인터넷정보학회논문지 (Journal of Internet Computing and Services)

  • 제18권6호
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  • Pages.55-63
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  • 2017
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  • 1598-0170(pISSN)
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  • 2287-1136(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지
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An Efficient Damage Information Extraction from Government Disaster Reports

  • Shin, Sungho (Decision Support Technology Lab., Korea Institute of Science and Technology Information) ;
  • Hong, Seungkyun (Department of Big Data Analysis, Korea University of Science and Technology) ;
  • Song, Sa-Kwang (Decision Support Technology Lab., Korea Institute of Science and Technology Information)
  • 투고 : 2017.06.23
  • 심사 : 2017.10.17
  • 발행 : 2017.12.31
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초록

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

키워드

참고문헌

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