Classifiers for Arabic NLP: survey. (28th February 2020)

• Record Type: Journal Article
• Authors: Omari, Marwan Al; Al-Hajj, Moustafa
• Is Part Of: International journal of computational complexity and intelligent algorithms. Volume 1:Number 3(2019)
• Page Start: 231
• Page End: 258
• Publication Date: 2020-02-28
• Subjects: lexicon-based approach -- corpus-based approach -- machine learning -- deep learning -- classification -- big data -- classifier -- Arabic NLP -- natural language processing -- NLP -- classification approach -- NLP lexicon-based -- NLP machine learning; Computational complexity -- Periodicals; Heuristic algorithms -- Periodicals; Neural networks (Computer science) -- Periodicals; 006.3
• Languages: English
• ISSNs: 2048-4720

Abstract

In this paper, we reviewed most common-used models and classifiers that used for the Arabic language to classify texts into categories, classes, or topics in tasks of opinion mining, sentence categorisation, part of speech tagging, language identification, name entity recognition, authorship attribution, word sense disambiguation, and text classification. Comparisons between classification tasks conducted in terms of models' performances and accuracies. Classification approaches are three types: lexicon-based, machine and deep learning, or hybrid ones. Research sample is 34 articles in the classification domain. Challenges facing the Arabic language discussed with further solutions: 1) solid research training on both approaches: lexicon-based and corpus-based (machine and deep learning); 2) research contribution mainly corpus, approach technique, and free accessibility; 3) fund increase to the research development in the Arab world.