On the Assessment of Deep Learning Models for Named Entity Recognition of Brazilian Legal Documents

Hidelberg Oliveira Albuquerque, Ellen Souza, Adriano L. I. Oliveira, David Macêdo, Cleber Zanchettin, Douglas Vitório, Nádia Félix F. da Silva, André C. P. L. F. de Carvalho

Abstract: A large amount of legal and legislative documents are generated every year with highly specialized content and significant repercussions on society. Besides technical, the produced information is not semantically standardized or format structured. Automating the document analysis, categorization, search, and summarization is essential. The Named Entity Recognition (NER) task is one of the tools that have the potential to extract information from legal documents with efficiency. This paper evaluates the state-of-the-art NER models BiLSTM+CRF and BERT+Fine-Tunning trained on Portuguese corpora through finetuning in the legal and legislative domains. The obtained results (F1-scores of 83.17% and 88.27%) suggest that the BERT model is superior, achieving better average results.