Chinese POS Tagging Using Restricted Maximum Entropy Model*

This paper presents Chinese Part-of-speech (POS) tagging using maximum entropy technique, in which we introduce a novel gain-driven method for feature selection, then we describe the restricted training method for model learning. We test our approach on the simplified Chinese corpus of Peking University China and achieve an accuracy of 97.80% and 98.60% over fine and coarse grained tag set - a significant improvement over the existing Chinese POS tagger.