School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
Faculty of Education, University of Canberra, Canberra, ACT, Australia.
PLoS One. 2019 Feb 8;14(2):e0211809. doi: 10.1371/journal.pone.0211809. eCollection 2019.
Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems.
尽管有大量文献研究文体分析在作者归属研究中的应用，但译文体分析仍是一个研究不足的领域。译文体分析的识别有助于许多领域，包括教育、知识产权和法证语言学。本文分两步进行，首先评估了现有词汇学方法在译文体分析问题中的应用。与之前的研究类似，我们发现使用词汇丰富度的传统形式，如文献中所使用的形式，无法识别译文体分析。这促使我们设计了一种方法，旨在通过使用网络模式来识别译员的独特模式。网络模式是旨在捕获复杂网络局部结构的小子图。所提出的方法在三分类中平均准确率达到 83%。这些结果表明，如果经典的基于词汇特征的工具与适当的非参数缩放相结合，可以用于识别译文体分析。此外，复杂网络分析和网络模式挖掘的使用使得设计能够解决译文体分析问题的特征成为可能。
