An evaluation of classification models for question topic categorization
Bo Yang Qu, Gao Cong, Cuiping Li, Aixin Sun, Hong Chen · Journal of the American Society for Information Science and Technology · 2012
We study the problem of question topic classification using a very large real‐worldCommunityQuestionAnswering (CQA) dataset fromYahoo!Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions intoCQAcategories: (a) the usefulness of n‐gram features and bag‐of‐word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state‐of‐the‐art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components ofCQAdata, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real‐world classification problems.