This chapter describes the various clustering techniques and document processing methods one can use to discover information about similarities found in translational corpora. Two types of clustering techniques, namely hierarchical clustering and partitioning clustering, and their variations are discussed and applied to a sample of the TK-NHH Translatørkorpus corpus consisting of 71 translated documents on 4 different topics. The results show that these clustering techniques are capable of differentiating translations accepted by experts from those rejected, suggesting that these accepted translations share a high degree of similarity and perhaps resemble an ideal translation of the original text.
2023. Innovation of Digital Stylistics in Literary Translation Studies. In Literary Digital Stylistics in Translation Studies [New Frontiers in Translation Studies, ], ► pp. 45 ff.
Figueredo, Giacomo & Grazziela P. Figueredo
2020. A Systemic Dynamics Model of Text Production. Journal of Quantitative Linguistics 27:4 ► pp. 291 ff.
Laviosa, Sara, Adriana Pagano, Hannu Kemppanen & Meng Ji
2017. A Contextual Approach to Translation Equivalence. In Textual and Contextual Analysis in Empirical Translation Studies [New Frontiers in Translation Studies, ], ► pp. 73 ff.
Covington, Michael A., Iris Potter & Tony Snodgrass
2015. Stylometric classification of different translations of the same text into the same language. Digital Scholarship in the Humanities 30:3 ► pp. 322 ff.
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