Assessment of text clustering approaches for legal documents

• Ingrid L. A. da Silva UFRPE
• Rafael Ferreira Mello UFRPE
• Péricles B. C. Miranda UFRPE
• André C. A. Nascimento UFRPE
• Isabel W. S. Maldonado NESS Law
• José L. M. Coelho Filho NESS Law

DOI: https://doi.org/10.5753/eniac.2021.18239

Abstract

The judicial system is composed of numerous court documents. These documents may contain relevant information that supports decision-making in future processes. However, collecting this information is not a trivial task. This article proposes the use of clustering techniques to group similar court lawsuits and facilitate the collection of information. In this way, different approaches were evaluated for identifying the most appropriate to perform this task. The approaches were applied to a database composed of 1515 facts of initial petitions. These approaches were evaluated using internal metrics and texts of the grouped court lawsuits. The results showed that the best approach to grouping court lawsuits is composed of the K-Means algorithm and the TF-IDF representation technique in combination with the PCA technique.

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