We present a study of the linguistic output of the German-speaking writer Robert Walser using NLP. We curated a corpus comprising texts written by Walser during periods of sound health, and writings from the year before his hospitalization, and writings from the first year of his stay in a psychiatric clinic, all likely at- tributed to schizophrenia. Within this corpus, we identified and analyzed a total of 20 lin- guistic markers encompassing established met- rics for lexical diversity, semantic similarity, and syntactic complexity. Additionally, we ex- plored lesser-known markers such as lexical innovation, concreteness, and imageability. No- tably, we introduced two additional markers for phonological similarity for the first time within this context. Our findings reveal sig- nificant temporal dynamics in these markers closely associated with Walser’s contempora- neous diagnosis of schizophrenia. Furthermore, we investigated the relationship between these markers, leveraging them for classification of the schizophrenic episode.

Incoherent discourse in schizophrenia has long been recognized as a dominant symptom of the mental disorder (Bleuler, 1911/1950). Recent studies have used modern sentence and word embeddings to compute coherence metrics for spontaneous speech in schizophrenia. While clinical ratings always have a subjective element, computational linguistic methodology allows quantification of speech abnormalities. Clinical and empirical knowledge from psychiatry provide the theoretical and conceptual basis for modelling. Our study is an interdisciplinary attempt at improving coherence models in schizophrenia. Speech samples were obtained from healthy controls and patients with a diagnosis of schizophrenia or schizoaffective disorder and different severity of positive formal thought disorder. Interviews were transcribed and coherence metrics derived from different embeddings. One model found higher coherence metrics for controls than patients. All other models remained non-significant. More detailed analysis of the data motivates different approaches to improving coherence models in schizophrenia, e.g. by assessing referential abnormalities.