Robust Arabic Text Categorization by Combining Convolutional and Recurrent Neural Networks

Abstract

Text Categorization is an important task in the area of Natural Language Processing (NLP). Its goal is to learn a model that can accurately classify any textual document for a given language into one of a set of predefined categories. In the context of the Arabic language, several approaches have been proposed to tackle this problem, many of which are based on the bag-of-words assumption. Even though these methods usually produce good results for the classification task, they often fail to capture contextual dependencies from textual data. On the other hand, deep learning architectures that are usually based on Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs) do not suffer from such a limitation and have recently shown very promising results in various NLP applications. In this work, we use deep learning models that combine RNN and CNN for the task of Arabic text categorization using static, dynamic, and fine-tuned word embeddings. The experimental results reported on the Open Source Arabic Corpora (OSAC) dataset have shown the effectiveness and high performance of our proposed models.