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Disaster Incident Analysis via Algebra Stories

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Abstract

Disaster management requires detailed data from past disasters for policy planning as well as for the generation of disaster exercises and simulations. Post-analysis of disasters is often distributed in official reports, which provide detailed analysis of the events that have happened. However, some information in such reports is often only given implicitly as part of natural language and thus not accessible to classical natural language processing-based text mining. To address this problem, in this paper, we propose to consider the information extraction tasks related to post-disaster report analysis as algebra stories, that can be treated with computer algebra systems together with natural language processing. We applied our enhanced information extraction approach in preliminary experiments to the report of a bushfire in 2009 in Victoria, Australia and used four different tools for solving fire-specific algebra story problems (ASP). Our evaluation shows that these tools have difficulty handling the occurring ASPs.

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Notes

  1. Listed alphabetically.

  2. The attentive reader will surely realize that the provided response from the tool is not correct. (truncated): 43.5.

  3. At the time of writing.

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Acknowledgements

SBA Research (SBA-K1) is a COMET Centre within the COMET - Competence Centers for Excellent Technologies Programme and funded by BMK, BMAW, and the federal state of Vienna. COMET is managed by FFG. This work was performed partly under the following financial assistance award 70NANB21H124 from U.S. Department of Commerce, National Institute of Standards and Technology.

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  1. MATRIS Research Group, SBA Research, Floragasse 7, Vienna, AT 1040, Austria

    Berina Celic, Klaus Kieseberg, Bernhard Garn & Dimitris E. Simos

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  1. Berina Celic
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Correspondence to Bernhard Garn.

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Celic, B., Kieseberg, K., Garn, B. et al. Disaster Incident Analysis via Algebra Stories. Math.Comput.Sci. 18, 11 (2024). https://doi.org/10.1007/s11786-024-00586-x

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