Text Classification Based on a Combination of Ontology with Statistical Method

Text classification is becoming one of the key techniques in organizing and handling a large amount of text data. In this paper, a combination of ontology with statistical method is presented to improve the precision of text classification. In this study, first, different kind of linguistic ontology knowledge will be respectively acquired by learning training corpus to determine text classifiers. For an actual document, the semantic evaluation value of the document will respectively be gotten by different kind of linguistic ontology knowledge and the categories will be judged by the highest evaluation value. Compared with Bayes, k-nearest neighbor and support vector machine, the proposed approach outperforms previous works