Chinese question Classification using Multilevel Random Walk

Question classification is crucial for the automatically question answering. And Random Walk is a promising approach for semi-supervised learning problems of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled, the goal is to predict the labels of the unlabeled points. Since labeling often requires expensive human labor, whereas unlabelled data is easier to obtain, semi-supervised learning is very useful in many real-world problems, such as text classification. Here we proposed an approach for Chinese question Classification using Multilevel Random Walk (MRK), which is an improvement of random walk. In this paper, we selected four kinds of features (words, pos, named entity, semantic) to present Chinese questions, and carried out experiments to validate the method on a large-scale real-world dataset.