Grammar-based question classification using ensemble learning algorithms

Alaa Mohasseb; Andreas Kanavos

doi:10.1007/978-3-031-43088-6_5

Grammar-based question classification using ensemble learning algorithms

Alaa Mohasseb, Andreas Kanavos

School of Computing

Ionian University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

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 language	English
Title of host publication	Web Information Systems and Technologies: 18th International Conference, WEBIST 2022
Editors	Massimo Marchiori, Francisco José Domínguez Mayo, Joaquim Filipe
Publisher	Springer
Chapter	5
Pages	84-97
Number of pages	14
ISBN (Electronic)	9783031430886
ISBN (Print)	9783031430879
DOIs	https://doi.org/10.1007/978-3-031-43088-6_5
Publication status	Published - 29 Aug 2023
Event	18th International Conference on Web Information Systems and Technologies - Valletta, Malta Duration: 25 Oct 2022 → 27 Oct 2022 https://webist.scitevents.org/

Publication series

Name	Lecture Notes in Business Information Processing
Publisher	Springer
Volume	494
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	18th International Conference on Web Information Systems and Technologies
Abbreviated title	(WEBIST 2022)
Country/Territory	Malta
City	Valletta
Period	25/10/22 → 27/10/22
Internet address	https://webist.scitevents.org/

Keywords

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

Access to Document

10.1007/978-3-031-43088-6_5

Grammar_Based_Question_Classification_using_Ensemble_Learning_Algorithms
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-43088-6_5.
Accepted author manuscript (Post-print), 384 KB

Cite this

Mohasseb, A., & Kanavos, A. (2023). Grammar-based question classification using ensemble learning algorithms. In M. Marchiori, F. J. Domínguez Mayo, & J. Filipe (Eds.), Web Information Systems and Technologies: 18th International Conference, WEBIST 2022 (pp. 84-97). (Lecture Notes in Business Information Processing ; Vol. 494). Springer. https://doi.org/10.1007/978-3-031-43088-6_5

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title = "Grammar-based question classification using ensemble learning algorithms",

abstract = "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.",

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Mohasseb, A & Kanavos, A 2023, Grammar-based question classification using ensemble learning algorithms. in M Marchiori, FJ Domínguez Mayo & J Filipe (eds), Web Information Systems and Technologies: 18th International Conference, WEBIST 2022. Lecture Notes in Business Information Processing , vol. 494, Springer, pp. 84-97, 18th International Conference on Web Information Systems and Technologies, Valletta, Malta, 25/10/22. https://doi.org/10.1007/978-3-031-43088-6_5

Grammar-based question classification using ensemble learning algorithms. / Mohasseb, Alaa; Kanavos, Andreas.
Web Information Systems and Technologies: 18th International Conference, WEBIST 2022. ed. / Massimo Marchiori; Francisco José Domínguez Mayo; Joaquim Filipe. Springer, 2023. p. 84-97 (Lecture Notes in Business Information Processing ; Vol. 494).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Kanavos, Andreas

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ER -

Grammar-based question classification using ensemble learning algorithms

Abstract

Publication series

Conference

Keywords

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Cite this