Arabic natural language processing: Data science application in big data environment

In the era of Big Data and Data Science, Text analysis within, Natural Language Processing (NLP), suffers from the curse of high dimensionality. The use of NLP in applications such as speech processing, semantic webs, and word processing has become a main element in today’s Artificial Intelligence and Big Data Applications. A natural language parsing system must incorporate three components of natural language, namely, lexicon, morphology, and syntax. As Arabic is highly derivational, each component requires extensive exploitation of the associated linguistic characteristics. Parsing Arabic sentences still has open challenges due to several reasons including the relatively free word order of Arabic, the length of sentences, and the omission of diacritics (vowels) in written Arabic and the frequency of pro-drop phenomena. This research exploits Visual Prolog to provide a scalable platform for Arabic parser and explains the details of the used lexicon and parser and shows the scalability of the system to address more functions.