COV19IR : COVID-19 Domain Literature Information Retrieval

• URL: http://arxiv.org/abs/2211.04013v1
• Date: Tue, 8 Nov 2022 05:12:37 GMT
• Title: COV19IR : COVID-19 Domain Literature Information Retrieval
• Authors: Arusarka Bose (1), Zili Zhou (2), Guandong Xu (3) ((1) Indian Institute of Technology Kharagpur, (2) University of Manchester, (3) University of Technology Sydney)
• Abstract summary: We demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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