CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice.
BMC Bioinformatics
; 23(1): 210, 2022 Jun 02.
Article
in English
| MEDLINE | ID: covidwho-1874993
ABSTRACT
BACKGROUND:
Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time.METHODS:
This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics.RESULTS:
Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%.CONCLUSION:
CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Benchmarking
/
COVID-19
Type of study:
Prognostic study
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
BMC Bioinformatics
Journal subject:
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
S12859-022-04751-6
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