Modeling Incoherent Discourse in Non-Affective Psychosis

Background: Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis. Methods: Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects. Results: Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder. Conclusions: Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech

Feature Explorations for Hate Speech Classification

In this work, we present a hate speech classifier for German tweets for the GermEval2018 Shared Task. Our best models are Linear SVM classifiers using character ngrams as well as additional textual features. We achieve a macro F1-score of 0.77 (95% confidence interval: ±0.04) in cross validation. We also present an ensemble classifier based on majority voting of the three component models

Feature Explorations for Hate Speech Classification

In this work, we present a hate speech classifier for German tweets for the GermEval2018 Shared Task. Our best models are Linear SVM classifiers using character ngrams as well as additional textual features. We achieve a macro F1-score of 0.77 (95% confidence interval: ±0.04) in cross validation. We also present an ensemble classifier based on majority voting of the three component models