Word Classification: An Experimental Approach with Naïve Bayes

Word classification is of significant interest in the domain of natural language processing and it has direct applications in information retrieval and knowledge discovery. This paper presents an experimental method using Naïve Bayes for word classification. The method is based on combing successful feature selection techniques on Mutual Information and Chi-Square with Naïve Bayes for word classification. We utilize the advances in feature-selection techniques in information retrieval and propose an efficient method to select key features for term identification and classification. We evaluate the method using real-world texts taken from the Wall Street Journal news articles. The experimental results proved that the method is fairly effective and competitive for word classification.