A Corpus-Based Comparative Study of Translators’ Style: with Seven Versions of Hetang Yuese as Examples
- DOI
- 10.2991/978-94-6463-040-4_211How to use a DOI?
- Keywords
- Corpus Technology; Data Mining; Translators’ style; Moonlight over Lotus Pond
- Abstract
The popularization of computer and information technology is underscored by the proliferation of corpus, which has been effectively applied to linguistics, lexicography, teaching and translation. Compared with traditional manual methods, the corpus-based translation study can provide more objective and empirical findings in terms of data mining, text retrieval, and statistics analysis. This paper aims to disclose the styles of seven translators through the data mining into the self-constructed corpus of Hetang Yuese (known as Moonlight over Lotus Pond in English), including WangT, ZhuT, Goldblatt T, YangDT, PollardT, LiT, and XuT. Based on the statistics, the paper finds that the English-native and non-English-native translators share the similarity of simplification and explication, but differ in sentence difficulty and discourse readability. Therefore, the translators are suggested to combine the advantages of both English and Chinese translators, for better transmission of Chinese prose and exchange of world literature.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wei Gong AU - Chao Xu PY - 2022 DA - 2022/12/27 TI - A Corpus-Based Comparative Study of Translators’ Style: with Seven Versions of Hetang Yuese as Examples BT - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022) PB - Atlantis Press SP - 1416 EP - 1421 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-040-4_211 DO - 10.2991/978-94-6463-040-4_211 ID - Gong2022 ER -