The prerequisite of many approaches to authorship analysis is a representation of writing style. But despite decades of research, it still remains unclear to what extent commonly used and widely accepted representations like character trigram frequencies actually represent an author's writing style, in contrast to more domain-specific style components or even topic. We address this shortcoming for the first time in a novel experimental setup of fixed authors but swapped domains between training and testing. With this setup, we reveal that approaches using character trigram features are highly susceptible to favor domain information when applied without attention to domains, suffering drops of up to 55.4 percentage points in classification accuracy under domain swapping. We further propose a new remedy based on domain-adversarial learning and compare it to ones from the literature based on heuristic rules. Both can work well, reducing accuracy losses under domain swapping to 3.6% and 3.9%, respectively.

本文研究了利用字符三元组频率等常用方法进行作者分析的不足，探讨了领域特异性和主题对于作者风格的影响，并通过固定作者但领域交换的实验展示，表明该方法在未考虑领域的情况下高度偏向于领域信息，对于此，本文提出了以领域对抗学习为基础的新疗法，并将其比较了基于启发式规则的一些方法，发现新方法和其他方法均可有效缓解领域交换对分类准确度的影响。

抑制领域风格在作者分析中的重要性