Data augmentation and transfer learning for cross-lingual Named Entity Recognition in the biomedical domain.
Given the increase in production of data for the biomedical field and the unstoppable growth of the internet, the need for Information Extraction (IE) techniques has skyrocketed. Named Entity Recognition (NER) is one of such IE tasks useful for professionals in different areas. There are several set...
| Published in: | Language Resources & Evaluation Vol. 59; no. 2; pp. 665 - 685 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
Jun2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=185240035&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185240035 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2025 vid: 59 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 185240035 10.1007/s10579-024-09738-8 ppf: 665 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P size: 937KB tig: atl: Data augmentation and transfer learning for cross-lingual Named Entity Recognition in the biomedical domain. aug: au: Lancheros, Brayan Stiven Corpas Pastor, Gloria Mitkov, Ruslan affil: https://ror.org/01k2y1055 University of Wolverhampton, Wolverhampton, UK https://ror.org/036b2ww28 Universidad de Malaga, IUITLM, Malaga, Spain https://ror.org/04f2nsd36 Lancaster University, Lancaster, UK su: Machine translating Artificial intelligence Data augmentation Data mining Spanish language sug: subj: Machine translating Artificial intelligence Data augmentation Data mining Spanish language keyword: Biomedical NER Information and Computing Sciences Artificial Intelligence and Image Processing Named entity recognition Spanish ab: Given the increase in production of data for the biomedical field and the unstoppable growth of the internet, the need for Information Extraction (IE) techniques has skyrocketed. Named Entity Recognition (NER) is one of such IE tasks useful for professionals in different areas. There are several settings where biomedical NER is needed, for instance, extraction and analysis of biomedical literature, relation extraction, organisation of biomedical documents, and knowledge-base completion. However, the computational treatment of entities in the biomedical domain has faced a number of challenges including its high cost of annotation, ambiguity, and lack of biomedical NER datasets in languages other than English. These difficulties have hampered data development, affecting both the domain itself and its multilingual coverage. The purpose of this study is to overcome the scarcity of biomedical data for NER in Spanish, for which only two datasets exist, by developing a robust bilingual NER model. Inspired by back-translation, this paper leverages the progress in Neural Machine Translation (NMT) to create a synthetic version of the Colorado Richly Annotated Full-Text (CRAFT) dataset in Spanish. Additionally, a new CRAFT dataset is constructed by replacing 20% of the entities in the original dataset generating a new augmented dataset. We evaluate two training methods: concatenation of datasets and continuous training to assess the transfer learning capabilities of transformers using the newly obtained datasets. The best performing NER system in the development set achieved an F-1 score of 86.39%. The novel methodology proposed in this paper presents the first bilingual NER system and it has the potential to improve applications across under-resourced languages. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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