@InProceedings{shao-EtAl:2017:I17-1,
  author    = {Shao, Yan  and  Hardmeier, Christian  and  Tiedemann, J\"{o}rg  and  Nivre, Joakim},
  title     = {Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {173--183},
  abstract  = {We present a character-based model for joint segmentation and POS tagging for
	Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is
	adapted and applied with novel vector representations of Chinese characters
	that capture rich contextual information and lower-than-character level
	features. The proposed model is extensively evaluated and compared with a
	state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The
	experimental results indicate that our model is accurate and robust across
	datasets in different sizes, genres and annotation schemes. We obtain
	state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint
	segmentation and POS tagging.},
  url       = {http://www.aclweb.org/anthology/I17-1018}
}