Rule-based text categorization using hierarchical categories

Document categorization, which is defined as the classification of text documents into one of several fixed classes or categories, has become important with the explosive growth of the World Wide Web. The goal of the work described here is to automatically categorize Web documents in order to enable effective retrieval of Web information. In this paper, based on the rule learning algorithm RIPPER (for Repeated Incremental Pruning to Produce Error Reduction), we propose an efficient method for hierarchical document categorization.