An integrated pattern matching and machine learning approach for question classification

In question answering system, the process of classifying a question to appropriate class and identification of the focus word play key role in determining accurate answer. In this paper, we propose an integrated pattern matching and machine learning approach for higher education domain that focuses on factoid question answering. We have developed a question taxonomy for higher education domain and defined 9 coarse classes and 63 fine classes. We adopted pattern matching for the primary stage of classification and focus word identification and used machine learning approach i.e., Support Vector Machine (SVM) for the secondary classification approach only to those questions whose pattern are not present in question pattern corpus. Our experimental result shows that the accuracy of question classification using integrated approach outperforms the accuracy shown by individual approaches. SVM enhances the classification accuracy while focus word identification is achieved by virtue of pattern matching. The integrated approach shows the accuracy of 92.5% and 87.8% for coarse and fine class respectively and achieved focus word identification up to 83.4%.