BARD10：一个新基准揭示孟加拉语停用词在作者归属中的重要性
BARD10: A New Benchmark Reveals Significance of Bangla Stop-Words in Authorship Attribution
AI总结：
本研究针对孟加拉语作者归属问题，构建平衡基准语料库BARD10，分析停用词移除对经典及深度学习模型的影响，揭示停用词的文体重要性，为相关研究提供可复现基准与关键见解。
摘要：
本研究对孟加拉语作者归属进行了全面调查，引入了新的平衡基准语料库BARD10（10位作者的孟加拉语作者识别数据集），并系统分析了停用词移除对经典和深度学习模型的影响，以揭示孟加拉语停用词的文体重要性。BARD10是一个精选语料库，包含10位当代作者的孟加拉语博客和评论散文，同时对4种代表性分类器进行了系统评估：SVM（支持向量机）、Bangla BERT（来自Transformer的双向编码器表示）、XGBoost和MLP（多层感知器），在BARD10和基准语料库BAAD16（16位作者的孟加拉语作者归属数据集）上采用统一预处理。在所有数据集上，经典的TF-IDF+SVM基线表现更优，在BAAD16上达到0.997的宏F1分数，在BARD10上达到0.921，而Bangla BERT落后多达5个百分点。本研究表明，BARD10的作者对停用词修剪高度敏感，而BAAD16的作者相对稳健，突显了对停用词特征的体裁依赖。误差分析显示，高频成分传递的作者特征被Transformer模型削弱或减少。研究确定了三个见解：孟加拉语停用词是重要的文体指标；精细校准的机器学习模型在短文本限制内被证明有效；BARD10连接了正式文学与当代网络对话，为未来长上下文或领域自适应Transformer提供了可复现的基准。
英文摘要：
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.
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