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.

Bias in Natural Language Processing (NLP) applications has become a critical issue, with many methods developed to measure and mitigate bias in word embeddings and language models. However, most approaches focus on single categories such as gender or ethnicity, neglecting the intersectionality of biases, particularly in non-English languages. This paper addresses these gaps by studying both single-category and intersectional biases in Italian word embeddings and language models. We extend existing bias metrics to Italian, introducing GG-FISE, a novel method for detecting intersectional bias while accounting for grammatical gender. We also adapt the CrowS-Pairs dataset and bias metric to Italian. Through a series of experiments using WEAT, SEAT, and LPBS tests, we identify significant biases along gender and ethnic lines, with particular attention to biases against Romanian and South Asian populations. Our results highlight the need for culturally adapted methods to detect and address biases in multilingual and intersectional contexts.