A corpus-based study of semantic shifts in the translation of self-help books from English into Arabic: a theoretical perspective based on relevance theory

P Language and Literature > PI Oriental languages and literatures

AlShubaily, Sarah Ahmed ; Daoudi, Anissa

University:University of Birmingham

Department:Department of Modern Languages

关键词: P Language and Literature;    PI Oriental languages and literatures;   

Others  :  http://etheses.bham.ac.uk//id/eprint/8994/7/Alshubaily2019PhD.pdf

来源: University of Birmingham eTheses Repository

【 摘 要 】

My research investigates the translation of self-help books from English into Arabic. Its primary concern is to analyse the translation of self-help books with emphasis on the terminology, as they constitute the core of this genre, from English into Arabic. It is a corpus-based study that aims to: (1) distinguish semantic shifts from equivalence; (2) identify the causes behind the shifts; and (3) propose a model for semantic shifts analysis that consists of three phases: (a) identification of shifts; (b). explaining them; and (c). evaluation of the shifts from a relevance theorey perspective. The classification taxonomy in this study consists of three main types which are: 1). addition; 2). omission; 3). mutation, and three subtypes including: a). mistranslation; b). wrong word choice; and c). incorrect literal translation. The methodology of this dissertation is based on a corpus linguistics approach as I created a parallel corpus of samples of English self-help books aligned with their Arabic translations. Then, following error analysis (EA) analysis procedures, I applied the following steps to the context of translation analysis: data collection, identification of problematic segments, definition of shift types, explanation of shifts, and finally evaluation. The results of applying the semantic shifts analysis model show that translators of the three books under investigation show a tendency towards mistranslating abstract concepts. The main findings of the study indicate that: 1). Mistranslation is the most common type of semantic shift in the data; 2). Omission is the least frequent type; 3). Most semantic shifts are caused by lack of translators’ competence; and 4). From a relevance theory perspective, the frequency of shifts in the data indicates failure in achieving the conditions of relevance provided in the model.