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

Fabregat, Hermenegildo, Duque, Andrés, Martinez-Romo, Juan y Araujo, Lourdes . (2023) Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction. Journal of Biomedical Informatics 104279

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Título Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction
Autor(es) Fabregat, Hermenegildo
Duque, Andrés
Martinez-Romo, Juan
Araujo, Lourdes
Materia(s) Biomedicina
Ingeniería Informática
Abstract Background 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.
Palabras clave Transfer learning
Named Entity Recognition
Negation detection
Relation Extraction
Editor(es) Elsevier
Fecha 2023-02
Formato application/pdf
Identificador bibliuned:DptoLSI-ETSI-Articulos-Aduque-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoLSI-ETSI-Articulos-Aduque-0001
DOI - identifier https://doi.org/10.1016/j.jbi.2022.104279
ISSN - identifier 1532-0464
Nombre de la revista Journal of Biomedical Informatics
Número de Volumen 138
Publicado en la Revista Journal of Biomedical Informatics 104279
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso bookPart
Derechos de acceso y licencia http://creativecommons.org/licenses/by/3.0/deed.en
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Journal of Biomedical Informatics (2023) 138 104279, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.jbi.2022.104279
Notas adicionales The registered version of this article, first published in the Journal of Biomedical Informatics (2023) 138 104279, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.jbi.2022.104279

 
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Creado: Fri, 22 Mar 2024, 21:12:47 CET