Evaluation of Named Entity Recognition using Ensemble in Transformers Models for Brazilian Public Texts - Sirqueira, Eutino Júnior Vieira; Vidal, Flávio de Barros (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.245227, 2024)
Natural Language Processing (NLP) has experienced significant advances, driven mainly by developing deep learning models using Transformers. In the Brazilian context, the analysis of open data, such as official documents published in the Official Federal Gazette (DOU), is crucial for transparency and access to information. In this work, we propose an evaluation of ensemble models, using Transformers models, applied for the Named Entity Recognition (NER) task in Brazilian Public Texts. The proposed evaluation tested a set of models based on the Bidirectional Encoder Representations from Transformers (BERT) model variations and combinations of ensemble strategies, reaching improvements of up to 11% in the proposed corpus when compared with classic NER approaches using only BERT-based models.