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 ...

Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political inte...

A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, we propose a transfer learning framework to jointly use large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. We have chosen the Qatari Arabic (QA) dialect as a typical example for an under-resourced...

Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of...

A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resou...

In this paper we address the following questions from our experience of the last two and a half years in developing a large-scale corpus of Arabic text annotated for morphological information, part-of-speech, English gloss, and syntactic structure: (a) How did we ‘leapfrog’ through the stumbling blocks of both methodology and training in setting up the Penn Arabic Treebank (ATB) annotation? (b)...

We present an approach for automatic verification and augmentation of multilingual lexica. We exploit existing parallel and monolingual corpora to extract multilingual correspondents via triangulation. We demonstrate the efficacy of our approach on two publicly available resources: Tharwa, a three-way lexicon comprising Dialectal Arabic, Modern Standard Arabic and English lemmas among other inf...

_________________________________________________________________________________________________________ Speech clarity and coarticulatory effects in standard and dialectal Arabic This study deals with the co-variation of speech clarity and coarticulatory patterns. Two experiments were conducted to investigate the influence of two parameters, the speech style (formal vs. non formal) and the pr...

In this paper, we address the problem of the morphological analysis of an Arabic dialect. We propose a method to adapt an Arabic morphological analyzer for the Tunisian dialect (TD). In order to do that, we create a lexicon for the TD. The creation of the lexicon is done in two steps. The first step consists in adapting a Modern Standard Arabic (MSA) lexicon. We adapted a list of MSA derivation...

We have developed an experimental Arabic-to-English example-based machine translation (EBMT) system, which exploits a bilingual corpus to find examples that match fragments of the input source-language text--Modern Standard Arabic (MSA), in our case--and imitates its translations. Translation examples were extracted from a collection of parallel, sentencealigned, unvocalized Arabic-English docu...