Grammar-based question classification using ensemble learning algorithms

Alaa Mohasseb, Andreas Kanavos

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


Question Classification is one of the important applications of information retrieval, as it plays a crucial role in improving the performance of question-answering systems. Differentiating between factoid and non-factoid questions is a particularly difficult task. Different methods have been suggested to improve the identification and classification of factoid questions. Most of these methods rely on semantic features and bag-of-words. This research paper explores the utilisation of a Grammar-based framework for Questions Categorisation and Classification (GQCC) to identify question types. This framework incorporates features such as grammatical features, domain-specific features, and patterns. These features leverage the question structure. By employing Ensemble Learning models, experimental findings demonstrate that the integration of question grammatical features with Ensemble Learning models contributes to achieving a good level of accuracy.
Original languageEnglish
Title of host publicationWeb Information Systems and Technologies: 18th International Conference, WEBIST 2022
EditorsMassimo Marchiori, Francisco José Domínguez Mayo, Joaquim Filipe
Number of pages14
ISBN (Electronic)9783031430886
ISBN (Print)9783031430879
Publication statusPublished - 29 Aug 2023
Event18th International Conference on Web Information Systems and Technologies - Valletta, Malta
Duration: 25 Oct 202227 Oct 2022

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356


Conference18th International Conference on Web Information Systems and Technologies
Abbreviated title(WEBIST 2022)
Internet address


  • question classification
  • grammatical features
  • factoid questions, information retrieval
  • machine learning
  • ensemble learning

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