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        <title>Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model</title>
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    <name type="personal">
        <namePart type="given">Yujie</namePart>
        <namePart type="family">Zhou</namePart>
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    <name type="personal">
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            <title>Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications</title>
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            <namePart type="given">Yuen-Hsien</namePart>
            <namePart type="family">Tseng</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages. However, few grammar checking applications have been developed to support the learners. The goal of this paper is to develop a tool to automatically diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W) in Chinese sentences written by those foreign learners. In this paper, a conventional linear CRF model with specific feature engineering and a LSTM-CRF model are used to solve the CGED (Chinese Grammatical Error Diagnosis) task. We make some improvement on both models and the submitted results have better performance on false positive rate and accuracy than the average of all runs from CGED2018 for all three evaluation levels.</abstract>
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    <identifier type="doi">10.18653/v1/W18-3724</identifier>
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%0 Conference Proceedings
%T Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model
%A Zhou, Yujie
%A Shao, Yinan
%A Zhou, Yong
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhou-etal-2018-chinese
%X When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages. However, few grammar checking applications have been developed to support the learners. The goal of this paper is to develop a tool to automatically diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W) in Chinese sentences written by those foreign learners. In this paper, a conventional linear CRF model with specific feature engineering and a LSTM-CRF model are used to solve the CGED (Chinese Grammatical Error Diagnosis) task. We make some improvement on both models and the submitted results have better performance on false positive rate and accuracy than the average of all runs from CGED2018 for all three evaluation levels.
%R 10.18653/v1/W18-3724
%U https://aclanthology.org/W18-3724
%U https://doi.org/10.18653/v1/W18-3724
%P 165-171

Markdown (Informal)

[Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model](https://aclanthology.org/W18-3724) (Zhou et al., NLP-TEA 2018)

• Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model (Zhou et al., NLP-TEA 2018)

ACL

• Yujie Zhou, Yinan Shao, and Yong Zhou. 2018. Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 165–171, Melbourne, Australia. Association for Computational Linguistics.