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A Study Of Conceptual Blending In Ancient Chinese Maxims Interpreting

Posted on:2014-07-27Degree:MasterType:Thesis
Country:ChinaCandidate:H R ZhangFull Text:PDF
GTID:2255330422965253Subject:English Language and Literature
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This thesis puts its emphasis on ancient Chinese maxims interpreting under the frameworkof Conceptual blending theory, launching studies on the conceptual blending mechanism ofancient Chinese maxims interpreting through collecting, clearing up and analyzing the data andconstructing a whole conceptual blending model for ancient Chinese maxims interpreting. Onthe one hand, the present study presents a detailed introduction of conceptual blending theoryincluding its fundamental concepts, models and principles as well as the integrated studybetween conceptual blending theory and other subjects, especially interpreting. On the otherhand, the present study applies conceptual blending theory to constructing a distinctive blendingmodel for ancient Chinese maxims interpreting on the purpose of testing the hypothesispresented in1.2and achieving the ultimate goal of enhancing the interpreting practice andconceptual blending theory itself.Adopting qualitative research method and three main approaches such as deduction,induction and analysis, the present study takes the ancient Chinese maxims in the speeches ofChinese leaders as research subject. In this thesis, more than50lines of ancient Chineseaxims are collected from either conference talks or speeches delivered by Heads of State andhigh-ranking officials, among which22of them are exemplified and analyzed exhaustively.These lines of ancient Chinese maxims are more representative and frequently quoted onofficial occasions, and the interpreting quality is more reliable and generally accepted, whichmakes this study more down-to-earth and constructive.Through establishing a novel conceptual blending model for ancient Chinese maximsinterpreting, the present study presents a complicated and dynamic blending process and coversthe interpreting methods as well. It puts forward such research questions: Is it a one-dimensionalconceptual blending model or a two-dimensional model? How many mental spaces are involved in the process of blending? What kinds of approaches can be adopted given thedistinctiveness of ancient Chinese maxims and its frequent application in recent decades?The research results are as follows:1) Two rounds of blending in ancient Chinese maxims interpretingļ¼šsource text decodingand target text producing. Source text decoding refers to the blending amid source text, whiletarget text producing is a complicated process concerning the interaction among such mentalspaces as source text space, context space, interpreter space, memory space and target text (orinterpreted text) space. Through source text decoding, the interpreter gets a whole and completegrasp of the conceptual structures or organizing frames of the source text, which serves as thepreconditions for target text producing. Once the source text is unpacked, the interpreter turns tothe next indispensable round of blending. In target text producing, the source text withunpacked linguistic structures and conceptual structures, as one of the mental spaces,participates in the blending process.2) The process of target text producing consists of five mental spaces and two levels ofblending. Source text space, interpreter space and context space are input spaces, while memoryspace is the blending space of the first level of blending and the input space in the second levelof blending.3) Free interpreting approach and literal interpreting approach are employed in ancientChinese maxims interpreting. When the counterpart between source text and target text could bematched and exerts the same semantic and rhetorical effect on target audience as it does onsource audience, literal interpreting approach will be employed; otherwise free interpretingapproach will be adopted.
Keywords/Search Tags:conceptual blending, ancient Chinese maxims interpreting, five mental spaces, source text decoding, target text producing
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