Arabic text classification using Polynomial Networks

Al-Tahrawi, Mayy M.; Al-Khatib, Sumaya N.

doi:10.1016/j.jksuci.2015.02.003

Cited by 28 publications

(32 citation statements)

References 14 publications

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“…The experimental results showed that Neural Networks achieved good results (about 88.3% accuracy) but the running time was very high because of the high dimensional problem of the text documents. Al-Tahrawi and Al-Khatib in [11] utilized the Polynomial Networks to be used for Arabic text documents. In their experiment, the classifiers were trained to classify the category in the one-versus-all method.…”

Section: Related Workmentioning

confidence: 99%

Efficient Topic Detection System for Online Arabic News

Mohammed¹,

Marwa²

2018

IJCA

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Nowadays, the news is updated very frequently, especially in the Middle East region where the Arabic language is the primary language of all its countries. The people in this region are interested in following up these updates through the available online news platforms. In order to automate the work in the news agencies, there is an urgent need for an automated system that is able to detect the topic of the news once it has arrived at the agency. In this paper, an efficient system is presented for classifying the online Arabic news into its proper topic. The proposed system uses various natural language processing techniques along with different classification methods. The experimental results show that utilizing the Information Gain, as a feature selection technique, with the Naïve Bayes algorithm, achieves the best accuracy in order to solve the topic detection problem for the online Arabic news.

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Section: Related Workmentioning

confidence: 99%

Efficient Topic Detection System for Online Arabic News

Mohammed¹,

Marwa²

2018

IJCA

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“…The authors in [13] proposed using PNNs for Arabic TC. They have shown that PNN is a fast and highly accurate Arabic text classifier.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, PNNs have shown to be one of the top English text classifiers of Reuters and 20News Groups [14]- [17] . More recently, PNNs have been investigated in Arabic TC and have achieved very high classification accuracy [13].…”

Section: Polynomial Neural Network (Pnns)mentioning

confidence: 99%

“…In each class, 80% of the texts are used for training and 20% for testing. Many researches in Arabic TC have adopted this dataset using different algorithms [49], [20], [8], [13], [28].…”

Section: The Datasetmentioning

confidence: 99%

“…Some FS criteria that were used in Arabic TC research include Chi Square (CHI) [13]. [24]- [26], [55], [9], [7], Cross Validation [2], Document Frequency (DF) [24], [20], Information Gain(IG) [24], [20], [5], Correlation Coefficient (CC) [20], Semi-Automatic Categorization Method (SACM), Mutual Information (MI) [20], BPSO-KNN (Binary Particle Swarm Optimization-K-Nearest-Neighbor ) [8] and ACM (Automatic Categorization Method) [56].…”

Section: Feature Selection (Fs)mentioning

confidence: 99%

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Polynomial Neural Networks versus Other Arabic Text Classifiers

Al-Tahrawi¹

2016

JSW

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Many Text Classification (TC) algorithms have been proposed for Arabic TC. Polynomial Neural Networks (PNNs) were used recently in English TC, and have proved to be competitive to the state of the art text classifiers in this field. Lately, they were proposed for classifying Arabic documents. In this research paper, an experimental study that directly compares PNNs against five famous classification algorithms in TC is conducted on Aljazeera-News Arabic dataset. All experiments use the same TC settings, like preprocessing, Feature Selection (FS) and reduction criteria, feature weighting and classifier performance evaluation measures. These algorithms are: SVM (Support Vector Machines), NB (Naive Bayes), kNN (k-Nearest-Neighbor), LR (Logistic Regression) and RBF (Radial Basis Function networks). Results reached in this study reveal that PNN are competitive classifiers in the field of Arabic TC.

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A novel methodology for Arabic news classification

Alfonse

Gawich

2021

WIREs Data Min & Knowl

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The automated news classification concerns the assignment of news to one or more predefined categories. The automated classified news helps the search engines to mine and categorize the type of news that the user asks for. Most of the researchers focused on the classification of English news and ignore the Arabic news due to the complexity of the Arabic morphology. This article presents a novel methodology to classify the Arabic news. It relies on the use of features extraction and the application of machine learning classifiers which are the Naive Bayes (NB), the Logistic Regression (LR), the Random Forest (RF), the Xtreme Gradient Boosting (XGB), the K-Nearest Neighbors (KNN), the Stochastic Gradient Descent (SGD), the Decision Tree (DT), and the Multi-Layer Perceptron (MLP). The methodology is applied to the Arabic news dataset provided by Mendeley. The accuracy of the classification is more than 95%.

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Arabic text classification using Polynomial Networks

Cited by 28 publications

References 14 publications

Efficient Topic Detection System for Online Arabic News

Efficient Topic Detection System for Online Arabic News

Polynomial Neural Networks versus Other Arabic Text Classifiers

A novel methodology for Arabic news classification

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