skip to main content
research-article

Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

Authors Info & Claims
Published:12 August 2021Publication History
Skip Abstract Section

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.

References

  1. J. M. Roschelle, R. D. Pea, C. M. Hoadley, D. N. Gordin, and B. M. Means. 2000. Changing how and what children learn in school with computer-based technologies. Future Child. 10, 2 (2000), 76–101.Google ScholarGoogle ScholarCross RefCross Ref
  2. S. C. Tsai. 2011. Courseware integration into task-based learning: A case study of multimedia courseware-supported oral presentations for non-English major students. ReCALL 23, 2 (2011), 117–134.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. B. T. Lau and C. H. Sim. 2008. Exploring the extent of ICT adoption among secondary school teachers in malaysia. Int. J. Comput. ICT Res. 2, 2 (2008), 19–36.Google ScholarGoogle Scholar
  4. Z. Ling and H. Jinhuan. 2001. A findings report on junior & senior college English teaching in institutions of higher learning. Foreign Lang. World 6 (2001).Google ScholarGoogle Scholar
  5. A. Wiriyachitra. 2002. English language teaching and learning in thailand in this decade. Thai TESOL Focus 15, 1 (2002), 4–9.Google ScholarGoogle Scholar
  6. L. Wozney, V. Venkatesh, and P. Abrami. 2006. Implementing computer technologies: Teachers’ perceptions and practices. J. Technol. Teach. Edu. 14, 1 (2006), 173–207.Google ScholarGoogle Scholar
  7. S. Suh, S. W. Kim, and N. J. Kim. 2010. Effectiveness of MMORPG-based instruction in elementary English education in Korea. J. Comput.-assist. Learn. 26, 5 (2010), 370–378.Google ScholarGoogle Scholar
  8. T. Y. Liu. 2009. A context-aware ubiquitous learning environment for language listening and speaking. J. Comput.-assist. Learn. 25, 6 (2009), 515–527.Google ScholarGoogle Scholar
  9. J. King. 2002. Using DVD feature films in the EFL classroom. Comput.-assist. Lang. Learn. 15, 5 (2002), 509–523.Google ScholarGoogle Scholar
  10. H. J. Becker. 2000. Findings from the teaching, learning, and computing survey. Education Policy Analysis Archives 8 (2000), 51.Google ScholarGoogle ScholarCross RefCross Ref
  11. S. C. Yang and Y. J. Chen. 2007. Technology-enhanced language learning: A case study. Comput. Hum. Behav. 23, 1 (2007), 860–879.Google ScholarGoogle ScholarCross RefCross Ref
  12. R. Kern. 2006. Perspectives on technology in learning and teaching languages. Tesol Quart. 40, 1 (2006), 183–210.Google ScholarGoogle ScholarCross RefCross Ref
  13. L. Johnson and J. Renner. 2012. Effect of the flipped classroom model on a secondary computer applications course: Student and teacher perceptions, questions and student achievement. Unpublished Doctoral Dissertation, University of Louisville, Louisville, KY.Google ScholarGoogle Scholar
  14. H. M. J. Hsu. 2011. The potential of kinect in education. Int. J. Info. Edu. Technol. 1, 5 (2011), 365.Google ScholarGoogle ScholarCross RefCross Ref
  15. A. Sadaf, T. J. Newby, and P. A. Ertmer. 2012. Exploring pre-service teachers' beliefs about using web 2.0 technologies in the K-12 classroom. Comput. Edu. 59, 3 (2012), 937–945.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. Bouhnik and T. Marcus. 2006. Interaction in distance-learning courses. J. Amer. Soc. Info. Sci. Technol. 57, 3 (2006), 299–305.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. R. Greenwood, C. Arreaga-Mayer, C. A. Utley, K. M. Gavin, and B. J. Terry. 2001. Classwide peer tutoring learning management system: Applications with elementary-level English language learners. Remed. Spec. Edu. 22, 1 (2001), 34–47.Google ScholarGoogle ScholarCross RefCross Ref
  18. P. L. Liu, C. J. Chen, and Y. J. Chang. 2010. Effects of a computer-assisted concept mapping learning strategy on EFL college students’ English reading comprehension. Comput. Edu. 54, 2 (2010), 436–445.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. E. Bañados. 2013. A blended-learning pedagogical model for teaching and learning EFL successfully through an online interactive multimedia environment. CALICO J. 23, 3 (2013), 533–550.Google ScholarGoogle ScholarCross RefCross Ref
  20. B. K. Bhardwaj and S. Pal. 2012. Data mining: A prediction for performance improvement using classification. Retrieved from https://arXiv:1201.3418.Google ScholarGoogle Scholar
  21. C. A. Young and J. Bush. 2004. Teaching the English language arts with technology: A critical approach and pedagogical framework. Contemp. Issues Technol. Teach. Edu. 4, 1 (2004), 1–22.Google ScholarGoogle Scholar
  22. J. Ranalli. 2008. Learning English with the sims: Exploiting authentic computer simulation games for L2 learning. Comput.-assisted Lang. Learn. 21, 5 (2008), 441–455.Google ScholarGoogle Scholar

Index Terms

  1. Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        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

        Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 August 2021
        • Accepted: 1 September 2020
        • Revised: 1 August 2020
        • Received: 1 March 2020
        Published in tallip Volume 20, Issue 3

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!