{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:53:04Z","timestamp":1775760784395,"version":"3.50.1"},"reference-count":56,"publisher":"Sociedade Brasileira de Computacao - SB","issue":"1","license":[{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JBCS"],"abstract":"Named Entity Recognition (NER) is essential in Natural Language Processing (NLP) for extracting pertinent information from unstructured data. Traditional NER approaches assume continuous and non-overlapping entities, which can be limiting in real-world scenarios. This research introduces BioNestedNER, a hybrid method for nested, discontinuous, and multi-type entity recognition, with a focus on clinical and biomedical domains. Our approach employs a language model (encoder-only Transformer-based model) using a machine reading comprehension strategy, treating NER as a question-answering-like task. A Conditional Random Field also addresses multi-label sequence labeling for handling nested entities as multi-type entities. Evaluation in Portuguese demonstrated state-of-the-art performance in micro F1-Scores across two clinical corpora. In NestedClinBr, featuring nested and discontinuous entities, our method achieved an F1-Score of 0.863, surpassing the second-place result by 2.1%. In SemClinBr, with multi-type entities, an F1-Score of 0.782 was achieved, surpassing the second-place result by 11.5%. This paper also presents a new clinical corpus in Brazilian Portuguese annotated with nested and discontinuous entities, offering a valuable resource for developing and evaluating models handling these complex entities. In conclusion, BioNestedNER presents an adaptable and effective NER solution for nested, discontinuous, and multi-type entities, with the potential to benefit various clinical applications.<\/jats:p>","DOI":"10.5753\/jbcs.2026.5790","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:57:32Z","timestamp":1775757452000},"page":"635-648","source":"Crossref","is-referenced-by-count":0,"title":["BioNestedNER: A Hybrid Language Model Approach for Recognizing Nested, Discontinuous, and Multi-Type Named Entities"],"prefix":"10.5753","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-5598","authenticated-orcid":false,"given":"Elisa Terumi Rubel","family":"Schneider","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8239-2930","authenticated-orcid":false,"given":"Yohan Bonescki","family":"Gumiel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3013-3771","authenticated-orcid":false,"given":"Paloma","family":"Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2637-3086","authenticated-orcid":false,"given":"Claudia","family":"Moro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6740-7855","authenticated-orcid":false,"given":"Emerson Cabrera","family":"Paraiso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2026,4,5]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Alex, B., Haddow, B., and Grover, C. 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