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An Eco-translatology Approach To The English Translation Of Chinese Internet Catchwords

Posted on:2014-02-07Degree:MasterType:Thesis
Country:ChinaCandidate:T T LiuFull Text:PDF
GTID:2235330395493996Subject:English Language and Literature
Abstract/Summary:PDF Full Text Request
As a particular language phenomenon, internet catchwords, with their increasinginfluence, have been attracting increasing academic attentions in recent years.However, current studies mainly focus on linguistic, cultural and communicativeaspects. As for the English translation of Chinese internet catchwords, few researcheshave been done. Relevant studies available now lay emphases mainly on a limitednumber of internet catchwords. Comprehensive and systematic studies are rather rare.Based on the eco-translatology put forward by Professor Hu Gengshen, thepresent dissertation will probe into the English translation of Chinese internetcatchwords from two aspects: translator’s adaptation and selection. In the translationprocess, the translator should conduct the analysis from three-dimensionaltransformations: linguistic, cultural and communicative dimensions. Under theguidance of multi-dimensional adaptation and selection, internet catchwords fromrecent five years and their corresponding English translations are adopted for theanalysis. After the analysis, four traditional translation methods and two innovativeones suitable for the English translation of Chinese internet catchwords are presentedwith convincing examples.In the present thesis, qualitative analysis is adopted. Data of internet catchwordsare chosen from those popular in recent five years. The thesis aims at throwing lightson further studies of the English translation of Chinese internet catchwords andpromoting the worldwide transmission of Chinese internet catchwords and internetculture. It is hoped that the dissertation could provide a new perspective for the studyof internet catchwords.
Keywords/Search Tags:internet catchword, Eco-translatology, adaptation, selection, translation
PDF Full Text Request
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