@InProceedings{iserman-EtAl:2018:W18-06,
  author    = {Iserman, Micah  and  Ireland, Molly  and  Littlefield, Andrew  and  Davis, Tyler  and  Maliepaard, Sage},
  title     = {An Approach to the CLPsych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection},
  booktitle = {Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, LA},
  publisher = {Association for Computational Linguistics},
  pages     = {47--56},
  abstract  = {The Computational Linguistics and Clinical Psychology (CLPsych) 2018 Shared Task asked teams to predict cross-sectional indices of anxiety and distress, and longitudinal indices of psychological distress from a subsample of the National Child Development Study, started in the United Kingdom in 1958. Teams aimed to predict mental health outcomes from essays written by 11-year-olds about what they believed their lives would be like at age 25. In the hopes of producing results that could be easily disseminated and applied, we used largely theory-based dictionaries to process the texts, and a simple data-driven approach to model selection. This approach yielded only modest results in terms of out-of-sample accuracy, but most of the category-level findings are interpretable and consistent with existing literature on psychological distress, anxiety, and depression.},
  url       = {http://www.aclweb.org/anthology/W18-0605}
}