2019 · 12 citations · 26 references
ComputingBehavioral SciencesComputer ScienceMachine LearningNatural Language ProcessingRisk AssessmentLanguage ModelingLanguage ModelsOpinion MiningSentiment AnalysisSuicide Risk AssessmentSupport Vector MachinesHybrid Model-based LearningRisk Level AssessmentSuicide Prediction ModelsSuicide Risk DetectionSuicide Risk RecognitionSuicide Risk StratificationSupport Vector ClassificationSupport Vector Machine Classification
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.
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Philipp Koehn, Richard Zens, Chris Dyer et al. · 2007 · 4.9K citations · Full text
Model-Based Gaussian and Non-Gaussian Clustering
Jeffrey D. Banfield, Adrian E. Raftery · Biometrics · 1993 · 2.4K citations