@InProceedings{benton-mitchell-hovy:2017:EACLlong,
  author    = {Benton, Adrian  and  Mitchell, Margaret  and  Hovy, Dirk},
  title     = {Multitask Learning for Mental Health Conditions with Limited Social Media Data},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {152--162},
  abstract  = {Language contains information about the author's demographic attributes as
	well as their mental state, and has been successfully leveraged in NLP to
	predict either one alone. However, demographic attributes and mental states
	also interact with each other, and we are the first to demonstrate how to use
	them jointly to improve the prediction of mental health conditions across the
	board. We model the different conditions as tasks in a multitask learning (MTL)
	framework, and establish for the first time the potential of deep learning in
	the prediction of mental health from online user-generated text. The framework
	we propose significantly improves over all baselines and single-task models for
	predicting mental health conditions, with particularly significant gains for
	conditions with limited data. In addition, our best MTL model can predict the
	presence of conditions (neuroatypicality) more generally, further reducing the
	error of the strong feed-forward baseline.},
  url       = {http://www.aclweb.org/anthology/E17-1015}
}