LegalNLP - Natural Language Processing methods for the Brazilian Legal Language

  • Felipe Maia Polo University of Michigan
  • Gabriel Caiaffa Floriano Mendonça USP
  • Kauê Capellato J. Parreira USP
  • Lucka Gianvechio USP
  • Peterson Cordeiro USP
  • Jonathan Batista Ferreira USP
  • Leticia Maria Paz de Lima USP
  • Antônio Carlos do Amaral Maia Tikal Tech
  • Renato Vicente USP / Latam Datalab Serasa Experian

Resumo


We present and make available pre-trained language models (Phraser, Word2Vec, Doc2Vec, FastText, and BERT) for the Brazilian legal language, a Python package with functions to facilitate their use, and a set of demonstrations/tutorials containing some applications involving them. Given that our material is built upon legal texts coming from several Brazilian courts, this initiative is extremely helpful for the Brazilian legal field, which lacks other open and specific tools and language models. Our main objective is to catalyze the use of natural language processing tools for legal texts analysis by the Brazilian industry, government, and academia, providing the necessary tools and accessible material.

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Publicado
29/11/2021
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POLO, Felipe Maia et al. LegalNLP - Natural Language Processing methods for the Brazilian Legal Language. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 763-774. DOI: https://doi.org/10.5753/eniac.2021.18301.