Journal Paper Published

Abstract

Research on translation universals has traditionally focused on isolated linguistic features along paradigmatic dimensions due to ease of interpretation. However, syntagmatic approaches, which examine how linguistic elements combine sequentially, remain underexplored. This corpus-based study addresses this gap by analysing R-motifs, defined as recurring sequences of part-of-speech tags, across four genres in translated and native Chinese texts. We investigate both the rank-frequency distributions of R-motif types and motif lengths as potential indicators of translation universals. Our analysis shows that R-motif frequencies in both text types follow the right-truncated Zeta distribution, whereas motif length distributions conform to the Pólya model. Random Forests are used to establish the text classification model where texts are represented by the POS R-motif distribution parameters and attributes. The experiments show that the combination of features from distribution parameters and attributes can detect the translationese efficiently. Future research may extend this approach by exploring more granular features beyond part-of-speech sequences.