• DocumentCode
    3656416
  • Title

    Academic Writing Support System Using Bayesian Networks

  • Author

    Masaki Uto;Maomi Ueno

  • Author_Institution
    Nagaoka Univ. of Technol., Niigata, Japan
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    385
  • Lastpage
    387
  • Abstract
    For academic writing, elaborating an argument particularly addressing an argument strength is important to establish causal relations between sentences. However, when an argument becomes large or complex, elaborating an argument considering the argument strength is difficult. To solve this problem, this article presents a proposal for an argument elaboration support system using a Bayesian network representation of the Toulmin model. Using that Bayesian network representation, the proposed system can estimate argument strength, sentence validity, and sentence influence. Moreover, it can generate optimal advice for revising the argument.
  • Keywords
    "Indexes","Bayes methods","Writing","Probabilistic logic","Silicon","Sensitivity","Numerical models"
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICALT.2015.16
  • Filename
    7265356