Researchers have developed a system for automatically classifying reflection levels in Hungarian student essays, addressing a gap in automated analysis for the language. The study utilized a dataset of 1,954 essays, experimenting with both traditional machine learning models and Hungarian-specific transformer models. While classical methods achieved a higher overall score of 71%, transformer models showed better generalization on minority classes, scoring 68%. The findings suggest classical approaches remain relevant for low-resource languages, while transformers offer robustness for imbalanced datasets. AI
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IMPACT Provides a new dataset and insights for automated text analysis in morphologically rich, low-resource languages.
RANK_REASON Academic paper presenting a new dataset and methodology for automated text classification.