Building a Question Classification Model for a Malay Question Answering System
Nurnasran Puteh1, Mohd. Zabidin Husin2, Hatim Mohamad Tahir3, Azham Hussain4
1Nurnasran Puteh, School of Computing, College of Arts and Science, University Utara Malaysia, Sintok, Malaysia
2Mohd. Zabidin Husin, School of Computing, College of Arts and Science, University Utara Malaysia, Sintok, Malaysia
3Hatim Mohamad Tahir, School of Computing, College of Arts and Science, University Utara Malaysia, Sintok, Malaysia
4Azham Hussain, School of Computing, College of Arts and Science, University Utara Malaysia, Sintok, Malaysia
Manuscript received on 03 February 2019 | Revised Manuscript received on 10 February 2019 | Manuscript Published on 22 March 2019 | PP: 184-190 | Volume-8 Issue-5S April 2019 | Retrieval Number: ES3415018319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Question answering system (QAS) is an example of an application of natural language processing where it is able to automatically return a specific answer to a question given in a natural language by a human. One of the important tasks in QAS is Question Classification which is the task to identify the semantic type of the required answer for the question posed to the QAS. Identifying the correct answer type is an important process before the required correct answer can be retrieved by the system. In this paper we presents a model of Answer Type Classification using machine learning approach targeted for a Malay QAS for the Quran, which is a restricted-domain QAS. The performance of the classification model using three different machine learning classification algorithms, namely Naïve Bayes, Random Forest and Support Vector Machine (SVM), are then evaluated. The results show that the classifier based on SVM has the best overall results in terms of accuracy, precision, recall and F1-score.
Keywords: Malay Question Answering, Question Classification, Machine learning, Quran, QAS.
Scope of the Article: Classification