Multilingual bi-encoder models for biomedical entity linking

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dc.authoridGuven, Zekeriya Anil/0000-0002-7025-2815
dc.authorwosidGuven, Zekeriya Anil/AAO-3360-2021
dc.contributor.authorGuven, Zekeriya Anil
dc.contributor.authorLamurias, Andre
dc.date.accessioned2024-03-09T18:48:34Z
dc.date.available2024-03-09T18:48:34Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractNatural language processing (NLP) is a field of study that focuses on data analysis on texts with certain methods. NLP includes tasks such as sentiment analysis, spam detection, entity linking, and question answering, to name a few. Entity linking is an NLP task that is used to map mentions specified in the text to the entities of a Knowledge Base. In this study, we analysed the efficacy of bi-encoder entity linking models for multilingual biomedical texts. Using surface-based, approximate nearest neighbour search and embedding approaches during the candidate generation phase, accuracy, and recall values were measured on language representation models such as BERT, SapBERT, BioBERT, and RoBERTa according to language and domain. The proposed entity linking framework was analysed on the BC5CDR and Cantemist datasets for English and Spanish, respectively. The framework achieved 76.75% accuracy for the BC5CDR and 60.19% for the Cantemist. In addition, the proposed framework was compared with previous studies. The results highlight the challenges that come with domain-specific multilingual datasets.en_US
dc.identifier.doi10.1111/exsy.13388
dc.identifier.issn0266-4720
dc.identifier.issn1468-0394
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85162707084en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1111/exsy.13388
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1389
dc.identifier.volume40en_US
dc.identifier.wosWOS:001009670900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofExpert Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiomedical Entity Linking; Data Analysis; Entity Linking; Language Model; Multilingual Analysis; Natural Language Processingen_US
dc.titleMultilingual bi-encoder models for biomedical entity linkingen_US
dc.typeArticleen_US

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