Bayesian Probability and Tanimoto Based Recurrent Neural Network for Question Answering System

Authors

  • Veeraraghavan Jagannathan Associate Professor, Department of Computer Science and Engineering, Sri Vasavi Engineering College (Autonomouus), Pedatadepalli, Tadepalligudem-534101. Andhra Pradesh, India

DOI:

https://doi.org/10.13052/jwe1540-9589.20315

Keywords:

Question answering system, question classification, recurrent neural network, Bayesian probability, machine learning

Abstract

Question Answering (QA) has become one of the most significant information retrieval applications. Despite that, most of the question answering system focused to increase the user experience in finding the relevant result. Due to the continuous increase of web content, retrieving the relevant result faces a challenging issue in the Question Answering System (QAS). Thus, an effective Question Classification (QC), and retrieval approach named Bayesian probability and Tanimoto-based Recurrent Neural Network (RNN) are proposed in this research to differentiate the types of questions more efficiently. This research presented an analysis of different types of questions with respect to the grammatical structures. Various patterns are identified from the questions and the RNN classifier is used to classify the questions. The results obtained by the proposed Bayesian probability and Tanimoto-based RNN showed that the syntactic categories related to the domain-specific types of proper nouns, numeral numbers, and the common nouns enable the RNN classifier to reveal better result for different types of questions. However, the proposed approach obtained better performance in terms of precision, recall, and F-measure with the values of 90.14, 86.301, and 90.936 using dataset-2.

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Author Biography

Veeraraghavan Jagannathan, Associate Professor, Department of Computer Science and Engineering, Sri Vasavi Engineering College (Autonomouus), Pedatadepalli, Tadepalligudem-534101. Andhra Pradesh, India

Veeraraghavan Jagannathan is a researcher in Machine Learning, NLP and deep learning. He received a Post Graduate in Computer Science and Engineering from Anna University, Chennai, India and obtained his Ph.D Degree in Computer Science, from the prestigious National Institute of Technology, Trichy, India in 2008 and 2017 respectively. He has over a decade of research experience and 20 years of academic experience. He has published papers in reputed international journals. His current research areas include, but not limited to Data Analytics, GANs for NLP, Computer Vision and CNN, Medical Image Processing, and Medical Data Analytics and predictive analytics.

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Published

2021-06-10

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Section

Articles