@inproceedings{cheng-etal-2018-languagenet,
    title = "{L}anguage{N}et: Learning to Find Sense Relevant Example Sentences",
    author = "Cheng, Shang-Chien  and
      Chen, Jhih-Jie  and
      Yang, Chingyu  and
      Chang, Jason",
    editor = "Zhao, Dongyan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C18-2022/",
    pages = "99--102",
    abstract = "In this paper, we present a system, LanguageNet, which can help second language learners to search for different meanings and usages of a word. We disambiguate word senses based on the pairs of an English word and its corresponding Chinese translations in a parallel corpus, UM-Corpus. The process involved performing word alignment, learning vector space representations of words and training a classifier to distinguish words into groups of senses. LanguageNet directly shows the definition of a sense, bilingual synonyms and sense relevant examples."
}