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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-0613.pdf