Yandex ceviri ingilizce tuerkce indir
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missing word, word order, incorrect word, unknown word and wrong nounverb agreement and illustrated with examples. On the basis of existing errors in these CP outputs they were divided into five groups i.e. To do so, the CPs were fed to the machine and seen how the machine maps its equivalent translational in the target language i.e. Next, five hundred Hindi CPs were identified manually and translated using Sampark and Google Translator platforms and errors in their TL outputs were compared. The data for this study have been gathered from different secondary sources like e-dictionaries, e-newspapers, and social media platforms. Further, this paper is limited in nature in the sense that it examines Hindi CPs output in Urdu language only. Hence, this study attempts to compare the errors produced by Sampark and Google translators with reference to complex predicates (CPs) while translating it from Hindi to Urdu language. Consequently, measuring such errors of MT output helps us to identify types of errors and subsequently enable us to improve its translation output efficacy. In this sense, MT also produces errors while performing the task of translation. Therefore, this is of high importance to consider world knowledge for an MT to be able to perform the task of translation effectively. Some words are differently translated depending on their contextual meaning. Because human languages are made up of both simple and complex words. The development of a machine translation (MT) system is an extremely challenging task. On the other hand, the findings also showed that the best solution to deal with these problems was to make self-corrections and check the meaning of some suspected words in th. From the analysis, it was found that the biggest problems often faced by the students of English Literature Department of USU were the inaccuracy and mismatch of the meaning translated which reached 31%, followed by the inaccuracy of the language structure in the translation result which reached 30%. The data sources in this research were taken from the questionnaires given to students of English Literature Department of USU. Some theories supporting this research were proposed by Munday, Imre, and also Ghasemi and Hasemian.
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This research was conducted by using the descriptive qualitative method with a case study approach. The discussion focuses on the identification of the problems faced by the translators. This paper is concerned with the problems in the application of Google Translate as a translation tool.