The syntactic ambiguity of a transitive verb (Vt) followed by a noun (N) has long been a problem in Chinese parsing. In this paper, we propose a classifier to resolve the ambiguity of Vt-N structures. The design of the classifier is based on three important guidelines, namely, adopting linguistically motivated features, using all available resources, and easy in-tegration into a parsing model. The lin-guistically motivated features include semantic relations, context, and morpho-logical structures; and the available re-sources are treebank, thesaurus, affix da-tabase, and large corpora. We also pro-pose two learning approaches that resolve the problem of data sparseness by auto-parsing and extracting relative knowledge from large-scale unlabeled data. Our experiment results show that the Vt-N classifier outperforms the cur-rent PCFG parser. Furthermore, it can be easily and effectively integrated into the PCFG parser and general statistical pars-ing models. Evaluation of the learning approaches indicates that world knowledge facilitates Vt-N disambigua-tion through data selection and error cor-rection. 1