{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/biobertpt-a-portuguese-neural-language-model","title":"BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition","arxiv_id":null,"date":"2020-11-01","proceeding":"EMNLP (ClinicalNLP) 2020 11","authors":["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"],"abstract":"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.","url_abs":"https://aclanthology.org/2020.clinicalnlp-1.7","url_pdf":"https://aclanthology.org/2020.clinicalnlp-1.7.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-ner-on-semclinbr","task":"Named Entity Recognition (NER)","dataset":"SemClinBr","model":"pucpr/biobertpt-clin","rank_in_archive_order":1,"of":3,"metrics":{"Micro F1":"0.602"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-semclinbr","task":"Named Entity Recognition (NER)","dataset":"SemClinBr","model":"pucpr/biobertpt-bio","rank_in_archive_order":2,"of":3,"metrics":{"Micro F1":"0.602"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-semclinbr","task":"Named Entity Recognition (NER)","dataset":"SemClinBr","model":"pucpr/biobertpt-all","rank_in_archive_order":3,"of":3,"metrics":{"Micro F1":"0.604"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}