A machine learning approach for Arabic text classification using N-gram frequency statistics
In this paper a machine learning approach for classifying Arabic text documents is presented. To handle the high dimensionality of text documents, embeddings are used to map each document (instance) into R (the set of real numbers) representing the tri-gram frequency statistics profiles for a document. Classification is achieved by computing a dissimilarity measure, called the Manhattan distance, between the profile of the instance to be classified and the profiles of all the instances in the training set. The class (category) to which an instance (document) belongs is the one with the least computed Manhattan measure. The Dice similarity measure is used to compare the performance of method. Results show that tri-gram text classification using the Dice measure outperforms classification using the Manhattan measure.
machine learning arabic classification n-gram data mining categorization