@InProceedings{hsieh-EtAl:2017:I17-21,
  author    = {Hsieh, Yu-Lun  and  Chang, Yung-Chun  and  Huang, Yi-Jie  and  Yeh, Shu-Hao  and  Chen, Chun-Hung  and  Hsu, Wen-Lian},
  title     = {MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {80--85},
  abstract  = {Part-of-speech (POS) tagging and named entity recognition (NER) are crucial
	steps in natural language processing. In addition, the difficulty of word
	segmentation places additional burden on those who intend to deal with
	languages such as Chinese, and pipelined systems often suffer from error
	propagation. This work proposes an end-to-end model using character-based
	recurrent neural network (RNN) to jointly accomplish segmentation, POS tagging
	and NER of a Chinese sentence. Experiments on previous word segmentation
	and NER datasets show that a single model with the proposed architecture is
	comparable to those trained specifically for each task, and outperforms
	freely-available softwares.  Moreover, we provide a web-based interface for the
	public to easily access this resource.},
  url       = {http://www.aclweb.org/anthology/I17-2014}
}