《大学》英译本特征比较——基于Python数据分析技术

Comparison of Textual Features of English Translations of the Great Learning—Based on Python Data Analysis Technology

摘要: 典籍外译研究是推动中国优秀传统文化走出国门，面向世界的重要部分。为探究不同典籍译本的文本特征，本文自建四位译者《大学》英译本的小型语料库，基于Python语言统计各译本的词汇密度、平均句长和特殊句式数量等数据，从词汇、句法、篇章层面进行分析比较。分析数据发现，四译本用词都较为正式，句式都较为复杂，其中理雅各和陈荣捷的译本较为简练，连贯程度略低；林语堂译本的衔接更加连贯，更多地使用“释译”的翻译方法，更易于读者理解；庞德的译本最长，译本中较多的“释译”来说明原文含义，相较于其他三译本，更加通俗易懂。

Abstract: The translation of Chinese classics plays an important role in promoting the dissemination of Chi-nese excellent traditional culture. In a bid to explore the textual features of different translations of Chinese classics, this paper builds a small corpus of four English translations of the Great Learning. Based on Python language, the data of the textual features, such as lexical density, average word length and average sentence length of each translation are analyzed and compared from lexical, syntactic and textual levels. The results show that all the four translations utilize formal words and complex sentence structures. And the translations of James Legge and Chan Wing-tsit are more con-cise with weak coherence relatively. Lin Yuyang’s translation is more coherent with more interpre-tive translation, which is more accessible to foreign readers. Pound’s translation is the longest one and there are more paraphrases to explain the meaning of the source text. Compared with the other three translations, it can be understood more easily.