| Record |
Author  |
Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang |
| 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 |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
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| 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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