This article addresses the procedures to validate the Arabic version of Multiple Intelligence Development Assessment Scale (MIDAS). The content validity was examined based on the experts’ judgments on the MIDAS’s items in the Arabic version. The content of eleven items in the Arabic version of MIDAS was modified to match the Arabic context. Then a translation from original English version of MI...

Arabic script is cursive in both handwritten and printed form. Segmentation of Arabic scriptespecially handwrittenis a very challenging task. Many difficulties arise due to the inherent characteristics of Arabic writing such as the overlapping of Arabic sub-words wherein the sub-words share the same vertical space, and vertical ligatures wherein characters are stacked upon each other in a word....

This paper presents DIWAN, an annotation interface for Arabic dialectal texts. While the Arabic dialects differ in many respects from each other and from Modern Standard Arabic, they also have much in common. To facilitate annotation and to make it as efficient as possible, it is therefore not advisable to treat each Arabic dialect as a separate language, unrelated to the other variants of Arab...

We present a method for generating Colloquial Egyptian Arabic (CEA) from morphologically disambiguated Modern Standard Arabic (MSA). When used in POS tagging, this process improves the accuracy from 73.24% to 86.84% on unseen CEA text, and reduces the percentage of out-ofvocabulary words from 28.98% to 16.66%. The process holds promise for any NLP task targeting the dialectal varieties of Arabi...

The Quranic Arabic Corpus (http://corpus.quran.com) is an annotated linguistic resource with multiple layers of annotation including morphological segmentation, part-of-speech tagging, and syntactic analysis using dependency grammar. The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year old central religious text of Islam. This paper...

This paper reports on the application of the Text Attribution Tool (TAT) to profiling the authors of Arabic emails. The TAT system has been developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. We describe the overall TAT system and the Machine Learning experiments resulting in classifiers for the different author t...

In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwriting. Classification has been performed in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers and three subsets of 53 Arabic CBSs, the t...

Stemming is an essential processing step in a wide range of high level text processing applications such as information extraction, machine translation and sentiment analysis. It is used to reduce words to their stems. Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substa...

This paper describes our efforts to build an Arabic ASR system with web-crawled resources. We first describe the processing done to handle Arabic text in general and more particularly to cope with the high number of different phonetic transcriptions associated to a typical Arabic word. Then, we present our experiments to build acoustic models using only audio data found in the web, in particula...

Statistical machine translation for dialectal Arabic is characterized by a lack of data since data acquisition involves the transcription and translation of spoken language. In this study we develop techniques for extracting parallel data for one particular dialect of Arabic (Iraqi Arabic) from out-ofdomain corpora in different dialects of Arabic or in Modern Standard Arabic. We compare two dif...