Automated Arabic Text Classification with P-Stemmer, Machine Learning, and a Tailored News Article Taxonomy

Arabic news articles in electronic collections are difficult to work with. Browsing by category is rarely supported. While helpful machine learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a QNRF funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237K Arabic news articles, which should be applicable to other Arabic news collections as well. We designed a simple taxonomy for Arabic news stories that is suitable for the needs in Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic-speaking volunteers. We developed tailored stemming (i.e., a new Arabic light stemmer) and automatic classification methods (the best being binary SVM classifiers) to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10-fold cross-validation and the Wilcoxon signed-rank test, we showed that our approach to stemming and classification is superior to state-of-the-art techniques.