arXiv Machine Learning
BanglaMed-QA: A Question Answering System for Healthcare Support in Bangla
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Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications
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arXiv Computation and Language
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Hugging Face Trending Papers
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IndoNutriQA: An Indonesian Clinical Nutrition Question Answering Benchmark
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Custom Named Entity Recognition and Topic Classification for Global Health Publications
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost. "whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."