Arabic Text Classification using Bag-of-Concepts Representation

Alaa Alahmadi, Arash Joorabchi, Abdulhussain E. Mahdi

2014

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.

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Paper Citation


in Harvard Style

Alahmadi A., Joorabchi A. and E. Mahdi A. (2014). Arabic Text Classification using Bag-of-Concepts Representation . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 374-380. DOI: 10.5220/0005138103740380


in Bibtex Style

@conference{kdir14,
author={Alaa Alahmadi and Arash Joorabchi and Abdulhussain E. Mahdi},
title={Arabic Text Classification using Bag-of-Concepts Representation},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={374-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005138103740380},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Arabic Text Classification using Bag-of-Concepts Representation
SN - 978-989-758-048-2
AU - Alahmadi A.
AU - Joorabchi A.
AU - E. Mahdi A.
PY - 2014
SP - 374
EP - 380
DO - 10.5220/0005138103740380