• DocumentCode
    257474
  • Title

    An Automatic English Composition scoring model based on neural network algorithm

  • Author

    Ya Zhou ; Taosong Fan ; Guimin Huang

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Guilin Univ. of Electron. Technol., Guilin, China
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    149
  • Lastpage
    152
  • Abstract
    In this paper, an Automatic English Composition scoring (AECS) model based on neural network algorithm is constructed by extracting the lexical feature, syntactic feature and readability features which reflect the content writing quality and determining these features´ weight in composition scoring. The model uses training data to train the neural network and eventually it obtains the neural networks indicating the relationship of these features which can be used to predict the English compositions´ final scores. Through an objective comparison of the scores predicted by AECS and experienced teachers, we know that the AECS model we proposed can well reflect the level of students´ writing.
  • Keywords
    natural language processing; neural nets; AECS model; automatic English composition scoring model; lexical feature; neural network algorithm; readability features; students writing; syntactic feature; Computational modeling; Feature extraction; Mathematical model; Neural networks; Syntactics; Training; Writing; automatic scoring; natural language processing; neural network; text feature extraction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Science (ICIS), 2014 IEEE/ACIS 13th International Conference on
  • Conference_Location
    Taiyuan
  • Type

    conf

  • DOI
    10.1109/ICIS.2014.6912123
  • Filename
    6912123