Abstract
Traditionally, authorship attribution (AA) tasks relied on statistical data analysis and classification based on stylistic features extracted from texts. In recent years, pre-trained language models (PLMs) have attracted significant attention in text classification tasks. However, although they demonstrate excellent performance on large-scale short-text datasets, their effectiveness remains under-explored for small samples, particularly in AA tasks. Additionally, a key challenge is how to effectively leverage PLMs in conjunction with traditional feature-based methods to advance AA research. In this study, we aimed to significantly improve performance using an integrated integrative ensemble of traditional feature-based and modern PLM-based methods on an AA task in a small sample. For the experiment, we used two corpora of literary works to classify 10 authors each. The results indicate that BERT is effective, even for small-sample AA tasks. Both BERT-based and classifier ensembles outperformed their respective stand-alone models, and the integrated ensemble approach further improved the scores significantly. For the corpus that was not included in the pre-training data, the integrated ensemble improved the F1 score by approximately 14 points, compared to the best-performing single model. Our methodology provides a viable solution for the efficient use of the ever-expanding array of data processing tools in the foreseeable future.
Abstract (translated)
传统上，作者身份归属（AA）任务依赖于基于从文本中提取的风格特征进行统计数据分析和分类。近年来，在文本分类任务中，预训练语言模型（PLM）引起了广泛关注。然而，尽管它们在大规模短文本数据集上的表现优异，但对于小样本尤其是在AA任务中的有效性仍需进一步探索。此外，一个关键挑战是如何有效结合传统基于特征的方法与现代PLM方法来推进AA研究。在这项研究中，我们旨在通过整合传统的基于特征和现代的基于PLM的方法，在小样本AA任务上显著提升性能。实验中，我们使用了两部文学作品语料库，每个语料库包含10位作者的作品。 结果表明，即使在小样本AA任务中，BERT也表现出有效性。基于BERT和分类器集成方法均优于各自独立的模型，并且综合集成的方法进一步显著提高了评分。对于未包括在预训练数据中的语料库，与表现最佳的单一模型相比，综合集成方法将F1分数提升了大约14个百分点。我们的研究方法为未来有效利用不断扩大的数据处理工具提供了可行方案。
URL
https://arxiv.org/abs/2504.08527
