.png){kind=icon}
.png){kind=icon}
Return
Approximating the semantic space: word embedding techniques in psychiatric speech analysis
.png){kind=icon}
OA
AI
DOI：10.1038/s41537-024-00524-7.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Large language models provide high-dimensional representations (embeddings) of word meaning, which allow quantifying changes in the geometry of the semantic space in mental disorders. A pattern of a more condensed ('shrinking') semantic space marked by an increase in mean semantic similarity between words has been recently documented in psychosis across several languages. We aimed to explore this pattern further in picture descriptions provided by a transdiagnostic German sample of patients with schizophrenia spectrum disorders (SSD) (n = 42), major depression (MDD, n = 43), and healthy controls (n = 44). Compared to controls, both clinical groups showed more restricted dynamic navigational patterns as captured by the time series of semantic distances crossed, while also showing differential patterns in the total distances and trajectories navigated. These findings demonstrate alterations centred on the dynamics of the flow of meaning across the semantic space in SSD and MDD, preserving previous indications towards a shrinking semantic space in both cases.
Keywords:
SCHIZOPHRENIA
LANGUAGE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
594
Citations:
905
View more organizations