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 |
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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 | |
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 |
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Publisher | Springer |
Volume | 494 |
ISSN (Print) | 1865-1348 |
ISSN (Electronic) | 1865-1356 |
Conference
Conference | 18th International Conference on Web Information Systems and Technologies |
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Abbreviated title | (WEBIST 2022) |
Country/Territory | Malta |
City | Valletta |
Period | 25/10/22 → 27/10/22 |
Internet address |
Keywords
- question classification
- grammatical features
- factoid questions, information retrieval
- machine learning
- ensemble learning