Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System

Abstract

Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively.