Term Weighting Schemes for Question Categorization

Abstract

Term weighting has proven to be an effective way to improve the performance of text categorization. Very recently, with the development of user-interactive question answering or community question answering, there has emerged a need to accurately categorize questions into predefined categories. However, as a question is usually a piece of short text, can the existing term-weighting methods perform consistently in question categorization as they do in text categorization? The answer is not clear, since to the best of our knowledge, we have not seen any work related to this problem despite of its significance. In this study, we investigate the popular unsupervised and supervised term-weighting methods for question categorization. At the same time, we propose three new supervised term-weighting methods, namely, qf*icf, iqf*qf*icf, and vrf. Comparisons of them with existing unsupervised and supervised term-weighting methods are made through a series of experiments on question collections of Yahoo! Answers. The experimental results show that iqf*qf*icf achieves the best performance among all term-weighting methods, while qf*icf and vrf are also competitive for question categorization. Meanwhile, tf*OR is proven to be the most significant one among existing methods. In addition, iqf*qf*icf and vrf are also effective for long document categorization.