  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.
