Discriminate chinese word segmenter with global and context features

Chinese Word segmenter is the basis for all subsequent applications of natural language processing. The Corpus-based statistic method has become the predominant method. However, the training corpora are not enough especially in certain areas. Therefore, we introduce some global features and context features in order to get almost the same performance only with much smaller scale corpus. The experiments results show that our approach significantly outperforms the original feature sets in the same training data. Meanwhile, the time-consuming of model training is also reduced. In addition, these features do not depend on classifiers, so our method can easily be changed to other models.