Research on Chinese Parsing Based on the Improved Compositional Vector Grammar

The basic task of syntactic parsing is to determine the syntactic structure of the sentence. Because the natural language is very complex, syntactic structure has a lot of ambiguities. Resolving ambiguity need to introduce a lot of information, and Compositional Vector Grammar (CVG) can well capture fine-grained syntactic and compositional-semantic information on phrases and words. In this paper, we first use a standard CVG model for Chinese parsing, and then we have made improvements on the CVG model. In order to introduce more information, for the word vector, we add the part of speech information; for the type of newborn node after binarization, add temporary node basic type; when computing node score, add the node type information. We also propose a solution for unknown word, replaced with structural vector. Our CVG parser improves the standard CVG parser by nearly 1% F1 on the development set of CTB8.0.