@InProceedings{vondaniken-cieliebak:2017:WNUT,
  author    = {von D\"{a}niken, Pius  and  Cieliebak, Mark},
  title     = {Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {166--171},
  abstract  = {We present our system for the WNUT 2017 Named Entity Recognition challenge on
	Twitter data. We describe two modifications of a basic neural network
	architecture for sequence tagging. First, we show how we exploit additional
	labeled data, where the Named Entity tags differ from the target task. Then, we
	propose a way to incorporate sentence level features. Our system uses both
	methods and ranked second for entity level annotations, achieving an F1-score
	of 40.78, and second for surface form annotations, achieving an F1-score of
	39.33.},
  url       = {http://www.aclweb.org/anthology/W17-4422}
}