A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents

• Hyan H. N. Batista UFRPE
• André C. A. Nascimento UFRPE
• Rafael Ferreira Melo UFRPE
• Péricles B. C. Miranda UFRPE
• Isabel W. S. Maldonado NESS Law
• José L. M. Coelho Filho NESS Law

DOI: https://doi.org/10.5753/eniac.2021.18255

Abstract

The initial petition is one of the most important components of a civil litigation process. Automating the analysis of these documents might reduce the time necessary for the postulatory phase's conclusion. The parties qualification body is the section in which are exposed the informations about the entities involved in the process. This paper suggests the employment of named entity extraction techniques on the problem of information extraction and recognition on initial petitions. With this in mind, was created a part description body corpora extracted from Brazilian courts. Seven BiLSTM-CRF models with distinct combinations of vector representations of words were trained, evaluated, and compared to investigate their effects on the performance of an algorithm with that architecture and, in this way, improve the recognition of legal entities in legal texts. Unlike other works based on BiLSTM-CRF for NER tasks in the legal domain, this research emphasizes not the architectures employed, but rather the text representation methods used. The experiments performed with the developed corpus show that the stacking of character, word, and pooled FLAIR embeddings is the preferred combination to extract the best possible performance from BiLSTM-CRF hybrid models.

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