A semantic similarity analysis of multiple English translations of The Analects: Based on a natural language processing algorithm

Working from the readers’ perspective, this study first investigates the online acceptance of the complete English translations of The Analects by investigating the number of online comments, downloads, academic citations, and other factors, and it ranks the different English versions according to how well they are received. The complete English translations of The Analects by D. C. Lau, James Legge, and 15 other translators are found to be well received by readers on mainstream online platforms. Then, based on five natural language processing (NLP) algorithms (TF-IDF, Word2Vec, GloVe, BERT, and SimHash), the 15 well-received English versions of The Analects are taken as samples to calculate semantic similarity. By comparing the semantic differences among the texts, this study analyzes the factors that affect the diversification of translated texts. (1) The influence of Chinese annotation on the translation semantics is great, even the greatest among many influential factors; and (2) different translators’ identities, the translation era, the translation purpose, and the translation background do not significantly affect the semantic influence of the translation. On the one hand, the readers can understand the differences between the different translations and choose an appropriate translation for their reading and learning more effectively. On the other hand, using the algorithms of NLP, we focus on the semantic similarity of different English translations of The Analects and analyze them to show the semantic differences quantitatively, which makes the comparison more intuitive and efficiently. Such a quantitative presentation of the results draws scholars’ attention to the differences in the translations.