Chinese Text Chunking Using Divide-Conquer Model

Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It has been shown that the limitations of using only one model are that: the use of the same types of features is not suitable for all phrases, and data sparseness may also result. In this paper, the divide-conquer model is proposed and applied in the identification of Chinese phrases. This model divides the task of chunking into several sub-tasks according to sensitive features of each phrase and identifies different phrases in parallel. Then, a two-stage decreasing conflict strategy is used to synthesize each sub-task´s answer. Through testing on Chinese Penn Treebank, F score of Chinese chunking using multi-agent strategy achieves to 95.82%, which is higher than the best result that has been reported.