Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

Abstract

Natural Language Processing (NLP) approaches play a crucial role in classifying inquiries and comprehending human language in diverse applications. A Question Answering System (QAS) consists of three components are question processing, information retrieval, and answer selection. Question Answering Systems (QASs) are a distinct form of information retrieval. The most crucial aspect of QAS is deciding on the question type since it influences the other sections following. However, an important question-answering system requires a prominent question classification system. In the past, there are different methods to solve this problem, such as rule-based learning, and hybrid approaches. However, the problem with these methods is that the rules require a lot of effort to create and are very limited. In this study, the utilization of genetic algorithm and deep neural network techniques enhances the quality control problem-solving process. This research utilizes the UIUC dataset. This collection comprises 5452 questions designed for learning purposes and an additional 500 questions specifically intended for assessment. The suggested solution involves converting each query into a matrix, with each row representing the Word2vec of a word. Subsequently, a Genetic Algorithm (GA) is employed to identify the most optimal features. Ultimately, a Convolutional Neural Network is utilized for classification, yielding a remarkable accuracy of 98.2% in our experimentation with the question dataset.