Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing
Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing
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DOI：
10.1146/annurev-biodatasci-021821-061045
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发表时间：
2021-01-01
期刊：
影响因子：
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通讯作者：
Lu, Zhiyong
中科院分区：
文献类型：
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作者：
Chen, Qingyu;Leaman, Robert;Lu, Zhiyong
COVID-19（2019冠状病毒病）大流行对社会产生重大影响，原因包括COVID-19对健康造成严重影响，以及为减缓其传播而实施的公共卫生措施。许多这些困难基本上是信息需求;试图解决这些需求造成了研究人员和公众的信息过载。自然语言处理（NLP）--解释人类语言的人工智能的分支--可以应用于解决COVID-19大流行所迫切需要的许多信息。本综述调查了约150项NLP研究以及50多个针对COVID-19大流行的系统和数据集。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答。我们还描述了通过四项额外任务直接解决大流行方面的工作：主题建模，情绪和情感分析，案例量预测和错误信息检测。最后，我们讨论了可观察到的趋势和剩余的挑战。
The COVID-19 (coronavirus disease 2019) 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.