Chinese Dependency Parsing Model Based on Lexical Governing Degree
基于词汇支配度的汉语依存分析模型

Use of structural information and lexicalization are two of the main challenges facing syntactic analysis, and they are investigated in this paper. First, the probabilities of lexical dependencies are obtained by training a large-scale dependency treebank and used to build the lexical model. Second, the governing degree of words is introduced to utilize the structure information. The lexical method overcomes the weakness of POS dependencies in the past work; meanwhile the governing degree of words is helpful to distinguish the syntactic structures so some ill-formed structures are avoided. Finally, the paper shows a good experimental result of around 74% accuracy on the test set that consists of 4000 sentences.