@InProceedings{zhang-zhang-fu:2017:EMNLP2017,
  author    = {Zhang, Meishan  and  Zhang, Yue  and  Fu, Guohong},
  title     = {End-to-End Neural Relation Extraction with Global Optimization},
  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     = {1730--1740},
  abstract  = {Neural networks have shown promising results for relation extraction.
	State-of-the-art models cast the task as an end-to-end problem, 
	solved incrementally using a local classifier.
	Yet previous work using statistical models have demonstrated that global
	optimization can achieve better performances compared to local classification.
	We build a globally optimized neural model for end-to-end relation extraction,
	proposing novel LSTM features in order to better learn context representations.
	In addition, we present a novel method to integrate syntactic information to
	facilitate global learning, yet requiring little background on syntactic
	grammars thus being easy to extend. Experimental results show that our proposed
	model is highly effective,
	achieving the best performances on two standard benchmarks.},
  url       = {https://www.aclweb.org/anthology/D17-1182}
}