Bayesian approaches to genre identification of Chinese finance text

Document genre information is one of the most distinguishing features in information retrieval, which brings order to the search results. What the genre classification concerned is not the topic but the genre of document. We examine the effectiveness of using non-machine learning techniques to solve genre classification of Chinese text with the same topic, viz. finance. We present two simple but effective methods based on Bayes rule for genre classification in this paper. The features used in the proposed methods are selected manually and subjectively, not derived by a statistical procedure. The experiment results show our methods perform better than approaches using machine learning techniques. 1553-9105/