@InProceedings{ye-EtAl:2017:Long1,
  author    = {Ye, Hai  and  Chao, Wenhan  and  Luo, Zhunchen  and  Li, Zhoujun},
  title     = {Jointly Extracting Relations with Class Ties via Effective Deep Ranking},
  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     = {1810--1820},
  abstract  = {Connections between relations in relation extraction, which we call class ties,
	are common. In distantly supervised scenario, one entity tuple may have
	multiple relation facts. Exploiting class ties between relations of one entity
	tuple will be promising for distantly supervised relation extraction. However,
	previous models are not effective or ignore to model this property. In this
	work, to effectively leverage class ties, we propose to make joint relation
	extraction with a unified model that integrates convolutional neural network
	(CNN) with a general pairwise ranking framework, in which three novel ranking
	loss functions are introduced. Additionally, an effective method is presented
	to relieve the severe class imbalance problem from NR (not relation) for model
	training. Experiments on a widely used dataset show that leveraging class ties
	will enhance extraction and demonstrate the effectiveness of our model to learn
	class ties. Our model outperforms the baselines significantly, achieving
	state-of-the-art performance.},
  url       = {http://aclweb.org/anthology/P17-1166}
}