Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach

Alaa Mohasseb, Andreas Kanavos

2022

Abstract

Question Classification is one of the most important applications of information retrieval. Identifying the correct question type constitutes the main step to enhance the performance of question answering systems. However, distinguishing between factoid and non-factoid questions is considered a challenging problem. In this paper, a grammatical based framework has been adapted for question identification. Ensemble Learning models were used for the classification process in which experimental results show that the combination of question grammatical features along with the ensemble learning models helped in achieving a good level of accuracy.

Paper Citation

in Harvard Style

Mohasseb A. and Kanavos A. (2022). Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-613-2, pages 265-271. DOI: 10.5220/0011525900003318

in Bibtex Style

@conference{webist22,
author={Alaa Mohasseb and Andreas Kanavos},
title={Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2022},
pages={265-271},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011525900003318},
isbn={978-989-758-613-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach
SN - 978-989-758-613-2
AU - Mohasseb A.
AU - Kanavos A.
PY - 2022
SP - 265
EP - 271
DO - 10.5220/0011525900003318