Computer-Assisted Databasing of Disaster Management Information Through Natural Language Processing

Kentaro Inui; Yotaro Watanabe; Kenshi Yamaguchi; Shingo Suzuki; Hiroko Koumoto; Naoko Kosaka; Akira Koyama; Tomohiro Kokogawa; Yuji Maeda

doi:10.20965/jdr.2015.p0830

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JDR Vol.10 No.5 pp. 830-844

(2015)

doi: 10.20965/jdr.2015.p0830

Paper:

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Computer-Assisted Databasing of Disaster Management Information Through Natural Language Processing

Kentaro Inui^1, Yotaro Watanabe^2, Kenshi Yamaguchi^1, Shingo Suzuki^3, Hiroko Koumoto^4, Naoko Kosaka^5, Akira Koyama^5, Tomohiro Kokogawa^5, and Yuji Maeda^*5

^*1Graduate School of Information Science, Tohoku University
Aoba, Sendai, Japan

^*2Knowledge Discovery Research Laboratories, NEC Corporation, Japan

^*3Disaster Prevention Research Institute, Kyoto University, Japan

^*4Graduate School of Environment and Disaster Research, Tokoha University, Japan

^*5Secure Platform Laboratories, Nippon Telegraph and Telephone Corporation, Japan

Received:

June 8, 2015

Accepted:

September 1, 2015

Published:

October 1, 2015

Keywords:

computer-assisted databasing, natural language processing

Abstract

During times of disaster, local government departments and divisions need to communicate a broad range of information for disaster management to share the understating of the changing situation. This paper addresses the issues of how to effectively use a computer database system to communicate disaster management information and how to apply natural language processing technology to reduce the human labor for databasing a vast amount of information. The database schema was designed based on analyzing a collection of real-life disaster management information and the specifications of existing standardized systems. Our data analysis reveals that our database schema sufficiently covers the information exchanged in a local government during the Great East Earthquake. Our prototype system is designed so as to allow local governments to introduce it at a low cost: (i) the system’s user interface facilitates the operations for databasing given information, (ii) the system can be easily customized to each local municipality by simply replacing the dictionary and the sample data for training the system, and (iii) the system can be automatically adapted to each local municipality or each disaster incident through its capability of automatic learning from the user’s corrections to the system’s language processing outputs.

Cite this article as:

K. Inui, Y. Watanabe, K. Yamaguchi, S. Suzuki, H. Koumoto, N. Kosaka, A. Koyama, T. Kokogawa, and Y. Maeda, “Computer-Assisted Databasing of Disaster Management Information Through Natural Language Processing,” J. Disaster Res., Vol.10 No.5, pp. 830-844, 2015.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] D. Feng, G. Burns, and E. Hovy, “Extracting data records from unstructured biomedical full text,” EMNLP-CoNLL 2007, 2007.

[2] [2] M. Higashida, M. Sugiyama, H. Takeda, T. Yamamoto, Y. Maeda, and H. Hayashi, “Analysis of Information Processing Patterns Appeared at Emergency Operation Center Training,” Outline of Social Safety and Science, Vol.30, 2012.

[3] [3] F. Ichinose, Y. Maeda, N. Kosaka, M. Higashida, M. Sugiyama, H. Takeda, T. Yamamoto, and H. Hayashi, “A Fundamental Study of Efficiency of Information Processing in Emergency Operations Center,” Journal of Disaster Research, Vol.9, No.2, pp. 206-215, 2014.

[4] [4] J. Kazama, S. D. Saeger, K. Kuroda, M. Murata, and K. Torisawa, “A Bayesian Method for Robust Estimation of Distributional Similarities,” Proc. of ACL 2010, 2010.

[5] [5] T. Kudo and Y. Matsumoto, “Japanese Dependency Analysis using Cascaded Chunking,” Proc. of CoNLL 2002, 2002.

[6] [6] T. Kudo, K. Yamamoto, and Y. Matsumoto, “Appliying Conditional Random Fields to Japanese Morphological Analysis,” Proc. of EMNLP 2004, 2004.

[7] [7] J. Lafferty, A. McCallum, and F. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proc. of ICML-2001, 2001.

[8] [8] Q. Li, H. Ji, and L. Huang, “Joint Event Extraction via Structured Prediction with Global Features,” Proc. of ACL 2013, 2013.

[9] [9] R. McDonald, F. Pereira, S. Kulick, S. Winters, Y. Jin, and P. White, “Simple algorithms for complex relation extraction with applications to biomedical IE,” Proc. of ACL 2005, 2005.

[10] [10] R. T. Mcdonald, R. S. Winters, M. Mandel, Y. Jin, P. S. White, and F. Pereira, “An Entity Tagger for Recognizing Acquired Genomic Variations in Cancer Literature,” Bioinformatics, Vol.20, No.17, 2004.

[11] [11] E. Minkov and L. Zettlemoyer, “Discriminative learning for joint template filling,” Proc. of ACL 2012, 2012.

[12] [12] N. Okazaki, “CRFsuite: a fast implementation of Conditional Random Fields (CRFs),” 2007.

[13] [13] S. Sekine, K. Sudo, and C. Nobata, “Extended Named Entity Hierarchy,” Proc. of LREC 2002, 2002.

[14] [14] T. Hashimoto and S. Nakamura, “Construction of a corpus with an extended named entity tag – White paper, books, core data of Yahoo! Answers,” Proceedings of the 16^th annuarl conference of the Association for Natural Language Processing, pp. 916–919, 2008 (in Japanese).

Computer-Assisted Databasing of Disaster Management Information Through Natural Language Processing

Kentaro Inui*1, Yotaro Watanabe*2, Kenshi Yamaguchi*1, Shingo Suzuki*3, Hiroko Koumoto*4, Naoko Kosaka*5, Akira Koyama*5, Tomohiro Kokogawa*5, and Yuji Maeda*5

Kentaro Inui^1, Yotaro Watanabe^2, Kenshi Yamaguchi^1, Shingo Suzuki^3, Hiroko Koumoto^4, Naoko Kosaka^5, Akira Koyama^5, Tomohiro Kokogawa^5, and Yuji Maeda^*5