We address the issue of consuming heterogeneous annotation data for Chinese word segmentation and part-of-speech tagging. We empirically analyze the diversity between two representative corpora, i.e. Penn Chinese Treebank (CTB) and PKU’s People’s Daily (PPD), on manually mapped data, and show that their linguistic annotations are systematically different and highly compatible. The analysis is further exploited to improve processing accuracy by (1) integrating systems that are respectively trained on heterogeneous annotations to reduce the approximation error, and (2) re-training models with high quality automatically converted data to reduce the estimation error. Evaluation on the CTB and PPD data shows that our novel model achieves a relative error reduction of 11 % over the best reported result in the literature. 1