Text categorization for streams

Abstract

We describe a novel system for evaluating and performing streambased text categorization. Stream-based text categorization considers the text being categorized as a stream of symbols, which differs from the traditional feature-based approach which relies on extracting features from the text. The system implements characterbased languages models-specifically models based on the PPM text compression scheme-as well as count-based measures such as RMeasure and C-Measure. Use of the system demonstrates that all of these techniques outperform SVM, a feature-based classifier, at stream-related classification tasks such as authorship ascription.