文章基本信息

标题：CLASSIFYING ARABIC TEXT USING DEEP LEARNING
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作者：MOHAMED GALAL ; MAGDA M. MADBOULY ; ADEL EL-ZOGHBY 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：23
页码：3412-3422
出版社：Journal of Theoretical and Applied
摘要：Nowadays, the volume of data offered on the Internet is growing every moment, and the necessity to analyze these data and convert to useful information increased. There are several types of research exploring techniques to deal with Text Classification (TC) in many languages; however, In Arabic, the researches are limited. TC is the process of categorizing text document into classes or categories according to the text contents. This research will focus on classifying Arabic Text using a Convolution neural network (CNN), which considered one of deep learning (DL) methods, as it achieved an excellent result in different Natural language processing (NLP) project types [1],[2],[3]. We also introduced a novel algorithm to group similar Arabic words based on extra Arabic letters and word embeddings distances. We named this algorithm as GStem.
关键词：Arabic Text Classification; Gstem; Neural Network; Deep Learning