Entropy-based discrimination between translated Chinese and original Chinese using data mining techniques

Abstract

The present research reports on the use of data mining techniques for differentiating between translated and non-translated original Chinese based on monolingual comparable corpora. We operationalized seven entropy-based metrics including character, wordform unigram, wordform bigram and wordform trigram, POS (Part-of-speech) unigram, POS bigram and POS trigram entropy from two balanced Chinese comparable corpora (translated vs non-translated) for data mining and analysis. We then applied four data mining techniques including Support Vector Machines (SVMs), Linear discriminant analysis (LDA), Random Forest (RF) and Multilayer Perceptron (MLP) to distinguish translated Chinese from original Chinese based on these seven features. Our results show that SVMs is the most robust and effective classifier, yielding an AUC of 90.5% and an accuracy rate of 84.3%. Our results have affirmed the hypothesis that translational language is categorically different from original language. Our research demonstrates that combining information-theoretic indicator of Shannon’s entropy together with machine learning techniques can provide a novel approach for studying translation as a unique communicative activity. This study has yielded new insights for corpus-based studies on the translationese phenomenon in the field of translation studies.

Linguistic Variations Between Translated and Non-Translated English Chairman’s Statements in Corporate Annual Reports: A Multidimensional Analysis

“…Future research can use self-designed MDA by including more indices of semantic and syntactic categories. Furthermore, future research may explore other comprehensive information-theoretic metrics, such as entropy (Liu et al, 2022a(Liu et al, , 2022b, to validate existing findings and enhance our understanding of linguistic differences between translated and non-translated chairman's statements.…”