@inproceedings{huang-etal-2016-automatically,
    title = "Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners",
    author = "Huang, Chieh-Yang  and
      Peinelt, Nicole  and
      Ku, Lun-Wei",
    editor = "Watanabe, Hideo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/C16-2063/",
    pages = "302--306",
    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."
}