2024
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Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk
Vasudha Varadarajan
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Allison Lahnala
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Adithya V Ganesan
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Gourab Dey
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Siddharth Mangalik
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Ana-Maria Bucur
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Nikita Soni
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Rajath Rao
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Kevin Lanning
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Isabella Vallejo
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Lucie Flek
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H. Andrew Schwartz
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Charles Welch
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Ryan Boyd
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
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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From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP
Adithya V Ganesan
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Siddharth Mangalik
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Vasudha Varadarajan
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Nikita Soni
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Swanie Juhng
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João Sedoc
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H. Andrew Schwartz
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Salvatore Giorgi
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Ryan L Boyd
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)
Aimed at the NLP researchers or practitioners who would like to integrate human - individual, group, or societal level factors into their analyses, this tutorial will cover recent techniques and libraries for doing so at each level of analysis. Starting with human-centered techniques that provide benefit to traditional document- or word-level NLP tasks (Garten et al., 2019; Lynn et al., 2017), we undertake a thorough exploration of critical human-level aspects as they pertain to NLP, gradually moving up to higher levels of analysis: individual persons, individual with agent (chat/dialogue), groups of people, and finally communities or societies.