The effect of training set size in authorship attribution: application on short arabic texts

Mohammed Al-Sarem, Abdel-Hamid Emara


Authorship attribution (AA) is a subfield of linguistics analysis, aiming to identify the original author among a set of candidate authors. Several research papers were published and several methods and models were developed for many languages. However, the number of related works for Arabic is limited. Moreover, investigating the impact of short words length and training set size is not well addressed. To the best of our knowledge, no published works or researches, in this direction or even in other languages, are available. Therefore, we propose to investigate this effect, taking into account different stylomatric combination. The Mahalanobis distance (MD), Linear Regression (LR), and Multilayer Perceptron (MP) are selected as AA classifiers. During the experiment, the training dataset size is increased and the accuracy of the classifiers is recorded. The results are quite interesting and show different classifiers behaviours. Combining word-based stylomatric features with n-grams provides the best accuracy reached in average 93%.


authorship attribution, training set size, arabic language, MLP classifier, linear regression, mahalonobis distance

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578