@InProceedings{tang-EtAl:2017:EMNLP2017,
  author    = {Tang, Siliang  and  Zhang, Ning  and  Zhang, Jinjiang  and  Wu, Fei  and  Zhuang, Yueting},
  title     = {NITE: A Neural Inductive Teaching Framework for Domain Specific NER},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2652--2657},
  abstract  = {In domain-specific NER, due to insufficient labeled training data, deep models
	usually fail to behave normally. In this paper, we proposed a novel Neural
	Inductive TEaching framework (NITE) to transfer knowledge from existing
	domain-specific NER models into an arbitrary deep neural network in a
	teacher-student training manner. NITE is a general framework that builds upon
	transfer learning and multiple instance learning, which collaboratively not
	only transfers knowledge to a deep student network but also reduces the noise
	from teachers. NITE can help deep learning methods to effectively utilize
	existing resources (i.e., models, labeled and unlabeled data) in a small
	domain. The experiment resulted on Disease NER proved that without using any
	labeled data, NITE can significantly boost the performance of a
	CNN-bidirectional LSTM-CRF NER neural network nearly over 30% in terms of
	F1-score.},
  url       = {https://www.aclweb.org/anthology/D17-1280}
}