Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)

TL;DR

This paper reviews how natural language processing (NLP), a branch of AI, has been applied to address urgent information needs during the COVID-19 pandemic, covering tasks like information retrieval, sentiment analysis, and misinformation detection.

Contribution

It provides a comprehensive survey of approximately 150 NLP studies and 50 systems/datasets related to COVID-19, highlighting core tasks and recent trends.

Findings

NLP techniques have been widely used for COVID-19 information retrieval and analysis.

Significant progress in misinformation detection and sentiment analysis during the pandemic.

Remaining challenges include data quality and adapting models to evolving information.

Abstract

The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP), the branch of artificial intelligence that interprets human language, can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.