Usage of distinctive classifiers for text categorization using distributional features

Predefined categories can be assigned to the natural language text using Text categorization. This paper explores the effect of other types of values, which express the distribution of a word in the document. These values are called distributional features. These different features are calculated for Window passage using distinctive classifiers. The classifier which gives the more accurate result is selected for categorization. Experiments show that the distributional features are useful for text categorization. These results are simulated using Weka tool.