Grammar-based question classification using ensemble learning algorithms

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.