@inproceedings{chen-etal-2019-similar,
    title = "Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model",
    author = "Chen, Lushi  and
      Aldayel, Abeer  and
      Bogoychev, Nikolay  and
      Gong, Tao",
    editor = "Niederhoffer, Kate  and
      Hollingshead, Kristy  and
      Resnik, Philip  and
      Resnik, Rebecca  and
      Loveys, Kate",
    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://preview.aclanthology.org/ingest-emnlp/W19-3018/",
    doi = "10.18653/v1/W19-3018",
    pages = "152--157",
    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."
}