A6105119119

Question Classification using a Rule based Model
Aarthi D¹, Viswanathan V², Nandhini B³, Ilakiyaselvan N^4
1Aarthi D, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
²Viswanathan V, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
³Nandhini B, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
⁴Ilakiyaselvan N, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4172-4176 | Volume-9 Issue-1, November 2019. | Retrieval Number: A6105119119/2019©BEIESP | DOI: 10.35940/ijitee.A6105.119119
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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 is one of the most common applications for data acquisition. Although the majority of text-mining applications strive to improve the user experience and the tools used to find appropriate answers, the problems still exist because the web content is constantly increasing. The Questions Classification (QC) task is one of the main tasks in improving the classification system is to classify types of questions in the text mining application. A large number of QC methods are introduced to help resolve classification problems, most of which are bag of words approaches. In this project, we propose a QC system that uses Parts of Speech (POS) Tagger and Named Entity Recognition (NER) Tagger from the Stanford core Natural Language Processing (NLP) to classify the questions correctly. We started by cleaning the data by removing the available labels in the questions then we proceed by tagging the questions by splitting words and tagging each and every words in the input question with the POS Tagger. After this step, we will convert them into a pattern without changing the structure of the question. Then we proceed by tagging the question with NER Tagger. Finally, we will do confirmation process for certain question types which is performed by confirming question type module to make the system work efficiently.
Keywords: Natural Language Processing, Text mining, Question Answering System, Data Mining.
Scope of the Article: Natural Language Processing