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        <title>Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model</title>
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    <abstract>This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models: a behaviour model; a language model and a hybrid model. For the behavioral model approach, we model each user’s behaviour and thoughts with four groups of features: posting behaviour, sentiment, motivation, and content of the user’s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user’s posts to determine how likely his/her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3018</identifier>
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%0 Conference Proceedings
%T Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model
%A Chen, Lushi
%A Aldayel, Abeer
%A Bogoychev, Nikolay
%A Gong, Tao
%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 chen-etal-2019-similar
%X This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models: a behaviour model; a language model and a hybrid model. For the behavioral model approach, we model each user’s behaviour and thoughts with four groups of features: posting behaviour, sentiment, motivation, and content of the user’s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user’s posts to determine how likely his/her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.
%R 10.18653/v1/W19-3018
%U https://aclanthology.org/W19-3018
%U https://doi.org/10.18653/v1/W19-3018
%P 152-157

Markdown (Informal)

[Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model](https://aclanthology.org/W19-3018) (Chen et al., 2019)

• Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model (Chen et al., 2019)

ACL

• Lushi Chen, Abeer Aldayel, Nikolay Bogoychev, and Tao Gong. 2019. Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 152–157, Minneapolis, Minnesota. Association for Computational Linguistics.