Arabic Text Classification using Bag-of-Concepts Representation

Authors: Alaa, Alahmadi; Arash, Joorabchi; Abdulhussain E., Mahdi
Year: 2014
Venue: In Proceedings of the Sixth International Conference on Knowledge Discovery and Information Retrieval (KDIR 2014), pp. 374-380, Rome, Italy, 21-24 October 2014.
Link: http://dx.doi.org/10.5220/0005138103740380
Product of the Action: No

Abstract:
With the exponential growth of Arabic text in digital form, the need for efficient organization, navigation and browsing of large amounts of documents in Arabic has increased. Text Classification (TC) is one of the important subfields of data mining. The Bag-of-Words (BOW) representation model, which is the traditional way to represent text for TC, only takes into account the frequency of term occurrence within a document. Therefore, it ignores important semantic relationships between terms and treats synonymous words independently. In order to address this problem, this paper describes the application of a Bag-of-Concepts (BOC) text representation model for Arabic text. The proposed model is based on utilizing the Arabic Wikipedia as a knowledge base for concept detection. The BOC model is used to generate a Vector Space Model, which in turn is fed into a classifier to categorize a collection of Arabic text documents. Two different machine-learning based classifiers have been deployed to evaluate the effectiveness of the proposed model in comparison to the traditional BOW model. The results of our experiment show that the proposed BOC model achieves an improved performance with respect to BOW in terms of classification accuracy.