The present paper respects 'inversion' as a habit of arranging the language of modern English and Arabic poetry . Inversion is a significant phenomenon generally in modern literature and particularly in poetry that it treats poetic text as it is a violator to the ordinary text. The paper displays the common patterns and functions of inversion which are spotted in modern English and Arabic poetry in order to show aspects of similarities and differences in both languages. It concludes that inversion is most commonly used in English and Arabic poetry in which it may both satisfy the demands of sound correspondence and emphasis. English and Arabic poetic languages vary in extant to their manipulation of inverted styles as they show changeable f

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

Verbal Antonyms: A research in the relationship in meaning Between the words in Arabic language

In 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

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The Arabic Grammar between Originality and Sufficiency