Publicación: Natural language processing-driven framework for the early detection of language and cognitive decline
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Fecha
2023-12
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info:eu-repo/semantics/openAccess
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Elsevier
Resumen
Natural 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.
Descripción
Categorías UNESCO
Palabras clave
Language production, Memory concerns, Pre-screening model, Role and reference grammar, Speech assessment, Natural language processing
Citación
Kulvinder 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.2023.09.002
Centro
Facultades y escuelas::Facultad de Filología
Departamento
Filologías Extranjeras y sus Lingüísticas