人工智能在行动:用自然语言处理应对COVID-19大流行。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Qingyu Chen, Robert Leaman, Alexis Allot, Ling Luo, Chih-Hsuan Wei, Shankai Yan, Zhiyong Lu
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引用次数: 27

摘要

COVID-19(2019冠状病毒病)大流行对社会产生了重大影响,这既是因为COVID-19对健康造成严重影响,也是因为为减缓其传播而采取的公共卫生措施。其中许多困难从根本上说是信息需求;试图解决这些需求已经造成了研究人员和公众的信息过载。自然语言处理(NLP)是解释人类语言的人工智能分支,可用于解决因COVID-19大流行而迫切需要的许多信息需求。本综述调查了关于COVID-19大流行的约150项NLP研究和50多个系统和数据集。我们详细介绍了四个核心NLP任务: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、情绪和情绪分析、病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仍然存在的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing.

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.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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