@InProceedings{meerkamp-zhou:2017:StructPred,
  author    = {Meerkamp, Philipp  and  Zhou, Zhengyi},
  title     = {Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision},
  booktitle = {Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {44--51},
  abstract  = {We present an architecture to boost the precision of existing information
	extraction systems. This is achieved by augmenting the existing parser, which
	may be constraint-based or hybrid statistical, with a character-level neural
	network. Our architecture combines the ability of constraint-based or hybrid
	extraction systems to easily incorporate domain knowledge with the ability of
	deep neural networks to leverage large amounts of data to learn complex
	features. The network is trained using a measure of consistency between
	extracted data and existing databases as a form of cheap, noisy supervision.
	Our architecture does not require large scale manual annotation or a system
	rewrite. It has led to large precision improvements over an existing,
	highly-tuned production information extraction system used at Bloomberg LP for
	financial language text.},
  url       = {http://www.aclweb.org/anthology/W17-4307}
}