Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.
Klim Zaporojets, Lucas Sterckx, Johannes Deleu, Thomas Demeester, Chris Develder
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.- Anthology ID:
- W18-0613
- Volume:
- Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, LA
- Editors:
- Kate Loveys, Kate Niederhoffer, Emily Prud’hommeaux, Rebecca Resnik, Philip Resnik
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 119–125
- Language:
- URL:
- https://aclanthology.org/W18-0613
- DOI:
- 10.18653/v1/W18-0613
- Cite (ACL):
- Klim Zaporojets, Lucas Sterckx, Johannes Deleu, Thomas Demeester, and Chris Develder. 2018. Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 119–125, New Orleans, LA. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System. (Zaporojets et al., CLPsych 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W18-0613.pdf