Fast Text Categorization Based on a Novel Class Space Model

摘要

Automatic categorization has been shown to be an accurate alternative to manual categorization in which documents are processed and automatically assigned to pre-defined categories. The accuracy of different methods for categorization has been studied largely, but their efficiency has seldom been mentioned. Aiming to maintain effectiveness while improving efficiency, we proposed a fast algorithm for text categorization and a compressed document vector representation method based on a novel class space model. The experiments proved our methods have better efficiency and tolerable effectiveness.