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...

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...

In general, word stemming is one of the most important factors that affect the performance of information retrieval systems. The optimization issues of Arabic light stemming algorithm as a main component in natural language processing and information retrieval for Arabic language are based on root-pattern schemes. Since Arabic language is a highly inflected language and has a complex morphologi...

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...

Dialectal Arabic speech recognition is a difficult problem and is relatively less studied. In this paper, we propose a cross-dialectal Gaussian mixture model training criteria to transfer knowledge from one domain to the other by data sharing. Specifically, phone classification experiments on West Point Modern Standard Arabic Speech corpus and Babylon Levantine Arabic Speech corpus demonstrate ...

This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done for Gulf Arabic sentiment analysis despite the fact that it is present in different online platforms. Hence, the first challenge is the absence of annotated data and sentiment lexicons. To fill this g...

Named entity recognition is an involved task and is one that usually requires the usage of numerous resources. Recognizing Arabic entities is an even more difficult task due to the inherent ambiguity of the Arabic language. Previous approaches that have tackled the problem of Arabic named entity recognition have used Arabic parsers and taggers combined with a huge set of gazetteers and sometime...

Clitics in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic language. We tokenize large Arabic text using white-spaces and an automatic clitics tokenizer (AMIRA 2.0) and compare the unique-word count in both cases with English language. We also show the re...

We provide lexical profiling for Arabic by covering two important linguistic aspects of Arabic lexical information, namely morphological inflectional paradigms and syntactic subcategorization frames, making our database a rich repository of Arabic lexicographic details. First, we provide a complete description of the inflectional behaviour of Arabic lemmas based on statistical distribution. We ...

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...