Language resources for Maghrebi Arabic dialects’ NLP: a survey

Diglossia is one of the main characteristics of Arabic language. In Arab countries, there are three forms of Arabic that co-exist: Classical Arabic (CA) which is mainly used in the Quran and in several classical literary texts, Modern Standard Arabic (MSA) that descends from CA and used as official...

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Published inLanguage resources and evaluation Vol. 54; no. 4; pp. 1079 - 1142
Main Authors Younes, Jihene, Souissi, Emna, Achour, Hadhemi, Ferchichi, Ahmed
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.12.2020
Springer Nature B.V
Subjects
Arabic language
Computational Linguistics
Computer Science
Dialects
Diglossia
Language and Literature
Language varieties
Latin language
Linguistics
Natural language processing
Official languages
Ontology
Polls & surveys
Regional dialects
Social Sciences
Survey
Maghreb (Northwestern Africa region)
Natural language processing
Ontology
Maghrebi Arabic dialects
Corpus
Lexicon
Language resources
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Summary:Diglossia is one of the main characteristics of Arabic language. In Arab countries, there are three forms of Arabic that co-exist: Classical Arabic (CA) which is mainly used in the Quran and in several classical literary texts, Modern Standard Arabic (MSA) that descends from CA and used as official language, and various regional colloquial varieties of Arabic that are usually referred to as Arabic dialects (AD). Deemed to be amongst low-resource languages, these dialects have aroused increased interest among the NLP community in recent years. Indeed, the various Arabic dialects are increasingly used on the social web and may be transcribed in both the Arabic and the Latin script. The latter is known as Arabizi and seems to be more frequently used for some of them. The AD NLP raises many challenges and requires the availability of large and appropriate language resources. In this study, we focus, in particular, on the Maghrebi Arabic dialects (MADs). We propose a thorough review of the language resources (LRs) that have been generated by the various work carried out on the MAD language processing. A survey of the currently online available MAD NLP dedicated-LRs is also compiled and discussed. LRs investigated in this work are essentially data-resources such as primary and annotated corpora, lexica, dictionaries, ontologies, etc.
ISSN:1574-020X
1574-0218
DOI:10.1007/s10579-020-09490-9