@inproceedings{merhbene-etal-2026-detecting,
title = "Detecting Primary Progressive Aphasia ({PPA}) from Text: A Benchmarking Study",
author = "Merhbene, Ghofrane and
Lecron, Fabian and
Fortemps, Philippe and
Dickerson, Bradford C. and
Kurpicz-Briki, Mascha and
Rezaii, Neguine",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.19/",
pages = "355--374",
ISBN = "979-8-89176-386-9",
abstract = "Classifying subtypes of primary progressive aphasia (PPA) from connected speech presents significant diagnostic challenges due to overlapping linguistic markers. This study benchmarks the performance of traditional machine learning models with various feature extraction techniques, transformer-based models, and large language models (LLMs) for PPA classification. Our results indicate that while transformer-based models and LLMs exceed chance-level performance in terms of balanced accuracy, traditional classifiers combined with contextual embeddings remain highly competitive. Notably, MLP using MentalBert{'}s embeddings achieves the highest accuracy. These findings underscore the potential of machine learning for enhancing the automatic classification of PPA subtypes."
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<abstract>Classifying subtypes of primary progressive aphasia (PPA) from connected speech presents significant diagnostic challenges due to overlapping linguistic markers. This study benchmarks the performance of traditional machine learning models with various feature extraction techniques, transformer-based models, and large language models (LLMs) for PPA classification. Our results indicate that while transformer-based models and LLMs exceed chance-level performance in terms of balanced accuracy, traditional classifiers combined with contextual embeddings remain highly competitive. Notably, MLP using MentalBert’s embeddings achieves the highest accuracy. These findings underscore the potential of machine learning for enhancing the automatic classification of PPA subtypes.</abstract>
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%0 Conference Proceedings
%T Detecting Primary Progressive Aphasia (PPA) from Text: A Benchmarking Study
%A Merhbene, Ghofrane
%A Lecron, Fabian
%A Fortemps, Philippe
%A Dickerson, Bradford C.
%A Kurpicz-Briki, Mascha
%A Rezaii, Neguine
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F merhbene-etal-2026-detecting
%X Classifying subtypes of primary progressive aphasia (PPA) from connected speech presents significant diagnostic challenges due to overlapping linguistic markers. This study benchmarks the performance of traditional machine learning models with various feature extraction techniques, transformer-based models, and large language models (LLMs) for PPA classification. Our results indicate that while transformer-based models and LLMs exceed chance-level performance in terms of balanced accuracy, traditional classifiers combined with contextual embeddings remain highly competitive. Notably, MLP using MentalBert’s embeddings achieves the highest accuracy. These findings underscore the potential of machine learning for enhancing the automatic classification of PPA subtypes.
%U https://aclanthology.org/2026.findings-eacl.19/
%P 355-374
Markdown (Informal)
[Detecting Primary Progressive Aphasia (PPA) from Text: A Benchmarking Study](https://aclanthology.org/2026.findings-eacl.19/) (Merhbene et al., Findings 2026)
ACL