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COVID-19 recommender system based on an annotated multilingual corpus

Tracking the most recent advances in Coronavirus disease 2019 (COVID-19)‒related research is essential, given the disease's novelty and its impact on society. However, with the publication pace speeding up, researchers and clinicians require automatic approaches to keep up with the incoming inf...

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Detalles Bibliográficos
Autores principales: Barros, Márcia, Ruas, Pedro, Sousa, Diana, Bangash, Ali Haider, Couto, Francisco M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korea Genome Organization 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510867/
https://www.ncbi.nlm.nih.gov/pubmed/34638171
http://dx.doi.org/10.5808/gi.21008
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author Barros, Márcia
Ruas, Pedro
Sousa, Diana
Bangash, Ali Haider
Couto, Francisco M.
author_facet Barros, Márcia
Ruas, Pedro
Sousa, Diana
Bangash, Ali Haider
Couto, Francisco M.
author_sort Barros, Márcia
collection PubMed
description Tracking the most recent advances in Coronavirus disease 2019 (COVID-19)‒related research is essential, given the disease's novelty and its impact on society. However, with the publication pace speeding up, researchers and clinicians require automatic approaches to keep up with the incoming information regarding this disease. A solution to this problem requires the development of text mining pipelines; the efficiency of which strongly depends on the availability of curated corpora. However, there is a lack of COVID-19‒related corpora, even more, if considering other languages besides English. This project's main contribution was the annotation of a multilingual parallel corpus and the generation of a recommendation dataset (EN-PT and EN-ES) regarding relevant entities, their relations, and recommendation, providing this resource to the community to improve the text mining research on COVID-19‒related literature. This work was developed during the 7th Biomedical Linked Annotation Hackathon (BLAH7).
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spelling pubmed-85108672021-10-22 COVID-19 recommender system based on an annotated multilingual corpus Barros, Márcia Ruas, Pedro Sousa, Diana Bangash, Ali Haider Couto, Francisco M. Genomics Inform Blah7 Tracking the most recent advances in Coronavirus disease 2019 (COVID-19)‒related research is essential, given the disease's novelty and its impact on society. However, with the publication pace speeding up, researchers and clinicians require automatic approaches to keep up with the incoming information regarding this disease. A solution to this problem requires the development of text mining pipelines; the efficiency of which strongly depends on the availability of curated corpora. However, there is a lack of COVID-19‒related corpora, even more, if considering other languages besides English. This project's main contribution was the annotation of a multilingual parallel corpus and the generation of a recommendation dataset (EN-PT and EN-ES) regarding relevant entities, their relations, and recommendation, providing this resource to the community to improve the text mining research on COVID-19‒related literature. This work was developed during the 7th Biomedical Linked Annotation Hackathon (BLAH7). Korea Genome Organization 2021-09-30 /pmc/articles/PMC8510867/ /pubmed/34638171 http://dx.doi.org/10.5808/gi.21008 Text en (c) 2021, Korea Genome Organization https://creativecommons.org/licenses/by/4.0/(CC) This is an open-access article distributed under the terms of the Creative Commons Attribution license(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Blah7
Barros, Márcia
Ruas, Pedro
Sousa, Diana
Bangash, Ali Haider
Couto, Francisco M.
COVID-19 recommender system based on an annotated multilingual corpus
title COVID-19 recommender system based on an annotated multilingual corpus
title_full COVID-19 recommender system based on an annotated multilingual corpus
title_fullStr COVID-19 recommender system based on an annotated multilingual corpus
title_full_unstemmed COVID-19 recommender system based on an annotated multilingual corpus
title_short COVID-19 recommender system based on an annotated multilingual corpus
title_sort covid-19 recommender system based on an annotated multilingual corpus
topic Blah7
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510867/
https://www.ncbi.nlm.nih.gov/pubmed/34638171
http://dx.doi.org/10.5808/gi.21008
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