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Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

Published: 12 August 2021 Publication History

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.

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      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 3
      May 2021
      240 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3457152
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 August 2021
      Accepted: 01 September 2020
      Revised: 01 August 2020
      Received: 01 March 2020
      Published in TALLIP Volume 20, Issue 3

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      Author Tags

      1. Dependence Parsing
      2. Semantic Graphical
      3. Mono-parsing Text
      4. and Bi-Parsing Text

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      • Research-article
      • Refereed

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      • Jilin Provincial Department of Education-Cultivation of English Majors’ Innovative and Entrepreneurial Ability in the Information Age

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      • (2023)Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese Named Entity RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358368522:6(1-21)Online publication date: 17-Jun-2023
      • (2023)Virtual Reality-based English Teaching and Translation with 5G Wireless Technology for Hyper-realistic Experiences2023 International Conference on Emerging Research in Computational Science (ICERCS)10.1109/ICERCS57948.2023.10433964(1-6)Online publication date: 7-Dec-2023
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      • (2022)Multi-granularity interaction model based on pinyins and radicals for Chinese semantic matchingWorld Wide Web10.1007/s11280-022-01037-y25:4(1703-1723)Online publication date: 1-Jul-2022
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