Using rough sets to construct sense type decision trees for text categorization

Accurate text categorization is needed for efficient and effective text retrieval, search and filtering. Finding appropriate categories and manually assigning them to existing documents is very laborious. The paper shows a simple procedure for automatic extraction of atomic sense types (semantic categories) from documents based on rough sets. The atomic sense types are nodes of a sense type decision tree, which represents a taxonomy