Question Classification in Question Answering System using Combination of Ensemble Classification and Feature Selection

A Question Answering System (QAS) is a special form of information retrieval that consists of three parts: question processing, information retrieval, and answer selection. Determining the type of question is the most important part of QAS as it affects the other following parts. In this work, we use the effective features and ensemble classification in order to improve the QAS performance by increasing the accuracy of question type identification. We use the Gravitational Search Algorithm (GSA) in order to select the features and perform the ensemble classification. The proposed system is extensively tested on different datasets using four types of experiments: (1) neither feature selection nor ensemble classification, (2) feature selection without ensemble classification, (3) ensemble classification without feature selection, and (4) feature selection with ensemble classification. These four kinds of experiments are carried out under the Differential Evolution (DE) algorithm and GSA. The experimental results obtained show that the proposed method outperforms compared to the state-of-the-art methods in the previous research works.