Nouf AlShenaifi


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Faheem at NADI shared task: Identifying the dialect of Arabic tweet
Nouf AlShenaifi | Aqil Azmi
Proceedings of the Fifth Arabic Natural Language Processing Workshop

This paper describes Faheem (adj. of understand), our submission to NADI (Nuanced Arabic Dialect Identification) shared task. With so many Arabic dialects being under-studied due to the scarcity of the resources, the objective is to identify the Arabic dialect used in the tweet, country wise. We propose a machine learning approach where we utilize word-level n-gram (n = 1 to 3) and tf-idf features and feed them to six different classifiers. We train the system using a data set of 21,000 tweets—provided by the organizers—covering twenty-one Arab countries. Our top performing classifiers are: Logistic Regression, Support Vector Machines, and Multinomial Na ̈ıve Bayes.


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Arib@QALB-2015 Shared Task: A Hybrid Cascade Model for Arabic Spelling Error Detection and Correction
Nouf AlShenaifi | Rehab AlNefie | Maha Al-Yahya | Hend Al-Khalifa
Proceedings of the Second Workshop on Arabic Natural Language Processing