A Comparison of fastText Implementations Using Arabic Text Classification

2019 ◽  
pp. 306-311 ◽  
Author(s):  
Nuha Alghamdi ◽  
Fatmah Assiri
Keyword(s):  
Text Classification ◽  
Arabic Text ◽  
Arabic Text Classification

Arabic Text Classification using K-NN and Naive Bayes

2008 ◽  
Vol 4 (7) ◽  
pp. 600-605 ◽  
Author(s):  
Mohammed J. Bawaneh ◽  
Mahmud S. Alkoffash ◽  
Adnan I. Al Rabea
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Arabic Text ◽  
Arabic Text Classification

The Effect of Stemming on Arabic Text Classification

2011 ◽  
Vol 1 (3) ◽  
pp. 54-70 ◽  
Author(s):  
Abdullah Wahbeh ◽  
Mohammed Al-Kabi ◽  
Qasem Al-Radaideh ◽  
Emad Al-Shawakfa ◽  
Izzat Alsmadi
Keyword(s):  
Text Classification ◽  
Digital Libraries ◽  
Arabic Language ◽  
Support Vector ◽  
Svm Classifier ◽  
Arabic Text ◽  
Text Documents ◽  
Information Retrieval Systems ◽  
Arabic Text Classification ◽  
The Web

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%.

Arabic Stemming Techniques as Feature Extraction Applied in Arabic Text Classification

2017 ◽  
pp. 349-361
Author(s):  
Samir Boukil ◽  
Fatiha El Adnani ◽  
Abd Elmajid El Moutaouakkil ◽  
Loubna Cherrat ◽  
Mostafa Ezziyyani
Keyword(s):  
Feature Extraction ◽  
Text Classification ◽  
Arabic Text ◽  
Arabic Text Classification ◽  
Arabic Stemming
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