Stylistic analysis of text is a key task in research areas ranging from
authorship attribution to forensic analysis and personality profiling. The
existing approaches for stylistic analysis are plagued by issues like topic
influence, lack of discriminability for large number of authors and the
requirement for large amounts of diverse data. In this paper, the source of
these issues are identified along with the necessity for a cognitive
perspective on authorial style in addressing them. A novel feature
representation, called Trajectory-based Style Estimation (TraSE), is introduced
to support this purpose. Authorship attribution experiments with over 27,000
authors and 1.4 million samples in a cross-domain scenario resulted in 90%
attribution accuracy suggesting that the feature representation is immune to
such negative influences and an excellent candidate for stylistic analysis.
Finally, a qualitative analysis is performed on TraSE using physical human
characteristics, like age, to validate its claim on capturing cognitive traits.

本文通过采用基于轨迹的风格估计算法，提出了一种新的特征表示法，成功地解决了作者风格识别领域中遇到的主题影响、无法有效鉴别大量作者和需要大量多样化数据的问题，并在跨领域的场景中筛选超过 27,000 个作者和 1.4 万个样本进行了作者归属实验。实验结果表明，该算法对负面影响具有免疫力，并成为风格研究的一个优秀选择。最后，通过以生理人类特征（如年龄）为基础进行定性分析，验证了其认知特征的表达能力。