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Automated Detection of Usage Errors in non-native English Writing

Fujishima, Satoru and Ishizaki, Shun (2011) Automated Detection of Usage Errors in non-native English Writing. In: IES 2011-Emerging Technology for Better Human Life.

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    In an investigation of the use of a novelty detection algorithm for identifying inappropriate word combinations in a raw English corpus, we employ an unsupervised detection algorithm based on the one- class support vector machines (OC-SVMs) and extract sentences containing word sequences whose frequency of appearance is significantly low in native English writing. Combined with n-gram language models and document categorization techniques, the OC-SVM classifier assigns given sentences into two different groups; the sentences containing errors and those without errors. Accuracies are 79.30 % with bigram model, 86.63 % with trigram model, and 34.34 % with four-gram model.

    Item Type: Conference or Workshop Item (Paper)
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
    Depositing User: Satria Hardinata
    Date Deposited: 14 Nov 2011 12:15
    Last Modified: 14 Nov 2011 12:15

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