A Text Classifier Based on Sentence Category VSM

摘要

VSM is a mature model of text representation for categorization. Words are commonly used as dimensions of feature space of VSM, but words only provide little semantic information. Sentence category theory is an important component of HNC theory and can provide abundant information about meaning, structure and style of a sentence. We use sentence categories as dimensions of feature space, reduce the dimensionality by dividing mixed sentence categories and reform the weights by tfc-weighting algorithm. By simple vector distance calculation, we can get the parameters of the classifier and execute the categorization. The average precision and recall of our classifier are acceptable and can be improved by other HNC techniques.