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The objective of this paper is to develop predictive models to classifyBrazilian legal proceedings in three possible classes of status: (i) archivedproceedings, (ii) active proceedings, and (iii) suspended proceedings. Thisproblem's resolution is intended to assist public and private institutions inmanaging large portfolios of legal proceedings, providing gains in scale andefficiency. In this paper, legal proceedings are made up of sequences of shorttexts called "motions." We combined several natural language processing (NLP)and machine learning techniques to solve the problem. Although working withPortuguese NLP, which can be challenging due to lack of resources, ourapproaches performed remarkably well in the classification task, achievingmaximum accuracy of .93 and top average F1 Scores of .89 (macro) and .93(weighted). Furthermore, we could extract and interpret the patterns learned byone of our models besides quantifying how those patterns relate to theclassification task. The interpretability step is important among machinelearning legal applications and gives us an exciting insight into how black-boxmodels make decisions.

Predicting Legal Proceedings Status: Approaches Based on Sequential Text Data