Effectiveness of Combined Features for Machine Learning Based Question Classification

抄録

Question classification is of crucial importance for question answering. In question classification, the accuracy of ML algorithms was found to significantly outperform other approaches. The two key issues in classification with a ML-based approach are classifier design and feature selection. Support Vector Machines is known to work well for sparse, high dimensional problems. However, the frequently used Bag-of-Words approach does not take full advantage of information contained in a question. To exploit this information we introduce three new feature types: Subordinate Word Category, Question Focus and Syntactic-Semantic Structure. As the results demonstrate, the inclusion of the new features provides higher accuracy of question classification compared to the standard Bag-of-Words approach and other ML based methods such as SVM with the Tree Kernel, SVM with Error Correcting Codes and SNoW. A classification accuracy of 85.6 % obtained using the three introduced feature types is, as of yet the highest reported in the literature, bringing error reduction of 27 % compared to the Bag-of-Words approach.