A machine learning approach for Arabic text classification using N-gram frequency statistics |
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Authors: | Laila Khreisat |
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Institution: | 1. Programa de Pós-Graduação em Computação Aplicada—PPGCA, Universidade do Vale do Rio dos Sinos—UNISINOS, Av. Unisinos, 950, São Leopoldo, RS, Brazil;2. Artificial Intelligence Engineers—AIE, Rua Vieira de Castro, 262, Porto Alegre, RS, Brazil;1. TEAMLAB, Department of Industrial and Management, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea;2. NAVER Corp., Seongnam-si, Gyeonggi-do, Republic of Korea |
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Abstract: | 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. |
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