@InProceedings{torisawa:2016:WNUT,
  author    = {Torisawa, Kentaro},
  title     = {DISAANA and D-SUMM: Large-scale Real Time NLP Systems for Analyzing Disaster Related Reports in Tweets},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  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.},
  url       = {http://aclweb.org/anthology/W16-3903}
}