The dependency-based word embedding can relieve the problem of data sparseness, since even without occurrence of dependency word pairs in a corpus, dependency scores can be still calculated by word embeddings <cite>[12]</cite> . In this paper, we proposed a rescoring approach for parsing, based on a combination of original parsing scores and dependency word embedding scores to assist the determination of the best parse tree among the n-best parse trees.