Authors:
Alaa Alahmadi
;
Arash Joorabchi
and
Abdulhussain E. Mahdi
Affiliation:
University of Limerick, Ireland
Keyword(s):
Automatic Text Classification, Arabic Text, Bag-of-Words, Bag-of-Concepts, Wikipedia.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Concept Mining
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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 deploy
ed 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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