Abstract
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect classification in four out of fifteen runs when using 5-gram features. These results demonstrate that lightweight, interpretable character n-gram approaches can deliver state-of-the-art accuracy for Romanian authorship attribution, rivaling more complex methods. Our findings highlight the potential of simple stylometric features in resource, constrained or under-studied language settings.
Abstract (translated)
这项研究解决了使用ROST语料库对罗马尼亚文本进行作者归属的问题，ROST语料库是该领域的标准基准。我们系统地评估了六种机器学习技术：支持向量机（SVM）、逻辑回归（LR）、k-近邻（k-NN）、决策树（DT）、随机森林（RF）和人工神经网络（ANN），采用字符n-gram特征进行分类。在这其中，使用5-gram特征时，ANN模型在十五次运行中的四次达到了完美的分类效果，表现最佳。这些结果表明，在罗马尼亚作者归属问题上，基于轻量级、可解释的字符n-gram方法可以实现最先进的精度，与更复杂的方法相当。我们的研究发现强调了简单风格度量特征在资源有限、约束或较少研究的语言环境中所具有的潜力。
URL
https://arxiv.org/abs/2506.15650
