@inproceedings{radford-etal-2018-adult,
title = "Can adult mental health be predicted by childhood future-self narratives? Insights from the {CLP}sych 2018 Shared Task",
author = "Radford, Kylie and
Lavrencic, Louise and
Peters, Ruth and
Kiely, Kim and
Hachey, Ben and
Nowson, Scott and
Radford, Will",
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/W18-0614",
doi = "10.18653/v1/W18-0614",
pages = "126--135",
abstract = "The CLPsych 2018 Shared Task B explores how childhood essays can predict psychological distress throughout the author{'}s life. Our main aim was to build tools to help our psychologists understand the data, propose features and interpret predictions. We submitted two linear regression models: ModelA uses simple demographic and word-count features, while ModelB uses linguistic, entity, typographic, expert-gazetteer, and readability features. Our models perform best at younger prediction ages, with our best unofficial score at 23 of 0.426 disattenuated Pearson correlation. This task is challenging and although predictive performance is limited, we propose that tight integration of expertise across computational linguistics and clinical psychology is a productive direction.",
}
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<abstract>The CLPsych 2018 Shared Task B explores how childhood essays can predict psychological distress throughout the author’s life. Our main aim was to build tools to help our psychologists understand the data, propose features and interpret predictions. We submitted two linear regression models: ModelA uses simple demographic and word-count features, while ModelB uses linguistic, entity, typographic, expert-gazetteer, and readability features. Our models perform best at younger prediction ages, with our best unofficial score at 23 of 0.426 disattenuated Pearson correlation. This task is challenging and although predictive performance is limited, we propose that tight integration of expertise across computational linguistics and clinical psychology is a productive direction.</abstract>
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%0 Conference Proceedings
%T Can adult mental health be predicted by childhood future-self narratives? Insights from the CLPsych 2018 Shared Task
%A Radford, Kylie
%A Lavrencic, Louise
%A Peters, Ruth
%A Kiely, Kim
%A Hachey, Ben
%A Nowson, Scott
%A Radford, Will
%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 radford-etal-2018-adult
%X The CLPsych 2018 Shared Task B explores how childhood essays can predict psychological distress throughout the author’s life. Our main aim was to build tools to help our psychologists understand the data, propose features and interpret predictions. We submitted two linear regression models: ModelA uses simple demographic and word-count features, while ModelB uses linguistic, entity, typographic, expert-gazetteer, and readability features. Our models perform best at younger prediction ages, with our best unofficial score at 23 of 0.426 disattenuated Pearson correlation. This task is challenging and although predictive performance is limited, we propose that tight integration of expertise across computational linguistics and clinical psychology is a productive direction.
%R 10.18653/v1/W18-0614
%U https://aclanthology.org/W18-0614
%U https://doi.org/10.18653/v1/W18-0614
%P 126-135
Markdown (Informal)
[Can adult mental health be predicted by childhood future-self narratives? Insights from the CLPsych 2018 Shared Task](https://aclanthology.org/W18-0614) (Radford et al., CLPsych 2018)
ACL