Arabic dialects also called colloquial Arabic or vernaculars are spoken varieties of Standard Arabic. These dialects have mixed form with many variations due to the influence of ancient local tongues and other languages like European ones. Many of these dialects are mutually incomprehensible. Arabic dialects were not written until recently and were used only in a speech form. Nowadays, with the advent of the internet and mobile telephony technologies, these dialects are increasingly used in a written form. Indeed, this kind of communication brought everyday conversations to a written format. This allows Arab people to use their dialects, which are their actual native languages for expressing their opinion on social media, for chatting, texting, etc. This growing use opens new research direction for Arabic natural language processing (NLP). We focus, in this paper, on machine translation in the context of Arabic dialects. We provide a survey of recent research in this area. We report for each study a detailed description of the adopted approach and we give its most relevant contribution.
Over the last years, with the explosive growth of social media, huge amounts of rumors have been rapidly spread on the internet. Indeed, the proliferation of malicious misinformation and nasty rumors in social media can have harmful effects on individuals and society. In this paper, we investigate the content of the fake news in the Arabic world through the information posted on YouTube. Our contribution is threefold. First, we introduce a novel Arab corpus for the task of fake news analysis, covering the topics most concerned by rumors. We describe the corpus and the data collection process in detail. Second, we present several exploratory analysis on the harvested data in order to retrieve some useful knowledge about the transmission of rumors for the studied topics. Third, we test the possibility of discrimination between rumor and no rumor comments using three machine learning classifiers namely, Support Vector Machine (SVM), Decision Tree (DT) and Multinomial Naïve Bayes (MNB).
Abstract-Arabic is the official language overall Arab countries, it is used for official speech, news-papers, public administration and school. In Parallel, for everyday communication, nonofficial talks, songs and movies, Arab people use their dialects which are inspired from Standard Arabic and differ from one Arabic country to another. These linguistic phenomenon is called disglossia, a situation in which two distinct varieties of a language are spoken within the same speech community. It is observed Throughout all Arab countries, standard Arabic widely written but not used in everyday conversation, dialect widely spoken in everyday life but almost never written. Thus, in NLP area, a lot of works have been dedicated for written Arabic. In contrast, Arabic dialects at a near time were not studied enough. Interest for them is recent. First work for these dialects began in the last decade for middle-east ones. Dialects of the Maghreb are just beginning to be studied. Compared to written Arabic, dialects are under-resourced languages which suffer from lack of NLP resources despite their large use. We deal in this paper with Arabic Algerian dialect a non-resourced language for which no known resource is available to date. We present a first linguistic study introducing its most important features and we describe the resources that we created from scratch for this dialect.
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