Ambiguity resolution for Vt-N structures in Chinese

Abstract

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 integration into a parsing model. The linguistically motivated features include semantic relations, context, and morphological structures; and the available resources are treebank, thesaurus, affix database, and large corpora. We also propose two learning approaches that resolve the problem of data sparseness by autoparsing and extracting relative knowledge from large-scale unlabeled data. Our experiment results show that the Vt-N classifier outperforms the current PCFG parser. Furthermore, it can be easily and effectively integrated into the PCFG parser and general statistical parsing models. Evaluation of the learning approaches indicates that world knowledge facilitates Vt-N disambiguation through data selection and error correction.