@InProceedings{yang-mitchell:2017:Long,
  author    = {Yang, Bishan  and  Mitchell, Tom},
  title     = {Leveraging Knowledge Bases in LSTMs for Improving Machine Reading},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1436--1446},
  abstract  = {This paper focuses on how to take advantage of external knowledge bases (KBs)
	to improve recurrent neural networks for machine reading. Traditional methods
	that exploit knowledge from KBs encode knowledge as discrete indicator
	features. Not only do these features generalize poorly, but they require
	task-specific feature engineering to achieve good performance. We propose
	KBLSTM, a novel neural model that leverages continuous representations of KBs
	to enhance the learning of recurrent neural networks for machine reading. To
	effectively integrate background knowledge with information from the currently
	processed text, our model employs an attention mechanism with a sentinel to
	adaptively decide whether to attend to background knowledge and which
	information from KBs is useful. Experimental results show that our model
	achieves accuracies that surpass the previous state-of-the-art results for both
	entity extraction and event extraction on the widely used ACE2005 dataset.},
  url       = {http://aclweb.org/anthology/P17-1132}
}