Arabizi Detection and Conversion to Arabic
This addresses the challenge of processing informal Arabic text in social media for natural language processing applications, but it is incremental as it builds on existing transliteration and language modeling techniques.
The paper tackled the problem of detecting Arabizi (Arabic text written in Latin characters) in mixed-language text and converting it to Arabic, achieving 98.5% identification accuracy and 88.7% conversion accuracy.
Arabizi is Arabic text that is written using Latin characters. Arabizi is used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is commonly used in informal settings such as social networking sites and is often with mixed with English. In this paper we address the problems of: identifying Arabizi in text and converting it to Arabic characters. We used word and sequence-level features to identify Arabizi that is mixed with English. We achieved an identification accuracy of 98.5%. As for conversion, we used transliteration mining with language modeling to generate equivalent Arabic text. We achieved 88.7% conversion accuracy, with roughly a third of errors being spelling and morphological variants of the forms in ground truth.