Figurative Language in Alzheimer's Discourse: Linguistic and Neural Alignment in Clinical Narratives

Abstract

Figurative language, including multiword expressions and metaphors, provides a sensitive lens on cognitive functioning but remains largely overlooked in computational studies of Alzheimer’s Disease (AD). This work investigates figurative-language patterns in AD and whether they can help in distinguishing AD from non-clinical discourse and whether a neural model encodes comparable linguistic tendencies. We propose a two-step framework that combines relevant linguistic features with neural representations. Figurative expressions are automatically identified using Large Language Models focusing on idiomaticity and metaphor detection. These figurative language indicators are integrated with lexical, syntactic, and readability features and used to train classifiers on the ADReSS dataset. Correlation and proxy-model analyses reveal significant alignment between linguistic indicators and model predictions: participants with AD produce fewer figurative constructions, lower lexical diversity, and more concrete language. The results obtained demonstrate that contextual embeddings implicitly encode linguistic cues associated with cognitive decline and highlight the value of figurative-language metrics for transparent and linguistically grounded clinical NLP.

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