Hierarchical text categorization with probabilistic topics
Enhong Chen · 2009
Probabilistic topic model is a statistical generative model for automatically extracting a set of topics from a collection of documents and then representing these documents as mixtures of topics.Topics obtained by this method pick out significant semantic information of documents,and they have broad applications in many fields.A novel approach was proposed for hierarchical text categorization based on the probabilistic topic model.The approach first extracted a set of topics based on Gibbs sampling,then computed the similarities between test documents and each class based on the topics.Results of experiments on 20 NewsGroups dataset show that this approach is able to produce superior classification performance when compared to support vector machines.