@InProceedings{kshirsagar-morris-bowman:2017:CLPsych,
  author    = {Kshirsagar, Rohan  and  Morris, Robert  and  Bowman, Samuel},
  title     = {Detecting and Explaining Crisis},
  booktitle = {Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology --- From Linguistic Signal to Clinical Reality},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, BC},
  publisher = {Association for Computational Linguistics},
  pages     = {66--73},
  abstract  = {Individuals on social media may reveal themselves to be in various states of
	crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
	from social media text automatically and accurately can have profound
	consequences. However, detecting a general state of crisis without explaining
	why has limited applications. An explanation in this context is a coherent,
	concise subset of the text that rationalizes the crisis detection. We explore
	several methods to detect and explain crisis using a combination of neural and
	non-neural techniques. We evaluate these techniques on a unique data set
	obtained from Koko, an anonymous emotional support network available through
	various messaging applications. We annotate a small subset of the samples
	labeled with crisis with corresponding explanations. Our best technique
	significantly outperforms the baseline for detection and explanation.},
  url       = {http://www.aclweb.org/anthology/W17-3108}
}