Howard Prioleau

The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and track aging-associated disease in cognitive function among older adult populations is imperative. In particular, research related to automatic detection and evaluation of Alzheimer’s disease (AD) is critical given the disease’s prevalence and the cost of current methods. As AD impacts the acoustics of speech and vocabulary, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted and learned features that capture linguistic and acoustic phenomena. Using a subset of 54 top features selected by two methods: (1) recursive elimination and (2) correlation scores, we outperform a state-of-the-art baseline for the same task. Upon scoring and evaluating the statistical significance of each of the selected subset of features for each model, we find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.

The recent release of the AfriSenti-SemEval shared Task 12 has made available 14 new datasets annotated for sentiment analysis on African Languages. We proposed and evaluated two approaches to this task, Delta TF-IDF, and a proposed Language-Specific Model Fusion System using Language Identification. Both produced comparable or better classification performance than the current state-of-art models on this task: AfriBERTa, AfroXLMR, and AfroLM.

This research article proposes developing predictive models to forecast Mini-Mental State Exam (MMSE) scores using the 54 most important features identified from the current state-of-the-art model. The study employs the SHapley Additive exPlanations (SHAP) method to explore feature importance and interpret model performance. The analysis shows that the Automated Readability Index (ARI) is the most influential feature in predicting MMSE scores. This finding suggests that ARI’s capability to capture language impairment and morphosyntax is valuable in predicting cognitive decline in dementia patients. Although the analysis could not evaluate all features, this study provides a foundation for future investigations into features that may assist in predicting MMSE scores and the onset of Dementia.

With the rise of internet usage, code-switching, where multiple languages or dialects intermingle, has surged. Traditional linguistic analysis struggles with this mixed text, as they’re typically monolingual-focused. This paper delves into two core tasks for analyzing code-switched data: Token Level Language Identification (LID) and our newly proposed Language Pair Identification (LPI). We benchmarked and compared current state-of-art transformer models across both tasks to gauge their applicability to the tasks. Our results showed that state-of-the-art multilingual transformer models could achieve state-of-the-art performance on both tasks. The impressive performance on LPI suggests that this will be the first step to utilizing Language Pair Identification to assist in various facets related to Code-Switched corpora and classification performance.