BARD10: A New Benchmark Reveals Significance of Bangla Stop-Words in Authorship Attribution

TL;DR

This paper introduces BARD10, a new Bangla authorship attribution benchmark, and systematically analyzes the impact of stop-word removal on classical and deep learning models, revealing the stylistic importance of stop-words in Bangla texts.

Contribution

It presents BARD10, a balanced Bangla authorship dataset, and provides a comprehensive analysis of stop-word effects across multiple models, highlighting their stylistic significance.

Findings

Classical TF-IDF + SVM outperforms deep models on BARD10 and BAAD16.

Stop-word removal significantly affects authorship attribution accuracy.

Bangla stop-words are key stylistic indicators for authorship attribution.

Abstract

This research presents a comprehensive investigation into Bangla authorship attribution, introducing a new balanced benchmark corpus BARD10 (Bangla Authorship Recognition Dataset of 10 authors) and systematically analyzing the impact of stop-word removal across classical and deep learning models to uncover the stylistic significance of Bangla stop-words. BARD10 is a curated corpus of Bangla blog and opinion prose from ten contemporary authors, alongside the methodical assessment of four representative classifiers: SVM (Support Vector Machine), Bangla BERT (Bidirectional Encoder Representations from Transformers), XGBoost, and a MLP (Multilayer Perception), utilizing uniform preprocessing on both BARD10 and the benchmark corpora BAAD16 (Bangla Authorship Attribution Dataset of 16 authors). In all datasets, the classical TF-IDF + SVM baseline outperformed, attaining a macro-F1 score of 0.997 on BAAD16 and 0.921 on BARD10, while Bangla BERT lagged by as much as five points. This study reveals that BARD10 authors are highly sensitive to stop-word pruning, while BAAD16 authors remain comparatively robust highlighting genre-dependent reliance on stop-word signatures. Error analysis revealed that high frequency components transmit authorial signatures that are diminished or reduced by transformer models. Three insights are identified: Bangla stop-words serve as essential stylistic indicators; finely calibrated ML models prove effective within short-text limitations; and BARD10 connects formal literature with contemporary web dialogue, offering a reproducible benchmark for future long-context or domain-adapted transformers.