@inproceedings{kylymnyk-etal-2026-figurative,
title = "Figurative Language in {A}lzheimer{'}s Discourse: Linguistic and Neural Alignment in Clinical Narratives",
author = "Kylymnyk, Diana and
Tomasel, Vit{\'o}ria Hilgert and
Caseli, Helena and
Watkins, Edward and
Villavicencio, Aline and
Wilkens, Rodrigo",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.890/",
doi = "10.63317/2yjp4743qieh",
pages = "11379--11389",
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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<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.</abstract>
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%0 Conference Proceedings
%T Figurative Language in Alzheimer’s Discourse: Linguistic and Neural Alignment in Clinical Narratives
%A Kylymnyk, Diana
%A Tomasel, Vitória Hilgert
%A Caseli, Helena
%A Watkins, Edward
%A Villavicencio, Aline
%A Wilkens, Rodrigo
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F kylymnyk-etal-2026-figurative
%X 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.
%R 10.63317/2yjp4743qieh
%U https://aclanthology.org/2026.lrec-1.890/
%U https://doi.org/10.63317/2yjp4743qieh
%P 11379-11389
Markdown (Informal)
[Figurative Language in Alzheimer’s Discourse: Linguistic and Neural Alignment in Clinical Narratives](https://aclanthology.org/2026.lrec-1.890/) (Kylymnyk et al., LREC 2026)
ACL