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Chapitre D'ouvrage Année : 2019

Automatic identification methods on a corpus of twenty five fine-grained Arabic dialects

Résumé

This research deals with Arabic dialect identification, a challenging issue related to Arabic NLP. Indeed, the increasing use of Arabic dialects in a written form especially in social media generates new needs in the area of Arabic dialect processing. For discriminating between dialects in a multi-dialect context, we use different approaches based on machine learning techniques. To this end, we explored several methods. We used a classification method based on symmetric Kullback-Leibler, and we experimented classical classification methods such as Naive Bayes Classifiers and more sophisticated methods like Word2Vec and Long Short-Term Memory neural network. We tested our approaches on a large database of 25 Arabic dialects in addition to MSA.
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Dates et versions

hal-02314245 , version 1 (11-10-2019)

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Salima Harrat, Karima Meftouh, Karima Abidi, Kamel Smaïli. Automatic identification methods on a corpus of twenty five fine-grained Arabic dialects. Arabic Language Processing: From Theory to Practice 7th International Conference, ICALP 2019, Nancy, France, October 16–17, 2019, Proceedings, Communications in Computer and Information Science book series (CCIS, volume 1108), 2019, ⟨10.1007/978-3-030-32959-4_6⟩. ⟨hal-02314245⟩
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