@InProceedings{shen-EtAl:2017:RepL4NLP2,
  author    = {Shen, Yanyao  and  Yun, Hyokun  and  Lipton, Zachary  and  Kronrod, Yakov  and  Anandkumar, Animashree},
  title     = {Deep Active Learning for Named Entity Recognition},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {252--256},
  abstract  = {Deep neural networks 
	have advanced the state of the art 
	in named entity recognition. 
	However, under typical training procedures,
	advantages over classical methods
	emerge only with large datasets. 
	As a result,  deep learning is employed
	only when large public datasets or a large budget 
	for manually labeling data is available. 
	In this work, we show otherwise: 
	by combining deep learning with active learning,
	we can outperform classical methods 
	even with a significantly smaller amount of training data.},
  url       = {http://www.aclweb.org/anthology/W17-2630}
}