I-Arabic: Computational Attempts and Corpus Issues in Modern Arabic

Modern Arabic encounters many challenges concerning the use of computer-based methods for analyzing Arabic data. These methods include natural language processing, machine learning, and corpus linguistics, among others. This paper addresses the challenges, the computational attempts, and a proposed model: I-Arabic. One of the main challenges in using computational methods for Arabic is the lack of large, high-quality language resources, such as text corpora, annotated data, and lexical resources. This is due to various factors, including the diversity of Arabic dialects and the limited availability of digitized Arabic texts. Another challenge is the complexity of Arabic morphology and syntax, which can pose difficulties for natural language processing algorithms. Arabic is a highly inflected language, with a rich system of prefixes, suffixes, and internal vowel changes that can affect the meaning and function of words. Additionally, Arabic has a flexible word order and a complex system of grammatical agreement.
Despite these challenges, there have been significant efforts to develop computational tools and resources for Arabic, including the creation of Arabic language corpora and the development of natural language processing algorithms specifically tailored to Arabic. These efforts have the potential to facilitate research in various fields, including linguistics, social media analysis, and machine translation.