The Open-domain Question Answering system (QA) has been attached great attention for its capacity of providing compact and precise results for sers. The question classification is an essential part in the system, affecting the accuracy of it. The paper studies question classification through machine learning approaches, namely, different classifiers and multiple classifier combination method. By using compositive statistic and rule classifiers, and by introducing dependency structure from Minipar and linguistic knowledge from Wordnet into question representation, the research shows high accuracy in question classification.
Finally, works in REF used machine learning approaches, namely, different classifiers and multiple classifier combination method by using composite statistic and rule classifiers, and by introducing dependency structure from Minipar and linguistic knowledge from Wordnet into question representation, in addition, features like the Dependency Structure, Wordnet Synsets, Bag-of-Word, and Bi-gram were used.
Question Classification using Multiple Classifiers
Proceedings of the Fifth Workshop on Asian Language Resources (ALR-05) and First Symposium on Asian Language Resources Network (ALRN)
Computer Science