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      Portuguese Named Entity Recognition using BERT-CRF

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          Abstract

          Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of a trained model to downstream natural language processing tasks, such as named entity recognition (NER) and question answering. It has been shown that the leverage of pre-trained language models improves the overall performance on many tasks and is highly beneficial when labeled data is scarce. In this work, we employ a pre-trained BERT with Conditional Random Fields (CRF) architecture to the NER task on the Portuguese language, combining the transfer capabilities of BERT with the structured predictions of CRF. We explore feature-based and fine-tuning training strategies for the BERT model. Our fine-tuning approach obtains new state-of-the-art results on the HAREM I dataset, improving the F1-score by 3.2 points on the selective scenario (5 NE classes) and by 3.8 points on the total scenario (10 NE classes).

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          Most cited references 4

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          Named Entity Recognition: Fallacies, challenges and opportunities

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            LeNER-Br A Dataset for Named Entity Recognition in Brazilian Legal Text

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              Applying Deep Neural Networks to Named Entity Recognition in Portuguese Texts

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                Author and article information

                Journal
                23 September 2019
                Article
                1909.10649

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CL cs.IR cs.LG

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