@InProceedings{zaporojets-EtAl:2018:W18-06,
  author    = {Zaporojets, Klim  and  Sterckx, Lucas  and  Deleu, Johannes  and  Demeester, Thomas  and  Develder, Chris},
  title     = {Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.},
  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     = {119--125},
  abstract  = {This paper describes the IDLab system submitted to Task A of the CLPsych 2018 shared task. The goal of this task is predicting psychological health of children based on language used in hand-written essays and socio-demographic control variables. Our entry uses word- and character-based features as well as lexicon-based features and features derived from the essays such as the quality of the language. We apply linear models, gradient boosting as well as neural-network based regressors (feed-forward, CNNs and RNNs) to predict scores. We then make ensembles of our best performing models using a weighted average.},
  url       = {http://www.aclweb.org/anthology/W18-0613}
}