@InProceedings{lin-EtAl:2017:WNUT,
  author    = {Lin, Bill Y.  and  Xu, Frank  and  Luo, Zhiyi  and  Zhu, Kenny},
  title     = {Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {160--165},
  abstract  = {In this paper, we present our multi-channel neural architecture for recognizing
	emerging named entity in social media messages, which we applied in the Novel
	and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on
	Noisy User-generated Text (W-NUT). We propose a novel approach, which
	incorporates comprehensive word representations with multi-channel information
	and Conditional Random Fields (CRF) into a traditional Bidirectional Long
	Short-Term Memory (BiLSTM) neural network without using any additional
	hand-craft features such as gazetteers. In comparison with other systems
	participating in the shared task, our system won the 2nd place.},
  url       = {http://www.aclweb.org/anthology/W17-4421}
}