PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION

Arabic Text Categorization

In this paper, we compare the performance of three classifiers for Arabic text categorization. In particular, the naïve Bayes, k-nearest-neighbors (knn), and distance-based classifiers were used. Unclassified documents were preprocessed by removing punctuation marks and stopwords. Each document is then represented as a vector of words (or of words and their frequencies as in the case of the naï...

Word sense disambiguation for arabic text categorization

In this paper, we present two contributions for Arabic Word Sense Disambiguation. In the first one, we propose to use both two external resources AWN and WN based on Term to Term Machine Translation System (MTS). The second contribution relates to the disambiguation strategies, it consists of choosing the nearest concept for the ambiguous terms, based on more relationships with different concep...

Machine learning for Arabic text categorization

An Intelligent System for Arabic Text Categorization

Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and feature selection are tried. Moreover, the document is represented using several term weighting ...

Feature reduction techniques for Arabic text categorization

This paper presents and compares three feature reduction techniques that were applied to Arabic text. The techniques include stemming, light stemming, and word clusters. The effects of the aforementioned techniques were studied and analyzed on the K-nearest-neighbor classifier. Stemming reduces words to their stems. Light stemming,by comparison, removes commonaffixes from words without reducing...