@InProceedings{zhang-EtAl:2017:EMNLP20171,
  author    = {Zhang, Yuhao  and  Zhong, Victor  and  Chen, Danqi  and  Angeli, Gabor  and  Manning, Christopher D.},
  title     = {Position-aware Attention and Supervised Data Improve Slot Filling},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {35--45},
  abstract  = {Organized relational knowledge in the form of "knowledge graphs" is important
	for many applications. However, the ability to populate knowledge bases with
	facts automatically extracted from documents has improved frustratingly slowly.
	This paper simultaneously addresses two issues that have held back prior work.
	We first propose an effective new model, which combines an LSTM sequence model
	with a form of entity position-aware attention that is better suited to
	relation extraction. Then we build TACRED, a large (119,474 examples)
	supervised relation extraction dataset obtained via crowdsourcing and targeted
	towards TAC KBP relations. The combination of better supervised data and a more
	appropriate high-capacity model enables much better relation extraction
	performance. When the model trained on this new dataset replaces the previous
	relation extraction component of the best TAC KBP 2015 slot filling system, its
	F1 score increases markedly from 22.2% to 26.7%.},
  url       = {https://www.aclweb.org/anthology/D17-1004}
}