Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling.

ABSTRACT:

Background

Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD.

Methods

We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measure

SUBMITTER: Fradkin I 

PROVIDER: S-EPMC11932972 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature