The article is devoted to the Russian-Arabic translation, a particular theory of which has not been developed in domestic translation studies to the extent that the mechanisms of translation from and into European languages are described. In this regard, as well as with the growing volumes of Russian-Arabic translation, the issues of this private theory of translation require significant additions and new approaches. The authors set the task of determining the means of translation (cognitive and mental operations and language transformations) that contribute to the achievement of the most equivalent correspondences of such typologically different languages as Russian and Arabic. The work summarizes and analyzes the accumulated experience of modern Russian linguists, Arabists (Belkin V.M., Gabuchan G.M., Grande B.M., Finkelberg N.D., Frolov V.D., etc.) and representatives of the Arabic classical linguistic school (Ibn Jinni, Sibawayh, etc.) in determining the carrier of word-formation meaning. The purpose of the study is designated as the description of the role of this category in achieving equivalence in the pair of Russian and Arabic. The lexical-semantic group of tools and instruments was considered as a material. These lexemes, both in Russian and Arabic, have an acceptable frequency, cover various stylistic registers and are formed by a relatively limited set of formants, interlingual correspondences of which can be established and compared. For the first time, on the material of this lexico-semantic group, a systemic interlingual correlation of the series of word-building formants in Russian and Arabic is revealed. The authors draw conclusions about the category of the word-formation model as a key one in the algorithm of translation activity, as well as about the specific linguistic signs of the equivalence of the Arabic-Russian translation (full transfer of the real meaning of the motivating base, the coincidence of the rhythm of the derived Arabic word with a model, the correspondence of the derivative to the grammatical categories contained in the model).
Loanwords are the words transferred from one language to another, which become essential part of the borrowing language. The loanwords have come from the source language to the recipient language because of many reasons. Detecting these loanwords is complicated task due to that there are no standard specifications for transferring words between languages and hence low accuracy. This work tries to enhance this accuracy of detecting loanwords between Turkish and Arabic language as a case study. In this paper, the proposed system contributes to find all possible loanwords using any set of characters either alphabetically or randomly arranged. Then, it processes the distortion in the pronunciation, and solves the problem of the missing lette
... Show MoreA study Andalusia woman in shade society Arabic Islamic conservative on social customs in respect instructions in the different cover and tongue and not reveal beautiful woman because she is development the honor conscience in the society and she is respect not mix with the men about fall what he said the law Islamic and belief that the Andalusian people prince Husham days ago and he entry dependence to the mawta book. But the Andalusia woman was having the freedom and boasting and the greatness and beautiful and not respected factions from Andalusia woman instruction religion fewness commitment in the tradition Arabian and Islamic her promiscuity her promiscuity in the Spanish society that it is different habits and traditions with soci
... Show MoreIn this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N
... Show MoreMR Younus, Alustath, 2011
Arabic text categorization for pattern recognitions is challenging. We propose for the first time a novel holistic method based on clustering for classifying Arabic writer. The categorization is accomplished stage-wise. Firstly, these document images are sectioned into lines, words, and characters. Secondly, their structural and statistical features are obtained from sectioned portions. Thirdly, F-Measure is used to evaluate the performance of the extracted features and their combination in different linkage methods for each distance measures and different numbers of groups. Finally, experiments are conducted on the standard KHATT dataset of Arabic handwritten text comprised of varying samples from 1000 writers. The results in the generatio
... Show MoreAn advertisement is a form of communication intended to promote the sale of a product or service, influence public opinion, gain political support, or to elicit some other response. It consists of various type, including style, target audience, geographic scope, medium, or purpose. An advertisement should catch a person's attention and quickly create a memorable impression. The main aim of the present paper is to investigate the phonological problems of translating English international TV advertisements into Arabic. It deals with the most common and popular TV advertisements. The importance of such advertisements lies not in its information content rather than in the achievement of the desired impact on the receivers. When translating such
... Show MoreAccording to grammarians In ( نإ) and Itha (اذإ) are conditionals and sometimes they may be used interchangeably. However, when they are mentioned in the Holy Qur’an, they have their own specific use. This paper attempts to investigate their meanings in the source language as well as investigate their translations and find out any differences or similarities. The translations that are adopted in this research are as follows: Pickthall, Al-Hilali & Khan, and Shakir.
In recent years, the need for Machine Translation (MT) has grown, especially for translating legal contracts between languages like Arabic and English. This study primarily investigates whether Google Translator can adequately replace human translation for legal documents. Utilizing a widely popular free web-based tool, Google Translate, the research method involved translating six segments from various legal contracts into Arabic and assessing the translations for lexical and syntactic accuracy. The findings show that although Google Translate can quickly produce English-Arabic translations, it falls short compared to professional translators, especially with complex legal terms and syntax. Errors can be categorized into: polysemy,
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