Information retrieval in an infodemic: the case of COVID-19 publications.
Options
BORIS DOI
Publisher DOI
PubMed ID
34375298
Description
BACKGROUND
The coronavirus disease (COVID-19) global health crisis has led to an exponential surge in the published scientific literature. In the attempt to tackle the pandemic, extremely large COVID-19-related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.
OBJECTIVE
In the context of searching for scientific evidence in the deluge of COVID-19-related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.
METHODS
Our multi-stage retrieval methodology combines probabilistic weighting models and re-ranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries are compared to documents and a series of post-processing methods are applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.
RESULTS
The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed a BM25-based baseline, retrieving on average 83% of relevant documents in the top 20.
CONCLUSIONS
These results indicate that multi-stage retrieval supported by deep learning could enhance identification of literature for COVID-19-related questions posed using natural language.
CLINICALTRIAL
The coronavirus disease (COVID-19) global health crisis has led to an exponential surge in the published scientific literature. In the attempt to tackle the pandemic, extremely large COVID-19-related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.
OBJECTIVE
In the context of searching for scientific evidence in the deluge of COVID-19-related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.
METHODS
Our multi-stage retrieval methodology combines probabilistic weighting models and re-ranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries are compared to documents and a series of post-processing methods are applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.
RESULTS
The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed a BM25-based baseline, retrieving on average 83% of relevant documents in the top 20.
CONCLUSIONS
These results indicate that multi-stage retrieval supported by deep learning could enhance identification of literature for COVID-19-related questions posed using natural language.
CLINICALTRIAL
Date of Publication
2021-09-17
Publication Type
Article
Language(s)
en
Contributor(s)
Teodoro, Douglas | |
Ferdowsi, Sohrab | |
Vicente Alvarez, David | |
Copara, Jenny | |
Gouareb, Racha | |
Naderi, Nona |
Additional Credits
Series
Journal of medical internet research
Publisher
Centre of Global eHealth Innovation
ISSN
1439-4456
Access(Rights)
open.access