@InProceedings{choi-EtAl:2017:SCLeM,
  author    = {Choi, Sanghyuk  and  Kim, Taeuk  and  Seol, Jinseok  and  Lee, Sang-goo},
  title     = {A Syllable-based Technique for Word Embeddings of Korean Words},
  booktitle = {Proceedings of the First Workshop on Subword and Character Level Models in NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-4105}
}