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

Abstract

With the abundance of electronic lawsuits already implemented throughout Brazil, courts have a valuable source of information in text format that constitute attractive bases for the application of Artificial Intelligence (AI) and machine learning (ML). In this research, supervised learning approaches were explored for the automatic classification of types of documents in electronic court proceedings of the Court of Justice of Minas Gerais (TJMG). The methodology is composed of cross-validation within the specific corpus of the legal domain, comparing traditional classifiers and more recent methods based on neural networks and deep learning models, using Glove word vectors generated for the Portuguese Language and Convolutional Neural Network (CNN). This work achieved high precision in the results and if implemented in the courts it can provide significant savings in financial and human resources, allowing lawsuits classification activities, currently done manually by employees, to be performed in seconds by the machine. The result of this experiment shows that the hit rates for the CNN and SVM classifiers exceed 93% and is considered a high result. Based on the assumption that Glove brings extra semantic resources that can help in classifying texts from court proceedings, this work demonstrates Glove’s effectiveness by showing that a CNN with Glove surpasses SVM.