Abstract
Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.
Highlights
Nowadays, text classification has become a vital technique to classify unclassified contents to pre-defined classes
We have introduced a semantic model by using a vector space model (term frequency-inverse document frequency (TF-IDF)) and the cosine similarity approach to improve Arabic classification and topic discovery techniques
We aim at examining the impact of the proposed semantic clustering mechanism (SCM) on the Arabic classification performance, as well as comparing the performance of the SCM with three baseline methods: Support vector machine (SVM), naive Bayesian (NB), and decision tree (DT)
Summary
Text classification has become a vital technique to classify unclassified contents to pre-defined classes. Such a technique can help in finding interesting information and can enhance decision making techniques. We have used an Arabic ontology, which has been introduced by HAWALAH [20], to propose an Arabic multi-disciplinary ontology from multiple resources. This ontology consists of a number of topics, which are linked with sub-topics. We have introduced a semantic model by using a vector space model (term frequency-inverse document frequency (TF-IDF)) and the cosine similarity approach to improve Arabic classification and topic discovery techniques.
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