Maximum Entropy Model is a probability estimation tech- nique widely used for a variety of natural language tasks. It offers a clean and accommodable frame to combine diverse pieces of contextual infor- mation to estimate the probability of a certain linguistics phenomena. This approach for many tasks of NLP perform near state-of-the-art level, or outperform other competing probability methods when trained and tested under similar conditions. In this paper, we use maximum entropy model for text categorization. We compare and analyze its categoriza- tion performance using different approaches for text feature generation, different number of features and smoothing technique. Moreover, in ex- periments we compare it to Bayes, KNN and SVM, and show that its performance is higher than Bayes and comparable with KNN and SVM. We think it is a promising technique for text categorization.
['Ronglu Li', 'Xiaopeng Tao', 'Lei Tang', 'Yunfa Hu']
Using Maximum Entropy Model for Chinese Text Categorization