# COV19IR : COVID-19 Domain Literature Information Retrieval

Source: [COV19IR : COVID-19 Domain Literature Information Retrieval](<https://www.sota2.com/research/paper/68fa6dd81e209131cea3c5bd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:44:11.594Z

Authors: Arusarka Bose, Zili Zhou, Guandong Xu (3) \_\_INSTITUTION\_3\_\_ Indian Institute of Technology Kharagpur, (2) University of Manchester, (3) University of Technology Sydney)

Published: 2022-11-08T05:12:37.000Z

## Abstract

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.04013>)

[PDF](<https://arxiv.org/pdf/2211.04013.pdf>)