@inproceedings{choi-etal-2017-syllable,
    title = "A Syllable-based Technique for Word Embeddings of {K}orean Words",
    author = "Choi, Sanghyuk  and
      Kim, Taeuk  and
      Seol, Jinseok  and
      Lee, Sang-goo",
    editor = "Faruqui, Manaal  and
      Schuetze, Hinrich  and
      Trancoso, Isabel  and
      Yaghoobzadeh, Yadollah",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-4105/",
    doi = "10.18653/v1/W17-4105",
    pages = "36--40",
    abstract = "Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are unaware of the morphology of words, so it is not directly applicable to highly agglutinative languages such as Korean. We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologically meaningful representation of Korean words compared to the original Skip-gram embeddings. The results also show that it is quite robust to the Out-of-Vocabulary problem."
}