@InProceedings{dugas-nichols:2016:WNUT,
  author    = {Dugas, Fabrice  and  Nichols, Eric},
  title     = {DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {178--187},
  abstract  = {In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy
	User-generated Text (WNUT) Shared Task \#2: Named Entity Recognition in Twitter.
	Our shared task submission adopts the bidirectional LSTM-CNN model of Chiu and
	Nichols (2016), as it has been shown to perform well on both newswire and Web
	texts. It uses word embeddings trained on large-scale Web text collections
	together with text normalization to cope with the diversity in Web texts, and
	lexicons for target named entity classes constructed from publicly-available
	sources. Extended evaluation comparing the effectiveness of various word
	embeddings, text normalization, and lexicon settings shows that our system
	achieves a maximum F1-score of 47.24, performance surpassing that of the shared
	task's second-ranked system.},
  url       = {http://aclweb.org/anthology/W16-3924}
}