Factoid vs. non-factoid question identification: an ensemble learning approach

Alaa Mohasseb, Andreas Kanavos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST)
EditorsStefan Decker, Francisco Domínguez Mayo, Massimo Marchiori, Joaquim Filipe
PublisherSciTePress
Pages265-271
ISBN (Print)9789897586132
DOIs
Publication statusPublished - 28 Oct 2022
Event18th International Conference on Web Information Systems and Technologies - Valletta, Malta
Duration: 25 Oct 202227 Oct 2022
https://webist.scitevents.org/

Publication series

NameScitePress WEBIST Proceedings Series
PublisherScitePress
ISSN (Print)2184-3252

Conference

Conference18th International Conference on Web Information Systems and Technologies
Abbreviated title(WEBIST 2022)
Country/TerritoryMalta
CityValletta
Period25/10/2227/10/22
Internet address

Keywords

  • Question Classification
  • Grammatical Features
  • Factoid Questions
  • Information Retrieval
  • Machine Learning
  • Ensemble Learning

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