Combined Support Vector Machine and Pattern Matching for Arabic Islamic Hadith Question Classification System

Abstract

The dimensional phase of Arabic Language, question answering (QA) involves an intrinsic form of question classification (QC) that functions to perform an important task in question answering system (QAS). The purpose of QC is to precisely assign labels to questions that are majorly dependent on the form of answer type. Moreover, classification of user’s question is a herculean task based on the tractability that natural language (NL) affords with different forms. The information enshrined in a group of words is not sufficient to effectively classify the question in the quote. Until now, few reports have focused on QC for Arabic Language question answering (QA). The earlier report has employed the technique of handcrafted rules and keyword matching for QC. Nonetheless, these procedures are considered obsolete in terms of applying it to new territories. In this paper, we present a question-answering system combining aims on a combination of model fixed on Support Vector Machine (SVM) and pattern-based Matching techniques for Arabic Language question classification (ALQC). The Islamic Hadith purview on QA in the study was focusing on the effect of a feature set on the performance of SVM for QC. About five patterns were employed in the analysis together with the classification of three types of questions, namely “Who”, “Where” and “What”. The dataset employed in this study consisted of 200 questions on Arabic Islamic Hadith derived from Sahih Al-Bukhari. The performance generated for the F-measure values for “Who”, “Where” and “What” were 88.39%, 87.66% and 87.93% respectively. In this research work, we evaluate the metric performance to combine the SVM with Pattern Matching to get the accuracy answer. The outcome of this answer reflected that the proposed prototype of SVM and pattern-based approach is indispensible from the field of QC in the Arabic language.