The analysis of judicial processes is an expensive task, requiring a long time of judge and advisors, either to make decisions or to classify according to the current jurisprudence. However, this process is repetitive and extracting the semantics of this corpus can be a step to support this process. The purpose of this research is to develop a methodology able of automatically generating classifications of legal documents, making use of techniques of natural language processing. Firstly, we collected 430,000 Brazilian labor court judgments from 2006 to 2018. Secondly, we propose the use of word embedding techniques for data representation. Thirdly, we use clustering techniques to semantically group the similar judicial decisions. Fourth, the clusters are used to create artificial labels for each document. Finally, we use classification techniques to produce models able to capture the semantics of judicial text. Results show a promise towards capturing the semantic context of legal texts and thus, this methodology may be used as support for the Brazilian decision-making process.
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