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.
Cite
CITATION STYLE
Zaporojets, K., Sterckx, L., Deleu, J., Demeester, T., & Develder, C. (2018). Predicting psychological health from childhood essays the UGent-IDLab CLPsych 2018 shared task system. In Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, CLPsych 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 (pp. 119–125). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-0613
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
