COV19IR : COVID-19 Domain Literature Information Retrieval
Bose, Arusarka, Zhou, Zili, Xu, Guandong
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Nov-8-2022
- Country:
- Africa > Middle East (0.04)
- Asia
- India > West Bengal
- Kharagpur (0.04)
- Japan (0.04)
- Middle East > Saudi Arabia (0.04)
- Russia (0.04)
- India > West Bengal
- Europe
- Italy (0.04)
- Middle East (0.04)
- Russia (0.04)
- Spain (0.04)
- Sweden (0.04)
- United Kingdom > England
- Greater Manchester > Manchester (0.04)
- Oceania > Australia
- New South Wales > Sydney (0.04)
- Genre:
- Research Report (1.00)
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- Technology: