Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities
Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities
复制标题
语义图解依存分析模型在提高英语教学能力中的应用
DOI：
10.1145/3425633
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
R. D. J. Samuel
中科院分区：
文献类型：
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作者：
Erlu Wang;P. Kumar;R. D. J. Samuel
在不增加解码难度的情况下实现图形依赖解析的高阶功能是一个非常困难的问题。为了解决这个问题，本文提供了一种语义图形依赖解析模型（SGDPM）的方法，该模型使用语言依赖模型和束搜索来表示计算机应用程序的高阶函数。第一种方法是使用基线解析器扫描大量未被注意到的数据。它将构建自动解析的数据以创建语言依赖模型（LDM）。LDM基于波束搜索解码过程中的一组新特征，将LDM特征整合到解析模型中，并在双语文本解析模型中加以利用。我们的方法有主要的好处，其中包括丰富的高阶特征，这些特征被描述为大尺寸和额外的大型粗语料库，以增加解码的难度。在此基础上，利用本文提出的单解析文本和双解析文本的分析方法对SGDPM进行了评估，并在计算机应用中对单解析文本功能下的中英文数据进行了实验。实验结果表明，最准确的中文数据是用最知名的英语数据系统获得的，其精度与之相当。此外，在实验室规模的实验中，中文/通用双语信息在文本分析过程中的表现优于已有的最佳解决方案。
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.