Discriminative parse reranking has been shown to be an effective technique to im-prove the generative parsing models. In this paper, we present a series of exper-iments on parsing the Tsinghua Chinese Treebank with hierarchically split-merge grammars and reranked with a perceptron-based discriminative model. In addition to the homogeneous annotation on TCT, we also incorporate the PCTB-based parsing result as heterogeneous annotation into the reranking feature model. The rerank-ing model achieved 1.12 % absolute im-provement on F1 over the Berkeley parser on a development set. The head labels in Task 2.1 are annotated with a sequence labeling model. The system achieved