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        <title>Automatic Grammatical Error Detection for Chinese based on Conditional Random Field</title>
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        <namePart type="given">Yajun</namePart>
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        <namePart type="given">Yingjie</namePart>
        <namePart type="family">Han</namePart>
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    <name type="personal">
        <namePart type="given">Hongying</namePart>
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            <title>Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)</title>
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        <name type="personal">
            <namePart type="given">Hsin-Hsi</namePart>
            <namePart type="family">Chen</namePart>
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            <namePart type="given">Yuen-Hsien</namePart>
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            <namePart type="given">Xiaofei</namePart>
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    <abstract>In the process of learning and using Chinese, foreigners may have grammatical errors due to negative migration of their native languages. Currently, the computer-oriented automatic detection method of grammatical errors is not mature enough. Based on the evaluating task — CGED2016, we select and analyze the classification model and design feature extraction method to obtain grammatical errors including Mission(M), Disorder(W), Selection (S) and Redundant (R) automatically. The experiment results based on the dynamic corpus of HSK show that the Chinese grammatical error automatic detection method, which uses CRF as classification model and n-gram as feature extraction method. It is simple and efficient which play a positive effect on the research of Chinese grammatical error automatic detection and also a supporting and guiding role in the teaching of Chinese as a foreign language.</abstract>
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%0 Conference Proceedings
%T Automatic Grammatical Error Detection for Chinese based on Conditional Random Field
%A Liu, Yajun
%A Han, Yingjie
%A Zhuo, Liyan
%A Zan, Hongying
%Y Chen, Hsin-Hsi
%Y Tseng, Yuen-Hsien
%Y Ng, Vincent
%Y Lu, Xiaofei
%S Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F liu-etal-2016-automatic
%X In the process of learning and using Chinese, foreigners may have grammatical errors due to negative migration of their native languages. Currently, the computer-oriented automatic detection method of grammatical errors is not mature enough. Based on the evaluating task — CGED2016, we select and analyze the classification model and design feature extraction method to obtain grammatical errors including Mission(M), Disorder(W), Selection (S) and Redundant (R) automatically. The experiment results based on the dynamic corpus of HSK show that the Chinese grammatical error automatic detection method, which uses CRF as classification model and n-gram as feature extraction method. It is simple and efficient which play a positive effect on the research of Chinese grammatical error automatic detection and also a supporting and guiding role in the teaching of Chinese as a foreign language.
%U https://aclanthology.org/W16-4908
%P 57-62

Markdown (Informal)

[Automatic Grammatical Error Detection for Chinese based on Conditional Random Field](https://aclanthology.org/W16-4908) (Liu et al., NLP-TEA 2016)

• Automatic Grammatical Error Detection for Chinese based on Conditional Random Field (Liu et al., NLP-TEA 2016)

ACL

• Yajun Liu, Yingjie Han, Liyan Zhuo, and Hongying Zan. 2016. Automatic Grammatical Error Detection for Chinese based on Conditional Random Field. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 57–62, Osaka, Japan. The COLING 2016 Organizing Committee.