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A deep autoencoder-based representation for Arabic text categorization

El-Alami, Fatima-Zahra and El Mahdaouy, Abdelkader and El Alaoui, Said Ouatik and En-Nahnahi, Noureddine (2020) A deep autoencoder-based representation for Arabic text categorization. Journal of Information and Communication Technology, 19 (3). pp. 381-398. ISSN 2180-3862

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Arabic text representation is a challenging assignment for several applications such as text categorization and clustering since the Arabic language is known for its variety, richness and complex morphology. Until recently, the Bag-of-Words remains the most common method for Arabic text representation. However, it suffers from several shortcomings such as semantics deficiency and high dimensionality of feature space. Moreover, most existing methods ignore the explicit knowledge contained in semantic vocabularies such as Arabic WordNet. To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. Our method allowed for the consideration of document semantics by combining both implicit and explicit semantics and reducing feature space dimensionality. To evaluate our method, we conducted several experiments on the standard Arabic dataset, OSAC. The obtained results showed the effectiveness of the proposed method compared to state-of-the-art ones.

Item Type: Article
Uncontrolled Keywords: Arabic text representation, deep autoencoder, feature selection, machine learning, text categorization.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Multimedia Technology & Communication
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 02 Feb 2021 02:52
Last Modified: 02 Feb 2021 02:52

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