Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their comb...

Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political inte...

persian language has influenced arabic language in many different ways; especially in the lexical area of language. since many persian words have entered arabic language, this language has been affected by persian language and culture. many of these persian words can be found in an arabic lexicon called lesan-al-arab. the writers of this article have analyzed the arabic form of the word sepanjg...

Here we describe work on learning the subcategories of verbs in a morphologically rich language using only minimal linguistic resources. Our goal is to learn verb subcategorizations for Quechua, an under-resourced morphologically rich language, from an unannotated corpus. We compare results from applying this approach to an unannotated Arabic corpus with those achieved by processing the same te...

A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resou...

A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, we propose a transfer learning framework to jointly use large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. We have chosen the Qatari Arabic (QA) dialect as a typical example for an under-resourced...

In the past few years, researchers have started paying attention to the Arabic language. In this paper we review information extraction systems that were developed for the Arabic to extract predefined entities. A comparisons are conducted between these systems in terms of their performance in extracting the common entities, the approach used whether rule-based or machine learning and type of co...

This special issue of the Journal consists of nearly two dozen articles that address both methodological approaches to Arabic Natural Language Processing and Automatic Speech Recognition as well as pilot-tested applications that are of commercial value. In keeping with prior special issues of this Journal, published under the auspices of the current Editor-in-Chief, this issue will consider bot...