Title: A regression model to evaluate interactive question answering using GEP

Authors: Mohammad Mehdi Hosseini

Addresses: Department of Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran

Abstract: Evaluation plays a pivotal role in the interactive question answering (IQA) systems. However, much uncertainty still exists on evaluating IQA systems and there is practically no specific methodology to evaluate these systems. One of the main challenges in designing an assessment method for IQA systems lies in the fact that it is rarely possible to predict the interaction part. To this end, a human needs to be involved in the evaluation process. In this paper, an appropriate model is presented by introducing a set of characteristics features for evaluating IQA systems. Data were collected from four IQA systems at various timespans. For the purpose of analysis, pre-processing is performed on each conversation, the statistical characteristics of the conversations are extracted to form the characteristic matrix. The characteristics matrix is classified into three separate clusters using K-means. Then, an equation is allotted to each of the clusters with an application of gene expression programming (GEP). The results reveal that the proposed model has the least error with an average of 0.09 root mean square error between real data and GEP model.

Keywords: evaluation; interactive question; answering systems; nonlinear regression; gene expression programming; GEP; feature extraction.

DOI: 10.1504/IJBIDM.2022.124849

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.2, pp.210 - 232

Received: 03 Jul 2020
Accepted: 17 Feb 2021

Published online: 11 Aug 2022 *

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