A Question-Answering System on COVID-19 Scientific Literature
46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
; : 1331-1336, 2022.
Article
in English
| Scopus | ID: covidwho-2018655
ABSTRACT
The vast amount of COVID-19 research literature has made it difficult for medical experts, clinical scientists, and researchers to keep up with the latest research findings. We present two datasets for COVID-19 in this work (1) first, we create a dataset from the up-to-date scientific publications on COVID-19, and (2) second, we build a gold-standard dataset of question-answering pairs annotated by volunteer biomedical experts on COVID-19 related scientific articles. We develop a question-answering (QA) pipeline that uses the first dataset to provide answers related to COVID-19 questions;we fine-tune MPNet (a Transformer model) on our gold-standard dataset and use it in the QA pipeline to enhance its reading capability. We also use this gold-standard dataset to evaluate the QA pipeline. The proposed MPNet version on the gold-standard dataset outperformed previous datasets and models, achieving an Exact Match/Fl score of 69.72/78.50 %, respectively © 2022 IEEE.
Deep neural networks; gold-standard dataset; pipeline; question-answering system; transfer learning; Clinical research; Pipelines; Gold standards; Medical experts; Question Answering; Question answering systems; Scientific articles; Scientific literature; Scientific publications; Transformer modeling; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS