Open AccessCATEGORY SHIFT USED IN NOUN PRHASE AND ITS QUALITY IN THE NOVEL DIVERGENT
Author(s) -
Septi Dwi Alfani,
Evert Haryanto Hilman,
Nico Harared Harared
Publication year - 2022
Publication title -
jurnal basis (bahasa dan sastra inggris)/jurnal basis
Language(s) - English
Resource type - Journals
eISSN - 2527-8835
pISSN - 2406-9809
DOI - 10.33884/basisupb.v9i1.5430
Subject(s) - noun phrase , computer science , readability , natural language processing , linguistics , noun , dynamic and formal equivalence , phrase , meaning (existential) , artificial intelligence , equivalence (formal languages) , source text , sentence , translation (biology) , machine translation , psychology , philosophy , biochemistry , chemistry , messenger rna , psychotherapist , gene , programming language
The aims of this research were to find the type of category shift, and quality assessment of noun phrase translation in the novel Divergent. Based on theory Catford (1965). Also, theory Nababan (2012) for quality assessment. This research used a descriptive–qualitative method. To achieve the aims of this study, the researcher used some techniques to analyze the data such as reading and collecting data, underlining the type of category shift in the text of the novel, categorizing types of translation shift, assessing translation accuracy, assessing translation readability and acceptability. Based on the data analysis, it was found that 92% of the data were accurate because the category shift of noun phrase in the target language has equivalent meaning with the source language; 6% of the data were less accurate because the category shift of noun phrase had no equal meaning with the source language and the equivalence seemed less natural, and 2% of the data were not accurate. Meanwhile, 93% of the data were readable because the translation was very easy to understand; 5% of the data were less readable because the translation was not very easy to understand, and 2% of the data were unreadable because the translation was difficult to understand. Furthermore, 92% of the data were acceptable because the translation sounds natural; the words, phrases, clauses, and sentences of the source text, 6% of data were less acceptable, and 2% of data were not acceptable. The dominant data of category shift are the structure shift which got the percentage of 56%, followed by intra-system shift 32%, class shift 8%, and unit shift 4 %.