Emerson Cabrera Paraiso

Also published as: Emerson Paraiso


UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition
Elisa Schneider | Renzo M. Rivera-Zavala | Paloma Martinez | Claudia Moro | Emerson Paraiso
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data.


BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition
Elisa Terumi Rubel Schneider | João Vitor Andrioli de Souza | Julien Knafou | Lucas Emanuel Silva e Oliveira | Jenny Copara | Yohan Bonescki Gumiel | Lucas Ferro Antunes de Oliveira | Emerson Cabrera Paraiso | Douglas Teodoro | Cláudia Maria Cabral Moro Barra
Proceedings of the 3rd Clinical Natural Language Processing Workshop

With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.


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Estudo exploratório de categorias gramaticais com potencial de indicadores para a Análise de Sentimentos (An Exploratory study of grammatical categories as potential indicators for Sentiment Analysis)[In Portuguese]
Júlia Rodrigues | Adriana Pagano | Emerson Paraiso
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology


Anotando um Corpus de Notícias para a Análise de Sentimentos: um Relato de Experiência (Annotating a corpus of News for Sentiment Analysis: An Experience Report)
Mariza Miola Dosciatti | Lohann Paterno Coutinho Ferreira | Emerson Cabrera Paraiso
Proceedings of the 10th Brazilian Symposium in Information and Human Language Technology