论文标题
行动中的人工智能（AI）：与自然语言处理（NLP）解决COVID-19
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
论文作者
论文摘要
199年的大流行对社会产生了重大影响，这是由于COVID-19的严重影响以及采取公共卫生措施以减缓其传播。这些困难中有许多是从根本上信息需求。解决这些需求的尝试导致了研究人员和公众的信息超负荷。自然语言处理（NLP）是解释人类语言的人工智能的分支，可以应用于COVID-19-19的大流行迫切需要的许多信息需求。这篇综述调查了大约150个NLP研究，以及针对COVID-19大流行的50多个系统和数据集。我们详细介绍了四个核心NLP任务的工作：信息检索，命名实体识别，基于文献的发现和问题回答。我们还描述了通过四个其他任务直接解决大流行方面的工作：主题建模，情感和情感分析，案件预测和错误信息检测。我们通过讨论可观察到的趋势和剩余挑战来结束。
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.
