Abstract
The Brazilian judicial system is currently one of the largest in the world with more than 77 million legal cases awaiting decision. The use of machine learning could help to improve celerity through text classification. This paper aims to compare some supervised machine processing techniques. TF-IDF text representation was used. The paper discusses comparison among classification methods such as Random Forest, Adaboost using decision trees, Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Multilayer Perceptron. The data set consists of 30,000 documents distributed among ten classes, which represent possible procedural movements resulting from court decisions. The classification results are quite satisfactory since some techniques were able to overcome a f1-score of 90%.
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Araújo, D.C., Lima, A., Lima, J.P., Costa, J.A. (2021). A Comparison of Classification Methods Applied to Legal Text Data. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_6
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