This paper proposes a method for shallow parsing on the basis of CRF and transformation-based error-driven learning.The method is applied to Penn Chinese Treebank and gets a good performance of chunking identification.First,CRF model is used to identify chunks to acquire candidate transformation rules by error-driven learning.Then,an evaluation function is used to filter candidate transformation rules.And last,transformation rules are used to revise the chunking results of CRF.The experimental results show that this approach is effective,and outperforms the single CRF-based approach in shallow parsing.Precision,recall and F-values are improved respectively.