@inproceedings{gamoran-etal-2021-using,
title = "Using Psychologically-Informed Priors for Suicide Prediction in the {CLP}sych 2021 Shared Task",
author = "Gamoran, Avi and
Kaplan, Yonatan and
Simchon, Almog and
Gilead, Michael",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.clpsych-1.12",
doi = "10.18653/v1/2021.clpsych-1.12",
pages = "103--109",
abstract = "This paper describes our approach to the CLPsych 2021 Shared Task, in which we aimed to predict suicide attempts based on Twitter feed data. We addressed this challenge by emphasizing reliance on prior domain knowledge. We engineered novel theory-driven features, and integrated prior knowledge with empirical evidence in a principled manner using Bayesian modeling. While this theory-guided approach increases bias and lowers accuracy on the training set, it was successful in preventing over-fitting. The models provided reasonable classification accuracy on unseen test data (0.68{\textless}=AUC{\textless}= 0.84). Our approach may be particularly useful in prediction tasks trained on a relatively small data set.",
}
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%0 Conference Proceedings
%T Using Psychologically-Informed Priors for Suicide Prediction in the CLPsych 2021 Shared Task
%A Gamoran, Avi
%A Kaplan, Yonatan
%A Simchon, Almog
%A Gilead, Michael
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F gamoran-etal-2021-using
%X This paper describes our approach to the CLPsych 2021 Shared Task, in which we aimed to predict suicide attempts based on Twitter feed data. We addressed this challenge by emphasizing reliance on prior domain knowledge. We engineered novel theory-driven features, and integrated prior knowledge with empirical evidence in a principled manner using Bayesian modeling. While this theory-guided approach increases bias and lowers accuracy on the training set, it was successful in preventing over-fitting. The models provided reasonable classification accuracy on unseen test data (0.68\textless=AUC\textless= 0.84). Our approach may be particularly useful in prediction tasks trained on a relatively small data set.
%R 10.18653/v1/2021.clpsych-1.12
%U https://aclanthology.org/2021.clpsych-1.12
%U https://doi.org/10.18653/v1/2021.clpsych-1.12
%P 103-109
Markdown (Informal)
[Using Psychologically-Informed Priors for Suicide Prediction in the CLPsych 2021 Shared Task](https://aclanthology.org/2021.clpsych-1.12) (Gamoran et al., CLPsych 2021)
ACL