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Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

机译:提高英语教学能力方面的语义图形依赖性解析模型

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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.
机译:实现用于图形依赖性解析的高阶功能,这是一个非常困难的问题,而不会越来越大的解码困难。为了解决这个问题,本文为语义图形依赖解析模型(SGDPM)提供了一种语言依赖性模型和光束搜索,以表示计算机应用的高阶函数。第一种方法是使用基线解析器扫描大量不受伤的数据。它将构建自动解析的数据以创建语言依赖模型(LDM)。 LDM基于光束搜索解码期间的一组新功能,在那里它将将LDM功能结合到解析模型中,并利用双语文本的解析模型中的功能。我们的方法具有主要益处,包括较丰富的高阶特征,这些功能较大,含有大尺寸和额外的大型原油语料库,用于增加解码难度。此外,已经使用了使用计算机应用程序在单译本文本功能中解析单一解析文本和双解析文本的提出的方法来评估SGDPM,以便在Mono-解析文本功能中对英语和中文数据进行实验。实验结果表明,使用最着名的英语数据系统及其可比准确度获得最准确的中国数据。此外,BITEXT解析过程中汉语/一般双语信息的实验室规模实验优于最佳录制的现有解决方案。

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