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Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction

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dc.contributor.authorFabregat Marcos, Hermenegildo
dc.contributor.authorDuque Fernández, Andrés
dc.contributor.authorMartínez Romo, Juan
dc.contributor.authorAraujo Serna, M. Lourdes
dc.date.accessioned2024-06-11T15:15:29Z
dc.date.available2024-06-11T15:15:29Z
dc.date.issued2023-02
dc.description.abstractBackground and Objectives: Named Entity Recognition (NER) and Relation Extraction (RE) are two of the most studied tasks in biomedical Natural Language Processing (NLP). The detection of specific terms and entities and the relationships between them are key aspects for the development of more complex automatic systems in the biomedical field. In this work, we explore transfer learning techniques for incorporating information about negation into systems performing NER and RE. The main purpose of this research is to analyse to what extent the successful detection of negated entities in separate tasks helps in the detection of biomedical entities and their relationships. Methods: Three neural architectures are proposed in this work, all of them mainly based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks and Conditional Random Fields (CRFs). While the first architecture is devoted to detecting triggers and scopes of negated entities in any domain, two specific models are developed for performing isolated NER tasks and joint NER and RE tasks in the biomedical domain. Then, weights related to negation detection learned by the first architecture are incorporated into those last models. Two different languages, Spanish and English, are taken into account in the experiments. Results: Performance of the biomedical models is analysed both when the weights of the neural networks are randomly initialized, and when weights from the negation detection model are incorporated into them. Improvements of around 3.5% of F-Measure in the English language and more than 7% in the Spanish language are achieved in the NER task, while the NER+RE task increases F-Measure scores by more than 13% for the NER submodel and around 2% for the RE submodel. Conclusions: The obtained results allow us to conclude that negation-based transfer learning techniques are appropriate for performing biomedical NER and RE tasks. These results highlight the importance of detecting negation for improving the identification of biomedical entities and their relationships. The explored echniques show robustness by maintaining consistent results and improvements across different tasks and languages.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1016/j.jbi.2022.104279
dc.identifier.issn1532-0464
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22416
dc.journal.titleJournal of Biomedical Informatics
dc.journal.volume138
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsAttribution 3.0 Unported
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/deed.en
dc.subject.keywordsTransfer learning
dc.subject.keywordsNamed Entity Recognition
dc.subject.keywordsNegation detection
dc.subject.keywordsRelation Extraction
dc.titleNegation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extractiones
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationd6578720-2401-40cf-860c-92822eaf361a
relation.isAuthorOfPublication91b7e317-2a30-494f-98e9-3a0e026747b1
relation.isAuthorOfPublication77c4023e-4374-442a-9dfb-b9d4b609c31e
relation.isAuthorOfPublication.latestForDiscoveryd6578720-2401-40cf-860c-92822eaf361a
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