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        <title>Chinese Preposition Selection for Grammatical Error Diagnosis</title>
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    <name type="personal">
        <namePart type="given">Hen-Hsen</namePart>
        <namePart type="family">Huang</namePart>
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        <namePart type="given">Yen-Chi</namePart>
        <namePart type="family">Shao</namePart>
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        <namePart type="given">Hsin-Hsi</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Misuse of Chinese prepositions is one of common word usage errors in grammatical error diagnosis. In this paper, we adopt the Chinese Gigaword corpus and HSK corpus as L1 and L2 corpora, respectively. We explore gated recurrent neural network model (GRU), and an ensemble of GRU model and maximum entropy language model (GRU-ME) to select the best preposition from 43 candidates for each test sentence. The experimental results show the advantage of the GRU models over simple RNN and n-gram models. We further analyze the effectiveness of linguistic information such as word boundary and part-of-speech tag in this task.</abstract>
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        <url>https://aclanthology.org/C16-1085</url>
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%0 Conference Proceedings
%T Chinese Preposition Selection for Grammatical Error Diagnosis
%A Huang, Hen-Hsen
%A Shao, Yen-Chi
%A Chen, Hsin-Hsi
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F huang-etal-2016-chinese
%X Misuse of Chinese prepositions is one of common word usage errors in grammatical error diagnosis. In this paper, we adopt the Chinese Gigaword corpus and HSK corpus as L1 and L2 corpora, respectively. We explore gated recurrent neural network model (GRU), and an ensemble of GRU model and maximum entropy language model (GRU-ME) to select the best preposition from 43 candidates for each test sentence. The experimental results show the advantage of the GRU models over simple RNN and n-gram models. We further analyze the effectiveness of linguistic information such as word boundary and part-of-speech tag in this task.
%U https://aclanthology.org/C16-1085
%P 888-899

Markdown (Informal)

[Chinese Preposition Selection for Grammatical Error Diagnosis](https://aclanthology.org/C16-1085) (Huang et al., COLING 2016)

• Chinese Preposition Selection for Grammatical Error Diagnosis (Huang et al., COLING 2016)

ACL

• Hen-Hsen Huang, Yen-Chi Shao, and Hsin-Hsi Chen. 2016. Chinese Preposition Selection for Grammatical Error Diagnosis. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 888–899, Osaka, Japan. The COLING 2016 Organizing Committee.