@InProceedings{zheng-EtAl:2017:Long,
  author    = {Zheng, Suncong  and  Wang, Feng  and  Bao, Hongyun  and  Hao, Yuexing  and  Zhou, Peng  and  Xu, Bo},
  title     = {Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme},
  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     = {1227--1236},
  abstract  = {Joint extraction of entities and relations is an important task in information
	extraction. To tackle this problem, we firstly propose a novel tagging scheme
	that can convert the joint extraction task to a tagging problem.. Then, based
	on our tagging scheme, we study different end-to-end models to extract entities
	and their relations directly, without identifying entities and relations
	separately. We conduct experiments on a public dataset produced by distant
	supervision method and the experimental results show that the tagging based
	methods are better than most of the existing pipelined and joint learning
	methods. What’s more, the end-to-end model proposed in this paper, achieves
	the best results on the public dataset.},
  url       = {http://aclweb.org/anthology/P17-1113}
}