Rescoring approaches for parsing aims to re-rank and change the order of parse trees produced by a general parser for a given sentence. The re-ranking performance depends on whether or not the rescoring function is able to precisely estimate the quality of parse trees by using more complex features from the whole parse tree. However it is a challenge to design an appropriate rescoring function since complex features usually face the severe problem of data sparseness. And it is also difficult to obtain sufficient information requisite in re-estimatation of tree structures because existing annotated Treebanks are generally small-sized. To address the issue, in this paper, we utilize a large amount of auto-parsed trees to learn the syntactic and sememtic information. And we propose a simple but effective score function in order to integrate the scores provided by the baseline parser and dependency association scores based on dependency-based word embeddings, learned from auto-parsed trees. The dependency association scores can relieve the problem of data sparseness, since they can be still calculated by word embeddings even without occurrence of a dependency word pair in a corpus. Moreover, semantic role labels are also considered to distinct semantic relation of word pairs. Experimental results show that our proposed model improves the base Chinese parser significantly.