@inproceedings{torisawa-2016-disaana,
    title = "{DISAANA} and {D}-{SUMM}: Large-scale Real Time {NLP} Systems for Analyzing Disaster Related Reports in Tweets",
    author = "Torisawa, Kentaro",
    editor = "Han, Bo  and
      Ritter, Alan  and
      Derczynski, Leon  and
      Xu, Wei  and
      Baldwin, Tim",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/fix-sig-urls/W16-3903/",
    pages = "3",
    abstract = "This talk presents two NLP systems that were developed for helping disaster victims and rescue workers in the aftermath of large-scale disasters. DISAANA provides answers to questions such as ``What is in short supply in Tokyo?'' and displays locations related to each answer on a map. D-SUMM automatically summarizes a large number of disaster related reports concerning a specified area and helps rescue workers to understand disaster situations from a macro perspective. Both systems are publicly available as Web services. In the aftermath of the 2016 Kumamoto Earthquake (M7.0), the Japanese government actually used DISAANA to analyze the situation."
}