@inproceedings{varadarajan-etal-2024-archetypes,
title = "Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk",
author = "Varadarajan, Vasudha and
Lahnala, Allison and
V Ganesan, Adithya and
Dey, Gourab and
Mangalik, Siddharth and
Bucur, Ana-Maria and
Soni, Nikita and
Rao, Rajath and
Lanning, Kevin and
Vallejo, Isabella and
Flek, Lucie and
Schwartz, H. Andrew and
Welch, Charles and
Boyd, Ryan",
editor = "Yates, Andrew and
Desmet, Bart and
Prud{'}hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.28",
pages = "278--291",
abstract = "Research on psychological risk factors for suicide has developed for decades. However, combining explainable theory with modern data-driven language model approaches is non-trivial. In this study, we propose and evaluate methods for identifying language patterns aligned with theories of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are based on prototypical statements that evince risk of suicidality while relative entropy considers the ratio of how unusual both a risk-familiar and unfamiliar model find the statements. While both approaches independently performed similarly, we find that combining the two significantly improved the performance in the shared task evaluations, yielding our combined system submission with a BERTScore Recall of 0.906. Consistent with the literature, we find that titles are highly informative as suicide risk evidence, despite the brevity. We conclude that a combination of theory- and data-driven methods are needed in the mental health space and can outperform more modern prompt-based methods.",
}
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<abstract>Research on psychological risk factors for suicide has developed for decades. However, combining explainable theory with modern data-driven language model approaches is non-trivial. In this study, we propose and evaluate methods for identifying language patterns aligned with theories of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are based on prototypical statements that evince risk of suicidality while relative entropy considers the ratio of how unusual both a risk-familiar and unfamiliar model find the statements. While both approaches independently performed similarly, we find that combining the two significantly improved the performance in the shared task evaluations, yielding our combined system submission with a BERTScore Recall of 0.906. Consistent with the literature, we find that titles are highly informative as suicide risk evidence, despite the brevity. We conclude that a combination of theory- and data-driven methods are needed in the mental health space and can outperform more modern prompt-based methods.</abstract>
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%0 Conference Proceedings
%T Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk
%A Varadarajan, Vasudha
%A Lahnala, Allison
%A V Ganesan, Adithya
%A Dey, Gourab
%A Mangalik, Siddharth
%A Bucur, Ana-Maria
%A Soni, Nikita
%A Rao, Rajath
%A Lanning, Kevin
%A Vallejo, Isabella
%A Flek, Lucie
%A Schwartz, H. Andrew
%A Welch, Charles
%A Boyd, Ryan
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F varadarajan-etal-2024-archetypes
%X Research on psychological risk factors for suicide has developed for decades. However, combining explainable theory with modern data-driven language model approaches is non-trivial. In this study, we propose and evaluate methods for identifying language patterns aligned with theories of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are based on prototypical statements that evince risk of suicidality while relative entropy considers the ratio of how unusual both a risk-familiar and unfamiliar model find the statements. While both approaches independently performed similarly, we find that combining the two significantly improved the performance in the shared task evaluations, yielding our combined system submission with a BERTScore Recall of 0.906. Consistent with the literature, we find that titles are highly informative as suicide risk evidence, despite the brevity. We conclude that a combination of theory- and data-driven methods are needed in the mental health space and can outperform more modern prompt-based methods.
%U https://aclanthology.org/2024.clpsych-1.28
%P 278-291
Markdown (Informal)
[Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk](https://aclanthology.org/2024.clpsych-1.28) (Varadarajan et al., CLPsych-WS 2024)
ACL
- Vasudha Varadarajan, Allison Lahnala, Adithya V Ganesan, Gourab Dey, Siddharth Mangalik, Ana-Maria Bucur, Nikita Soni, Rajath Rao, Kevin Lanning, Isabella Vallejo, Lucie Flek, H. Andrew Schwartz, Charles Welch, and Ryan Boyd. 2024. Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 278–291, St. Julians, Malta. Association for Computational Linguistics.