Bayesian Network Model for Oath Statement Retrieval: A Case Study in Quranic Text Using Machine Learning Techniques

Abstract

Automated text classification by means of machine learning algorithms has been measured recently as a necessary process to handle and process Quranic studies. In general, machine learning algorithms participate an important task in feature extraction and topic retrieval. This paper presents an oath detection approach based on a Bayesian model to extract oath style markers that fulfill automated oath topic retrieval in the Quranic text. The experiments were performed on large and small datasets that contain oath using several machine language algorithms. The results obtained that machine learning algorithms are well performed in small-size dataset contains higher percentage of oath statement, and IBK and Multilayer Perceptron classifiers performed better results compare to J48 and BN classifiers.
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