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).

Corpus Resources for Descriptive and Applied Studies. Current Challenges and Future Directions: Selected Papers from the 5th International Conference on Corpus Linguistics (CILC2013)International audienceParallel corpora are not available for all domains and languages, but statistical methods in multilingual research domains require huge parallel/comparable corpora. Comparable corpora can be used when the parallel is not sufficient or not available for specific domains and languages. In this paper, we propose a method to extract all comparable articles from Wikipedia for multiple languages based on interlanguge links. We also extract comparable articles from Euro News website. We also present two comparability measures (CM) to compute the degree of comparability of multilingual articles. We extracted about 40K and 34K comparable articles from Wikipedia and Euro News respectively in three languages including Arabic, French, and English. Experimental results of comparability measures show that our measure can capture the comparability of multilingual corpora and allow to retrieve articles from different language concerning the same topic

This paper addresses the issue of comparability of comments extracted from Youtube. The comments concern spoken Algerian that could be either local Arabic, Modern Standard Arabic or French. This diversity of expression gives rise to a huge number of problems concerning the data processing. In this article, several methods of alignment will be proposed and tested. The method which permits to best align is Word2Vecbased approach that will be used iteratively. This recurrent call of Word2Vec allows us improve significantly the results of comparability. In fact, a dictionary-based approach leads to a Recall of 4, while our approach allows one to get a Recall of 33 at rank 1. Thanks to this approach, we built from Youtube CALYOU, a Comparable Corpus of the spoken Algerian.