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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korea Genome Organization
2021
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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). |
format | Online Article Text |
id | pubmed-8510867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Korea Genome Organization |
record_format | MEDLINE/PubMed |
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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