Integration of manual and automatic text categorization. A categorization workbench for text-based email and spam

Abstract

As a method structuring information and knowledge contained in texts, text categorization can be to a great extend automated. The automatic text classification systems implement machine learning algorithms and need training samples. In commercial applications however, the automatic categorization appear to come up against limiting factors. For example, it turns out to be difficult to reduce the sample complexity without the categorization quality in terms of recall and precision will suffer. Instead of trying to fully replace the human work by machine, it could be more effective and ultimately efficient to let human and machine cooperate. So we have developed a categorization workbench to realise synergy between manual and machine categorization. To compare the categorization workbench with common automatic classification systems, the automatic categorizer of the IBM DB2 Information Integrator for Content has been chosen for tests. The test results show that, benefiting from the incorporation of user's domain knowledge, the categorization workbench can improve the recall by a factor of two till four with the same number of training samples as the automatic categorizer uses. Further, to get a comparable categorization quality, the categorization workbench just needs an eighth till a quarter of the training samples as the automatic categorizer does. © 2004 Springer-Verlag.