Document Type : Original/Review Paper

Authors

1 Department of Electrical and Computer Engineering, University of Hormozgan, Bandar Abbas, Iran and Deep Learning Research Group, University of Hormozgan, Bandar Abbas, Iran

2 Department of Electrical and Computer Engineering, University of Hormozgan, Bandar Abbas, Iran.

3 Markaz-e Elmi Karbordi Bandar Abbas 1, University of Applied Science and Technology, Farahani Boulevard, Bandar Abbas, 79199_33153, Iran.

10.22044/jadm.2021.10016.2142

Abstract

A Question Answering System (QAS) is a special form of information retrieval which 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 other following parts. This study uses effective features and ensemble classification to improve the QAS performance by increasing the accuracy of question type identification. We use the gravitational search algorithm to select the features and perform 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 algorithm and gravitational search algorithm. The experimental results show that the proposed method outperforms compared to state-of-the-art methods in previous researches.

Keywords

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