  The objective of this paper is to develop predictive models to classify
Brazilian legal proceedings in three possible classes of status: (i) archived
proceedings, (ii) active proceedings, and (iii) suspended proceedings. This
problem's resolution is intended to assist public and private institutions in
managing large portfolios of legal proceedings, providing gains in scale and
efficiency. In this paper, legal proceedings are made up of sequences of short
texts called "motions." We combined several natural language processing (NLP)
and machine learning techniques to solve the problem. Although working with
Portuguese NLP, which can be challenging due to lack of resources, our
approaches performed remarkably well in the classification task, achieving
maximum accuracy of .93 and top average F1 Scores of .89 (macro) and .93
(weighted). Furthermore, we could extract and interpret the patterns learned by
one of our models besides quantifying how those patterns relate to the
classification task. The interpretability step is important among machine
learning legal applications and gives us an exciting insight into how black-box
models make decisions.
