Predicting Legal Proceedings Status: an Approach Based on Sequential Text Data
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Machine learning applications in the legal field are numerous and diverse. In order to make contribution to both the machine learning community and the legal community, we have made efforts to create a model compatible with the classification of text sequences, valuing the interpretability of the results. The purpose of this paper is to classify legal proceedings in three possible status classes, which are (i) archived proceedings, (ii) active proceedings and (iii) suspended proceedings. Our approach is composed by natural language processing, supervised and unsupervised deep learning models and performed remarkably well in the classification task. Furthermore we had some insights regarding the patterns learned by the neural network applying tools to make the results more interpretable.
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