This paper presents the first phase of building YADAC ― a multi-genre Dialectal Arabic (DA) corpus ― that is compiled using Web data from microblogs (i.e. Twitter), blogs/forums and online knowledge market services in which both questions and answers are user-generated. In addition to introducing two new genres to the current efforts of building DA corpora (i.e. microblogs and question-answer pairs extracted from online knowledge market services), the paper highlights and tackles several new issues related to building DA corpora that have not been handled in previous studies: function-based Web harvesting and dialect identification, vowel-based spelling variation, linguistic hypercorrection and its effect on spelling variation, unsupervised Part-of-Speech (POS) tagging and base phrase chunking for DA. Although the algorithms for both POS tagging and base-phrase chunking are still under development, the results are promising.

This paper describes the first phase of building a lexicon of Egyptian Cairene Arabic (ECA) ― one of the most widely understood dialects in the Arab World ― and Modern Standard Arabic (MSA). Each ECA entry is mapped to its MSA synonym, Part-of-Speech (POS) tag and top-ranked contexts based on Web queries; and thus each entry is provided with basic syntactic and semantic information for a generic lexicon compatible with multiple NLP applications. Moreover, through their MSA synonyms, ECA entries acquire access to MSA available NLP tools and resources which are considerably available. Using an associationist approach based on the correlations between word co-occurrence patterns in both dialects, we change the direction of the acquisition process from parallel to circular to overcome a bottleneck of current research on Arabic dialects, namely the lack of parallel corpora, and to alleviate accuracy rates for using unrelated Web documents which are more frequently available. Manually evaluated for 1,000 word entries by two native speakers of the ECA-MSA varieties, the proposed approach achieves a promising F-measured performance rate of 70.9%. In discussion to the proposed algorithm, different semantic issues are highlighted for upcoming phases of the induction of a more comprehensive ECA-MSA lexicon.