@InProceedings{liu-EtAl:2017:EMNLP20175,
  author    = {Liu, Tianyu  and  Wang, Kexiang  and  Chang, Baobao  and  Sui, Zhifang},
  title     = {A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction},
  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     = {1790--1795},
  abstract  = {Distant-supervised relation extraction inevitably suffers from wrong labeling
	problems because it heuristically labels relational facts with knowledge bases.
	Previous sentence level denoise models don’t achieve satisfying performances
	because they use hard labels which are determined by distant supervision and
	immutable during training. To this end, we introduce an entity-pair level
	denoise method which exploits semantic information from correctly labeled
	entity pairs to correct wrong labels dynamically during training. We propose a
	joint score
	function which combines the relational scores based on the entity-pair
	representation and the confidence of the hard label to obtain a new label,
	namely a soft label, for certain entity pair. During training, soft labels
	instead of hard labels serve as gold labels. Experiments on the benchmark
	dataset show that our method dramatically reduces noisy instances and
	outperforms other state-of-the-art systems.
	Author{4}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1189}
}