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
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
119–125
Language:
URL:
https://www.aclweb.org/anthology/W18-0613
DOI:
10.18653/v1/W18-0613
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0613.pdf