Morphological analyzers and part-of-speech taggers are key technologies for most text analysis applications. Our aim is to develop a part-of-speech tagger for annotating a wide range of Arabic text formats, domains and genres including both vowelized and non-vowelized text. Enriching the text with linguistic analysis will maximize the potential for corpus re-use in a wide range of applications....

Example Based Machine Translation (EBMT) is limited by the quantity and scope of its training data. Even with a reasonably large corpus, we will not have examples that cover everything we want to translate. This problem is especially severe in Arabic due to its rich morphology. We demonstrate a novel method that exploits the regular nature of Arabic morphology to increase the quality and covera...

Text processing is a vital step in the information retrieval process, text mining, and natural language processing. It includes several stages, such as normalization, stop word removal, and stemming. Stemming is the process of reducing the lexicon to its root. Due to the different structures and rules in languages, the task of stemming is language-dependent. This research introduces a new stemm...

Most tools and resources developed for natural language processing of Arabic are designed for Modern Standard Arabic (MSA) and perform terribly on Arabic dialects, such as Egyptian Arabic. Egyptian Arabic differs from MSA phonologically, morphologically and lexically and has no standardized orthography. We present a linguistically accurate, large-scale morphological analyzer for Egyptian Arabic...

Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications such as machine translation, especially when languages with complex morphology are concerned such as Arabic. Arabic is a highly flexional language, in that, the same root can lead to various forms according to its context. In this paper, we present a system which disambiguates t...

Arabic is a morphologically rich language, which presents a challenge for part of speech tagging. In this paper, we compare two novel methods for POS tagging of Arabic without the use of gold standard word segmentation but with the full POS tagset of the Penn Arabic Treebank. The first approach uses complex tags that describe full words and does not require any word segmentation. The second app...

Most tools and resources developed for natural language processing of Arabic are designed for Modern Standard Arabic (MSA) and perform terribly on Arabic dialects, such as Egyptian Arabic. Egyptian Arabic differs from MSA phonologically, morphologically and lexically and has no standardized orthography. We present a linguistically accurate, large-scale morphological analyzer for Egyptian Arabic...

The signal sound contains many different features, including Voice Onset Time (VOT), which is a very important feature of stop sounds in many languages. The only application of VOT values is stopping phoneme subsets. This subset of consonant sounds is stop phonemes exist in the Arabic language, and in fact, all languages. Very important subsets of Semitic language’s consonants are the Emphatic ...