Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

Abstract

It is a very difficult problem to achieve high-order functionality for graphical dependency parsing without growing decoding difficulties. To solve this problem, this article offers a way for Semantic Graphical Dependence Parsing Model (SGDPM) with a language-dependency model and a beam search to represent high-order functions for computer applications. The first approach is to scan a large amount of unnoticed data using a baseline parser. It will build auto-parsed data to create the Language-dependence Model (LDM). The LDM is based on a set of new features during beam search decoding, where it will incorporate the LDM features into the parsing model and utilize the features in parsing models of bilingual text. Our approach has main benefits, which include rich high-order features that are described given the large size and the additional large crude corpus for increasing the difficulty of decoding.  Further, SGDPM has been evaluated using the suggested method for parsing tasks of mono-parsing text and bi-parsing text to carry out experiments on the English and Chinese data in the mono-parsing text function using computer applications. Experimental results show that the most accurate Chinese data is obtained with the best known English data systems and their comparable accuracy. Furthermore, the lab-scale experiments on the Chinese/General bilingual information in the bitext parsing process outperform the best recorded existing solutions.