Abstract
The feature type (FT) chosen for extraction from the text and presented to the classification algorithm (CAL) is one of the factors affecting text classification (TC) accuracy. Character N-grams, word roots, word stems, and single words have been used as features for Arabic TC (ATC). A survey of current literature shows that no prior studies have been conducted on the effect of using word N-grams (N consecutive words) on ATC accuracy. Consequently, we have conducted 576 experiments using four FTs (single words, 2-grams, 3-grams, and 4-grams), four feature selection methods (document frequency (DF), chi-squared, information gain, and Galavotti, Sebastiani, Simi) with four thresholds for numbers of features (50, 100, 150, and 200), three data representation schemas (Boolean, term frequency-inversed document frequency, and lookup table convolution), and three CALs (naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM)). Our results show that the use of single words as a feature provides greater classification accuracy (CA) for ATC compared to N-grams. Moreover, CA decreases by 17% on average when the number of N-grams increases. The data also show that the SVM CAL provides greater CA than NB and KNN; however, the best CA for 2-grams, 3-grams, and 4-grams is achieved when the NB CAL is used with Boolean representation and the number of features is 200.
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Al-Thubaity, A., Alhoshan, M., Hazzaa, I. (2015). Using Word N-Grams as Features in Arabic Text Classification. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-10389-1_3
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DOI: https://doi.org/10.1007/978-3-319-10389-1_3
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