@InProceedings{lei-EtAl:2018:C18-1,
  author    = {Lei, Kai  and  Chen, Daoyuan  and  Li, Yaliang  and  Du, Nan  and  Yang, Min  and  Fan, Wei  and  Shen, Ying},
  title     = {Cooperative Denoising for Distantly Supervised Relation Extraction},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {426--436},
  abstract  = {Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.},
  url       = {http://www.aclweb.org/anthology/C18-1036}
}