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Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for the translation of texts of social media. More precisely, this paper focuses on the Tunisian Dialect of Arabic (TAD) with an application on automatic machine translation for a social media text into MSA and any other target language. Linguistic tools such as a bilingual TAD-MSA lexicon and a set of grammatical mapping rules are collaboratively constructed and exploited in addition to a language model to produce MSA sentences of Tunisian dialectal sentences. This work is a first-step towards collaboratively constructed semantic and lexical resources for Arabic Social Media within the ASMAT (Arabic Social Media Analysis Tools) project.

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Abstract-In this paper, we present a hybrid method for semi-automatic building of domain ontology from spoken dialogue corpus in Tunisian Dialect for the railway request information domain. The proposed method is based on a statistical method for term and concept extraction and a linguistic method for semantic relation extraction. This method consists of three fundamental phases, namely the corpus construction and treatment, the ontology construction and the ontology evaluation. The proposed method is implemented through the ABDO system to generate the RIO ontology that contains 14 concepts, 25 semantic relations and 387 concepts instances. The generated domain ontology is used to semantically label Tunisian dialect utterances in spoken dialogue.