Research and realization of naive Bayes English text classification method based on base noun phrase identification

To more advance classification accuracy of English texts, naive Bayes method based on base noun phrase (BaseNP) identification is presented. The rising maximum entropy model is applied to the identification. Firstly, use training corpus and user-defined feature templates to generate candidate features. Secondly, the feature selection algorithm computing feature gains is applied to select features. Finally, at the parameter estimation stage, the improved iterative scaling (IIS) algorithm is adopted. The experimental results show that this technique achieved precision and recall rates of roughly 93% for BaseNP identification and the classification accuracy is remarkably improved on this basis. It indicates that shallow parsing of high accuracy is very helpful to text classification.