@inproceedings{morales-etal-2021-team,
title = "Team 9: A Comparison of Simple vs. Complex Models for Suicide Risk Assessment",
author = "Morales, Michelle and
Dey, Prajjalita and
Kohli, Kriti",
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-1.11",
doi = "10.18653/v1/2021.clpsych-1.11",
pages = "99--102",
abstract = "This work presents the systems explored as part of the CLPsych 2021 Shared Task. More specifically, this work explores the relative performance of models trained on social me- dia data for suicide risk assessment. For this task, we aim to investigate whether or not simple traditional models can outperform more complex fine-tuned deep learning mod- els. Specifically, we build and compare a range of models including simple baseline models, feature-engineered machine learning models, and lastly, fine-tuned deep learning models. We find that simple more traditional machine learning models are more suited for this task and highlight the challenges faced when trying to leverage more sophisticated deep learning models.",
}
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<abstract>This work presents the systems explored as part of the CLPsych 2021 Shared Task. More specifically, this work explores the relative performance of models trained on social me- dia data for suicide risk assessment. For this task, we aim to investigate whether or not simple traditional models can outperform more complex fine-tuned deep learning mod- els. Specifically, we build and compare a range of models including simple baseline models, feature-engineered machine learning models, and lastly, fine-tuned deep learning models. We find that simple more traditional machine learning models are more suited for this task and highlight the challenges faced when trying to leverage more sophisticated deep learning models.</abstract>
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%0 Conference Proceedings
%T Team 9: A Comparison of Simple vs. Complex Models for Suicide Risk Assessment
%A Morales, Michelle
%A Dey, Prajjalita
%A Kohli, Kriti
%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 morales-etal-2021-team
%X This work presents the systems explored as part of the CLPsych 2021 Shared Task. More specifically, this work explores the relative performance of models trained on social me- dia data for suicide risk assessment. For this task, we aim to investigate whether or not simple traditional models can outperform more complex fine-tuned deep learning mod- els. Specifically, we build and compare a range of models including simple baseline models, feature-engineered machine learning models, and lastly, fine-tuned deep learning models. We find that simple more traditional machine learning models are more suited for this task and highlight the challenges faced when trying to leverage more sophisticated deep learning models.
%R 10.18653/v1/2021.clpsych-1.11
%U https://aclanthology.org/2021.clpsych-1.11
%U https://doi.org/10.18653/v1/2021.clpsych-1.11
%P 99-102
Markdown (Informal)
[Team 9: A Comparison of Simple vs. Complex Models for Suicide Risk Assessment](https://aclanthology.org/2021.clpsych-1.11) (Morales et al., CLPsych 2021)
ACL