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dc.contributor.author | Rosso-Mateus, Andrés![]() |
es_ES |
dc.contributor.author | Montes Gomez, Manuel![]() |
es_ES |
dc.contributor.author | Rosso, Paolo![]() |
es_ES |
dc.contributor.author | González, Fabio![]() |
es_ES |
dc.date.accessioned | 2021-05-27T03:34:10Z | |
dc.date.available | 2021-05-27T03:34:10Z | |
dc.date.issued | 2020-08-31 | es_ES |
dc.identifier.issn | 1064-1246 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/166829 | |
dc.description.abstract | [EN] Passage retrieval is an important stage of question answering systems. Closed domain passage retrieval, e.g. biomedical passage retrieval presents additional challenges such as specialized terminology, more complex and elaborated queries, scarcity in the amount of available data, among others. However, closed domains also offer some advantages such as the availability of specialized structured information sources, e.g. ontologies and thesauri, that could be used to improve retrieval performance. This paper presents a novel approach for biomedical passage retrieval which is able to combine different information sources using a similarity matrix fusion strategy based on convolutional neural network architecture. The method was evaluated over the standard BioASQ dataset, a dataset specialized on biomedical question answering. The results show that the method is an effective strategy for biomedical passage retrieval able to outperform other state-of-the-art methods in this domain. | es_ES |
dc.description.sponsorship | COLCIENCIAS, REF. Agreement #727, 2016 provided financial as well as logistical and planning support. Mindlab research group (Universidad Nacional de Colombia sede Bogota) with the cooperation of INAOE (Instituto Nacional de Astrofisica, optica y Electronica) and Universitat Politecnica de Valencia wich also provided technical support for this work. The work of Paolo Rosso was carried out in the framework of the research project PROMETEO/2019/121. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IOS Press | es_ES |
dc.relation.ispartof | Journal of Intelligent & Fuzzy Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Biomedical passage retrieval | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Question answering | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3233/JIFS-179887 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/COLCIENCIAS//727/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F121/ES/Deep learning for adaptative and multimodal interaction in pattern recognition/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Rosso-Mateus, A.; Montes Gomez, M.; Rosso, P.; González, F. (2020). Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval. Journal of Intelligent & Fuzzy Systems. 39(2):2239-2248. https://doi.org/10.3233/JIFS-179887 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3233/JIFS-179887 | es_ES |
dc.description.upvformatpinicio | 2239 | es_ES |
dc.description.upvformatpfin | 2248 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 39 | es_ES |
dc.description.issue | 2 | es_ES |
dc.relation.pasarela | S\433824 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Departamento Administrativo de Ciencia, Tecnología e Innovación, Colombia | es_ES |
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