@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://preview.aclanthology.org/ingest-eacl/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."
}