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

Alaa Mohasseb; Andreas Kanavos

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Paper

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

Topics: Data Web Mining; Web Information Filtering and Retrieval

In Proceedings of the 18th International Conference on Web Information Systems and Technologies WEBIST - Volume 1, 265-271, 2022 , Valletta, Malta

Authors: Alaa Mohasseb ¹ and Andreas Kanavos ²

Affiliations: ¹ School of Computing, University of Portsmouth, Portsmouth, U.K. ; ² Department of Digital Media and Communication, Ionian University, Kefalonia, Greece

Keyword(s): Question Classification, Grammatical Features, Factoid Questions, Information Retrieval, Machine Learning, Ensemble Learning.

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.

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Paper citation in several formats:

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 - WEBIST; ISBN 978-989-758-613-2; ISSN 2184-3252, SciTePress, pages 265-271. DOI: 10.5220/0011525900003318

@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 - WEBIST},
year={2022},
pages={265-271},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011525900003318},
isbn={978-989-758-613-2},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST
TI - Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach
SN - 978-989-758-613-2
IS - 2184-3252
AU - Mohasseb, A.
AU - Kanavos, A.
PY - 2022
SP - 265
EP - 271
DO - 10.5220/0011525900003318
PB - SciTePress