Panesar, KulvinderPérez Cabello de Alba, María Beatriz2024-11-252024-11-252023-12Kulvinder Panesar, María Beatriz Pérez Cabello de Alba; Natural language processing-driven framework for the early detection of language and cognitive decline, Language and Health, Volume 1, Issue 2, 2023, Pages 20-35, ISSN 2949-9038, https://doi.org/10.1016/j.laheal.2023.09.0022949-9038https://doi.org/10.1016/j.laheal.2023.09.002https://hdl.handle.net/20.500.14468/24501Natural Language Processing (NLP) technology has the potential to provide a non-invasive, cost-effective method using a timely intervention for detecting early-stage language and cognitive decline in individuals concerned about their memory. The proposed pre-screening language and cognition assessment model (PST-LCAM) is based on the functional linguistic model Role and Reference Grammar (RRG) to analyse and represent the structure and meaning of utterances, via a set of language production and cognition parameters. The model is trained on a Dementia TalkBank dataset with markers of cognitive decline aligned to the global deterioration scale (GDS). A hybrid approach of qualitative linguistic analysis and assessment is applied, which includes the mapping of participants´ tasks of speech utterances and words to RRG phenomena. It uses a metric-based scoring with resulting quantitative scores and qualitative indicators as pre-screening results. This model is to be deployed in a user-centred conversational assessment platform.eninfo:eu-repo/semantics/openAccess55 Historia::5505 Ciencias auxiliares de la historia::5505.10 FilologíaartículoLanguage productionMemory concernsPre-screening modelRole and reference grammarSpeech assessmentNatural language processing