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Author (up) Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang pdf  openurl
  Title A Question Answering System Applied to Disasters Type Conference Article
  Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021  
  Volume Issue Pages 2-16  
  Keywords Emergency Management, Disaster, Natural Language Processing, Deep Learning  
  Abstract In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph.  
  Address Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-61-5 ISBN Medium  
  Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management  
  Notes gxj19@mails.tsinghua.edu.cn Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2308  
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