A Correcting Model for Preposition Error in English Essays of Chinese Student Based on Hybrid Features Classification

Ya Zhou; Zun He Rong; Gui Min Huang

doi:10.4028/www.scientific.net/AMM.631-632.1435

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A Correcting Model for Preposition Error in English Essays of Chinese Student Based on Hybrid Features Classification

Abstract:

In this paper, we propose an automatic preposition correction method for Chinese learners of English. Our corpus comes from Chinese learners of English Corpus (CLEC). We use N-gram model to extract the semantic features based on the form of the word in context, part of speech tagging and syntactic parse tree. Meanwhile, this paper proposes a new method based on mutual information and improves the training and classification method. This method’s performance has been improved in terms of both effectiveness and efficiency, and the results show a significant improvement compared with the previous results.

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Periodical:

Applied Mechanics and Materials (Volumes 631-632)

Pages:

1435-1441

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.631-632.1435

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Online since:

September 2014

Authors:

Ya Zhou, Zun He Rong*, Gui Min Huang

Keywords:

Features Classification, Mutual Information (MI), N-Gram Model, Preposition Correcting

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* - Corresponding Author

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