@inproceedings{park-etal-2020-suicidal,
title = "Suicidal Risk Detection for Military Personnel",
author = "Park, Sungjoon and
Park, Kiwoong and
Ahn, Jaimeen and
Oh, Alice",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.198",
doi = "10.18653/v1/2020.emnlp-main.198",
pages = "2523--2531",
abstract = "We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Korean social Q{\&}A site, we collect posts containing military-relevant content written by active-duty military personnel. We then annotate the posts with two groups of experts: military experts and mental health experts. Our dataset includes 2,791 posts with 13,955 corresponding expert annotations of suicidal risk levels, and this dataset is available to researchers who consent to research ethics agreement. Using various fine-tuned state-of-the-art language models, we predict the level of suicide risk, reaching .88 F1 score for classifying the risks.",
}
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<abstract>We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Korean social Q&A site, we collect posts containing military-relevant content written by active-duty military personnel. We then annotate the posts with two groups of experts: military experts and mental health experts. Our dataset includes 2,791 posts with 13,955 corresponding expert annotations of suicidal risk levels, and this dataset is available to researchers who consent to research ethics agreement. Using various fine-tuned state-of-the-art language models, we predict the level of suicide risk, reaching .88 F1 score for classifying the risks.</abstract>
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%0 Conference Proceedings
%T Suicidal Risk Detection for Military Personnel
%A Park, Sungjoon
%A Park, Kiwoong
%A Ahn, Jaimeen
%A Oh, Alice
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F park-etal-2020-suicidal
%X We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Korean social Q&A site, we collect posts containing military-relevant content written by active-duty military personnel. We then annotate the posts with two groups of experts: military experts and mental health experts. Our dataset includes 2,791 posts with 13,955 corresponding expert annotations of suicidal risk levels, and this dataset is available to researchers who consent to research ethics agreement. Using various fine-tuned state-of-the-art language models, we predict the level of suicide risk, reaching .88 F1 score for classifying the risks.
%R 10.18653/v1/2020.emnlp-main.198
%U https://aclanthology.org/2020.emnlp-main.198
%U https://doi.org/10.18653/v1/2020.emnlp-main.198
%P 2523-2531
Markdown (Informal)
[Suicidal Risk Detection for Military Personnel](https://aclanthology.org/2020.emnlp-main.198) (Park et al., EMNLP 2020)
ACL
- Sungjoon Park, Kiwoong Park, Jaimeen Ahn, and Alice Oh. 2020. Suicidal Risk Detection for Military Personnel. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2523–2531, Online. Association for Computational Linguistics.