Automatic extraction and prediction of word order errors from language learning SNS

Brendan Flanagan, Sachio Hirokawa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Recent research into writing tools to support foreign language learners of English has focused on prevalent errors in learner writing, while other errors, such as word order errors, have received little attention. As the word order of some languages are similar, there are also large differences between languages which can affect foreign language learning. In this paper, we automatically extract corrected sentences that contain word order errors samples from a language learning SNS to create a word order error corpus for machine learning. This corpus is then analyzed to train and evaluate the effectiveness of SVM classifiers to automatically classify word order errors in learner writing.

    Original languageEnglish
    Title of host publicationProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
    EditorsAyako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages292-295
    Number of pages4
    ISBN (Electronic)9781467389853
    DOIs
    Publication statusPublished - Aug 31 2016
    Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
    Duration: Jul 10 2016Jul 14 2016

    Publication series

    NameProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

    Other

    Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
    CountryJapan
    CityKumamoto
    Period7/10/167/14/16

    All Science Journal Classification (ASJC) codes

    • Information Systems
    • Computer Networks and Communications
    • Computer Science Applications
    • Computer Vision and Pattern Recognition

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