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        <title>Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners</title>
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    <name type="personal">
        <namePart type="given">Chieh-Yang</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations</title>
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        <name type="personal">
            <namePart type="given">Hideo</namePart>
            <namePart type="family">Watanabe</namePart>
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    <abstract>In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners. The difficulty of the example sentences is automatically detected. Furthermore, the usage models of the near-synonyms are built by the GMM and Bi-LSTM models to suggest the best elaborative sentences. Experiments show the good performance both in the fill-in-the-blank test and on the manually labeled gold data, that is, the built models can select the appropriate words for the given context and vice versa.</abstract>
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%0 Conference Proceedings
%T Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners
%A Huang, Chieh-Yang
%A Peinelt, Nicole
%A Ku, Lun-Wei
%Y Watanabe, Hideo
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F huang-etal-2016-automatically
%X In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners. The difficulty of the example sentences is automatically detected. Furthermore, the usage models of the near-synonyms are built by the GMM and Bi-LSTM models to suggest the best elaborative sentences. Experiments show the good performance both in the fill-in-the-blank test and on the manually labeled gold data, that is, the built models can select the appropriate words for the given context and vice versa.
%U https://aclanthology.org/C16-2063
%P 302-306

Markdown (Informal)

[Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners](https://aclanthology.org/C16-2063) (Huang et al., COLING 2016)

• Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners (Huang et al., COLING 2016)

ACL

• Chieh-Yang Huang, Nicole Peinelt, and Lun-Wei Ku. 2016. Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 302–306, Osaka, Japan. The COLING 2016 Organizing Committee.