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BENHA@ IDAT: Improving Irony Detection in Arabic Tweets using Ensemble Approach.

FIRE (Working Notes) • 2019
العودة
معلومات البحث
المؤلفون Hamada A Nayel, Walaa Medhat, Metwally Rashad
الكلمات المفتاحية Irony Detection, NLP
المجلة العلمية FIRE (Working Notes)
الناشر Not Available
المجلد Not Available
العدد Not Available
الصفحات 401-408
publication.type International
رابط البحث Open Link
المواد المرفقة Not Available
الملخص
This paper describes the methods and experiments that have been used in the development of our model submitted to Irony Detection for Arabic Tweets shared task. We submitted three runs based on our model using Support Vector Machines (SVM), Linear and Ensemble classifiers. Bag-of-Words with range of n-grams model have been used for feature extraction. Our submissions achieved accuracies of 82.1%, 81.6% and 81.1% for ensemble based, SVM and linear classifiers respectively.