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, semisupervised 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.
Chinese question classification Lazy random walk semi-supervised learning
Kepei Zhang Jieyu Zhao
Research Institute of Computer Science and Technology Ningbo University Ningbo, China
国际会议
厦门
英文
515-519
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）