Chinese chunking based on frequently used words

Chinese chunking is defined as a task to automatically segment Chinese sentences into small chunks which hold semantic meanings. To improve the performance of Chinese chunking, we propose an approach to use frequently used words (FUW) for Chinese chunking. We use conditional random fields for chunking, and modified the training corpus according to the frequency of the words in it. Finally we devise an experiment to evaluate how the number of FUW affects the performance of conditional random fields. The experiment shows that the approach can be helpful for Chinese chunking, but the count of FUW should be carefully selected for high performance and low training cost.