Text Classification using Bi-Gram Alphabet Document Vector Representation
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2018 by IJCTT Journal | ||
| Volume-60 Number-2 |
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| Year of Publication : 2018 | ||
| Authors : Fatma Elghannam | ||
| DOI : 10.14445/22312803/IJCTT-V60P114 |
Fatma Elghannam "Text Classification using Bi-Gram Alphabet Document Vector Representation". International Journal of Computer Trends and Technology (IJCTT) V60(2):91-98 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract
Text classification TC is the process of assignment of text documents to appropriate categories based on their content. High dimensionality of feature space is a primary challenge in TC. The most common approach for TC is bag of words BOW which is limited due to the continuous increase in the number of features as the volume of vocabulary increases. Many investigators have addressed the issue of management of dimensionality by applying careful preprocessing techniques that include complex morphological phase, in particular for the high inflectional languages including Arabic. In the present study, term frequency of bi-gram alphabet is used to construct document vector. A main contribution of bi-gram alphabet approach is that feature terms are standard and separate from documents contents; this helps to reduce the high dimensionality associated with the increasing the volume of data. In addition, the classification process performs well on both Arabic and English collections without morphological preprocessing requirements. The proposed approach has proved high accuracy results and outperformed other Arabic TC systems.
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Keywords
Text classification, Arabic document, bi-gram alphabet, feature selection, support vector machine.