@InProceedings{kuru-can-yuret:2016:COLING,
  author    = {Kuru, Onur  and  Can, Ozan Arkan  and  Yuret, Deniz},
  title     = {CharNER: Character-Level Named Entity Recognition},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {911--921},
  abstract  = {We describe and evaluate a character-level tagger for language-independent
	Named Entity Recognition (NER).
	Instead of words, a sentence is represented as a sequence of characters.
	The model consists of stacked bidirectional LSTMs which inputs characters and
	outputs tag probabilities for each character.  These probabilities are then
	converted to consistent word level named entity tags using a Viterbi decoder. 
	We are able to achieve close to state-of-the-art NER performance in seven
	languages with the same basic model using only labeled NER data and no
	hand-engineered features or other external resources like syntactic taggers or
	Gazetteers.},
  url       = {http://aclweb.org/anthology/C16-1087}
}