Abstract
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.
Highlights
Introduction and motivationA much-debated question about translation is whether the translation is an art, science, or art and science combined
The same ranking approach is applied to the vocabulary richness measures that we described in Method II to investigate the performance of network motifs and vocabulary richness
Comparing the five groups of attributes to each other, we found that the best accuracy was achieved by the fifth group using support vector machine (SVM) classifier
Summary
A much-debated question about translation is whether the translation is an art, science, or art and science combined. If we have different translations for a piece of text that a particular author wrote, the best translation is the one that is able to deliver the closest mental picture that the author drew in the original text. The general stpdf followed in these activities are invariant among people. There are salient differences in the parameterisations of these stpdf and choices made along the way for fine grained implementations. These choices will vary noticeably from one person to another
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