Building an intelligent system for answering specialized questions about COVID-19

The paper discusses the design and implementation process of an intelligent system for answering specialized questions about COVID-19. The system is based on deep learning and transfer learning techniques and uses the popular CORD-19 dataset as a source of scientific knowledge about the problem doma...

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Detalles Bibliográficos
Autores principales: Dobreva, Bilyana, Nisheva-Pavlova, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030178/
https://www.ncbi.nlm.nih.gov/pubmed/36968672
http://dx.doi.org/10.1016/j.procs.2023.01.304
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author Dobreva, Bilyana
Nisheva-Pavlova, Maria
author_facet Dobreva, Bilyana
Nisheva-Pavlova, Maria
author_sort Dobreva, Bilyana
collection PubMed
description The paper discusses the design and implementation process of an intelligent system for answering specialized questions about COVID-19. The system is based on deep learning and transfer learning techniques and uses the popular CORD-19 dataset as a source of scientific knowledge about the problem domain. The experiments performed with the pilot version of the system are presented and the obtained results are analyzed. Conclusions are formulated about the applicability and the opportunities for improvement of the proposed approach.
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spelling pubmed-100301782023-03-22 Building an intelligent system for answering specialized questions about COVID-19 Dobreva, Bilyana Nisheva-Pavlova, Maria Procedia Comput Sci Article The paper discusses the design and implementation process of an intelligent system for answering specialized questions about COVID-19. The system is based on deep learning and transfer learning techniques and uses the popular CORD-19 dataset as a source of scientific knowledge about the problem domain. The experiments performed with the pilot version of the system are presented and the obtained results are analyzed. Conclusions are formulated about the applicability and the opportunities for improvement of the proposed approach. The Author(s). Published by Elsevier B.V. 2023 2023-03-22 /pmc/articles/PMC10030178/ /pubmed/36968672 http://dx.doi.org/10.1016/j.procs.2023.01.304 Text en © 2023 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Dobreva, Bilyana
Nisheva-Pavlova, Maria
Building an intelligent system for answering specialized questions about COVID-19
title Building an intelligent system for answering specialized questions about COVID-19
title_full Building an intelligent system for answering specialized questions about COVID-19
title_fullStr Building an intelligent system for answering specialized questions about COVID-19
title_full_unstemmed Building an intelligent system for answering specialized questions about COVID-19
title_short Building an intelligent system for answering specialized questions about COVID-19
title_sort building an intelligent system for answering specialized questions about covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030178/
https://www.ncbi.nlm.nih.gov/pubmed/36968672
http://dx.doi.org/10.1016/j.procs.2023.01.304
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