@inproceedings{matero-etal-2019-suicide,
title = "Suicide Risk Assessment with Multi-level Dual-Context Language and {BERT}",
author = "Matero, Matthew and
Idnani, Akash and
Son, Youngseo and
Giorgi, Salvatore and
Vu, Huy and
Zamani, Mohammad and
Limbachiya, Parth and
Guntuku, Sharath Chandra and
Schwartz, H. Andrew",
booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3005",
doi = "10.18653/v1/W19-3005",
pages = "39--44",
abstract = "Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with {``}any-risk{''}, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.",
}
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<abstract>Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with “any-risk”, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.</abstract>
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%0 Conference Proceedings
%T Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
%A Matero, Matthew
%A Idnani, Akash
%A Son, Youngseo
%A Giorgi, Salvatore
%A Vu, Huy
%A Zamani, Mohammad
%A Limbachiya, Parth
%A Guntuku, Sharath Chandra
%A Schwartz, H. Andrew
%S Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F matero-etal-2019-suicide
%X Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with “any-risk”, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.
%R 10.18653/v1/W19-3005
%U https://aclanthology.org/W19-3005
%U https://doi.org/10.18653/v1/W19-3005
%P 39-44
Markdown (Informal)
[Suicide Risk Assessment with Multi-level Dual-Context Language and BERT](https://aclanthology.org/W19-3005) (Matero et al., 2019)
ACL
- Matthew Matero, Akash Idnani, Youngseo Son, Salvatore Giorgi, Huy Vu, Mohammad Zamani, Parth Limbachiya, Sharath Chandra Guntuku, and H. Andrew Schwartz. 2019. Suicide Risk Assessment with Multi-level Dual-Context Language and BERT. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 39–44, Minneapolis, Minnesota. Association for Computational Linguistics.