publications | Howard Prioleau

2023

Howard University Computer Science at SemEval-2023 Task 12: A 2-Step System Design for Multilingual Sentiment Classification with Language Identification

Saurav Aryal, and Howard Prioleau

In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), 2023

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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.
Zero-Shot Classification Reveals Potential Positive Sentiment Bias in African Languages Translations

Hrishav Sapkota, Saurav Keshari Aryal, and Howard Prioleau

2023

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Feature Importance Analysis for Mini Mental Status Score Prediction in Alzheimer’s Disease

Howard Prioleau, and Saurav Keshari Aryal

2023

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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.
Sentiment analysis across multiple african languages: A current benchmark

Saurav K Aryal, Howard Prioleau, and Surakshya Aryal

Social Impact of AI for Africa (SIAIA-23) at AAAI-23, 2023

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Baselining Performance for Multilingual Codeswitching Sentiment Classification

Saurav K Aryal, Howard Prioleau, Surakshya Aryal, and Gloria Washington

Journal of Computing Sciences in Colleges, 2023
Term Frequency Features vs Transformers: A Comparision for Sentiment Classification of African Languages

Saurav K Aryal, Howard Prioleau, Ujjawal Shah, and Sameer Acharya

Journal of Computing Sciences in Colleges, 2023
EXPERIMENTING WITH MULTIMODAL AUTOML: DETECTION AND EVALUATION OF ALZHEIMER’S DISEASE

Ujjawal Shah, Saurav K Aryal, Howard Prioleau, and Legand Burge

2023

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Evaluating Ensembled Transformers for Multilingual Code-Switched Sentiment Analysis

Saurav K. Aryal, Howard Prioleau, Gloria Washington, and Legand Burge

In 2023 International Conference on Computational Science and Computational Intelligence (CSCI), 2023
Ensembling and Modeling Approaches for Enhancing Alzheimer’s Disease Scoring and Severity Assessment

Saurav K. Aryal, Ujjawal Shah, Howard Prioleau, and Legand Burge

In 2023 International Conference on Computational Science and Computational Intelligence (CSCI), 2023
Benchmarking Current State-of-the-Art Transformer Models on Token Level Language Identification and Language Pair Identification

Howard Prioleau, and Saurav K. Aryal

In 2023 International Conference on Computational Science and Computational Intelligence (CSCI), 2023

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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.

2022

Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer’s

Saurav K Aryal, Howard Prioleau, and Legand Burge

In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023: Kohala Coast, Hawaii, USA, 3–7 January 2023, 2022

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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.
Sentiment Classification of Code-Switched Text using Pre-Trained Multilingual Embeddings and Segmentation

Saurav K Aryal, Howard Prioleau, and Gloria Washington

In CS & IT Conference Proceedings, 2022

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