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        <title>Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model</title>
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        <namePart type="given">Yajun</namePart>
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            <namePart type="given">Yuen-Hsien</namePart>
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    <abstract>In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.</abstract>
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%0 Conference Proceedings
%T Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model
%A Liu, Yajun
%A Zan, Hongying
%A Zhong, Mengjie
%A Ma, Hongchao
%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 liu-etal-2018-detecting
%X In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.
%R 10.18653/v1/W18-3727
%U https://aclanthology.org/W18-3727
%U https://doi.org/10.18653/v1/W18-3727
%P 188-193

Markdown (Informal)

[Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model](https://aclanthology.org/W18-3727) (Liu et al., NLP-TEA 2018)

• Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model (Liu et al., NLP-TEA 2018)

ACL

• Yajun Liu, Hongying Zan, Mengjie Zhong, and Hongchao Ma. 2018. Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 188–193, Melbourne, Australia. Association for Computational Linguistics.