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        <title>Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning</title>
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    <name type="personal">
        <namePart type="given">Jui-Feng</namePart>
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        <namePart type="given">Tsung-Wei</namePart>
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    <name type="personal">
        <namePart type="given">Chan-Kun</namePart>
        <namePart type="family">Yeh</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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        <name type="personal">
            <namePart type="given">Yuen-Hsien</namePart>
            <namePart type="family">Tseng</namePart>
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            <namePart type="given">Xiaofei</namePart>
            <namePart type="family">Lu</namePart>
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    <abstract>Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin’s grammars have flexible rule and special usage. Therefore, the common grammatical errors can classify to missing, redundant, selection and disorder. In this paper, we proposed the structure of the Recurrent Neural Networks using Long Short-term memory (RNN-LSTM). It can detect the error type from the foreign learner writing. The features based on the word vector and part-of-speech vector. In the test data found that our method in the detection level of recall better than the others, even as high as 0.9755. That is because we give the possibility of greater choice in detecting errors.</abstract>
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%0 Conference Proceedings
%T Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning
%A Yeh, Jui-Feng
%A Hsu, Tsung-Wei
%A Yeh, Chan-Kun
%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 yeh-etal-2016-grammatical
%X Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin’s grammars have flexible rule and special usage. Therefore, the common grammatical errors can classify to missing, redundant, selection and disorder. In this paper, we proposed the structure of the Recurrent Neural Networks using Long Short-term memory (RNN-LSTM). It can detect the error type from the foreign learner writing. The features based on the word vector and part-of-speech vector. In the test data found that our method in the detection level of recall better than the others, even as high as 0.9755. That is because we give the possibility of greater choice in detecting errors.
%U https://aclanthology.org/W16-4918
%P 140-147

Markdown (Informal)

[Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning](https://aclanthology.org/W16-4918) (Yeh et al., NLP-TEA 2016)

• Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning (Yeh et al., NLP-TEA 2016)

ACL

• Jui-Feng Yeh, Tsung-Wei Hsu, and Chan-Kun Yeh. 2016. Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 140–147, Osaka, Japan. The COLING 2016 Organizing Committee.