives the best results. After executing the runs, it was identified that the adaboost technique excelled in the task of estimating the duration for issuing a ruling, as it had the best performance among the tested techniques. Thus, this study shows that it is possible to use machine learning techniques to perform this type of prediction, for the test data set, with an R2 of 0.819 and when transformed into levels, an accuracy of 84%.

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Section: State Of the Artmentioning

confidence: 99%

Predicting the number of days in court cases using artificial intelligence

Oliveira¹,

Reis

Nascimento

2022

PLoS ONE

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“…The selected articles mainly belong to three categories. The first one concerns the prediction of outcomes from judgments [17][18][19][20] and classification of legal documents [21]. The second branch of works investigates the duration of a case in machine learning [16,22,23], and a mathematical [24][25][26][27] perspective.…”

Section: Related Workmentioning

confidence: 99%

“…Silva and Maia [21] classified legal documents using CNN and SVM models. These text documents resemble the procedural movements (PM) previously mentioned, such as Petition, Sentence and Decision.…”

Section: Judgement Outcomes Prediction and Legal Documents Classifica...mentioning

confidence: 99%

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Vercosa,

Silva,

Cruz

et al. 2024

Preprint

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Delays in legal proceedings can significantly harm both corporate finances and individual lives. While many jurisdictions rely on subjective criteria such as reasonable process duration to manage case processing times, machine learning offers an alternative approach by estimating lawsuit durations and assessing the impact of multiple variables on these timelines. This study explores the use of machine learning to estimate lawsuit duration, considering multiple variables, including control-flow characteristics. We observed that the sequences of procedural movements exhibit complex patterns, making them suitable for analysis using clustering techniques employed in process mining. We framed the problem as a supervised learning regression task, incorporating clustering results as additional features. Employing linear regression, support vector regression, and gradient boosting methods, we analyzed over 60,000 cases from Brazilian labor courts. The gradient boosting model achieved an R2-score of 0.87, and feature importance analysis revealed that clustering techniques features emerged among the most significant in time estimation.

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“…A disponibilização desses modelos pré-treinados são importantes pois o custo computacional para o treinamento deles do zero poderia inviabilizar muitas pesquisas. O que confirma essa importância, é que trabalhos analisados na Seção 6, especialmente [Silva and Maia 2020] e [Mota et al 2020], lançam mão dos modelos de word embedding gerados em [Hartmann et al 2017] dentro da arquitetura de Classificação Textual aplicada.…”

Section: Modelos De Linguagem Estado Da Arte Em Classificação Textual (Q1)unclassified

“…Dentre as aplicações no contexto jurídico, há a classificação de documentos para fins de migração dos processos físicos para eletrônicos [Silva and Maia 2020], ou ainda para direcionamento correto de demandas peticionadas eletronicamente, tal qual analisado em [Noguti et al 2020] e [Mota et al 2020]. Essas atividades requerem recursos humanos e horas de trabalho que podem ser destinadas a outras áreas com necessidades mais cognitivas, possibilitando a promoção de maior celeridade no Sistema de Justiça Brasileiro.…”

Section: Introductionunclassified

Text Classification in Law Area: a Systematic Review

Martins¹,

Silva²

2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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Automatic Text Classification represents a great improvement in law area workflow, mainly in the migration of physical to electronic lawsuits. A systematic review of studies on text classification in law area from January 2017 up to February 2020 was conducted. The search strategy identified 20 studies, that were analyzed and compared. The review investigates from research questions: what are the state-of-art language models, its application of text classification in English and Brazilian Portuguese datasets from legal area, if there are available language models trained on Brazilian Portuguese, and datasets in Brazilian law area. It concludes that there are applications of automatic text classification in Brazil, although there is a gap on the use of language models when compared with English language dataset studies, also the importance of language model in domain pre-training to improve results, as well as there are two studies making available Brazilian Portuguese language models, and one introducing a dataset in Brazilian law area.

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The Use of Machine Learning in the Classification of Electronic Lawsuits: An Application in the Court of Justice of Minas Gerais

Cited by 5 publications

References 6 publications

Predicting the number of days in court cases using artificial intelligence

Predicting the number of days in court cases using artificial intelligence

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Text Classification in Law Area: a Systematic Review

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