@inproceedings{zaporojets-etal-2018-predicting,
title = "Predicting Psychological Health from Childhood Essays. The {UG}ent-{IDL}ab {CLP}sych 2018 Shared Task System.",
author = "Zaporojets, Klim and
Sterckx, Lucas and
Deleu, Johannes and
Demeester, Thomas and
Develder, Chris",
editor = "Loveys, Kate and
Niederhoffer, Kate and
Prud{'}hommeaux, Emily and
Resnik, Rebecca and
Resnik, Philip",
booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
month = jun,
year = "2018",
address = "New Orleans, LA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0613",
doi = "10.18653/v1/W18-0613",
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.",
}
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%0 Conference Proceedings
%T Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.
%A Zaporojets, Klim
%A Sterckx, Lucas
%A Deleu, Johannes
%A Demeester, Thomas
%A Develder, Chris
%Y Loveys, Kate
%Y Niederhoffer, Kate
%Y Prud’hommeaux, Emily
%Y Resnik, Rebecca
%Y Resnik, Philip
%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, LA
%F zaporojets-etal-2018-predicting
%X 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.
%R 10.18653/v1/W18-0613
%U https://aclanthology.org/W18-0613
%U https://doi.org/10.18653/v1/W18-0613
%P 119-125
Markdown (Informal)
[Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.](https://aclanthology.org/W18-0613) (Zaporojets et al., CLPsych 2018)
ACL