@InProceedings{misawa-EtAl:2017:SCLeM,
  author    = {Misawa, Shotaro  and  Taniguchi, Motoki  and  Miura, Yasuhide  and  Ohkuma, Tomoko},
  title     = {Character-based Bidirectional LSTM-CRF with words and characters for Japanese Named Entity Recognition},
  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     = {97--102},
  abstract  = {Recently, neural models have shown superior performance over conventional
	models in NER tasks. These models use CNN to extract sub-word information along
	with RNN to predict a tag for each word. However, these models have been tested
	almost entirely on English texts. It remains unclear whether they perform
	similarly in other languages. We worked on Japanese NER using neural models and
	discovered two obstacles of the state-of-the-art model.
	 First, CNN is unsuitable for extracting Japanese sub-word information.
	Secondly, a model predicting a tag for each word cannot extract an entity when
	a part of a word composes an entity. The contributions of this work are (1)
	verifying the effectiveness of the state-of-the-art NER model for Japanese, (2)
	proposing a neural model for predicting a tag for each character using word and
	character information. Experimentally obtained results demonstrate that our
	model outperforms the state-of-the-art neural English NER model in Japanese.},
  url       = {http://www.aclweb.org/anthology/W17-4114}
}