Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model

Lushi Chen, Abeer Aldayel, Nikolay Bogoychev, Tao Gong


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.
Anthology ID:
W19-3018
Volume:
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
152–157
Language:
URL:
https://www.aclweb.org/anthology/W19-3018
DOI:
10.18653/v1/W19-3018
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3018.pdf