@InProceedings{zhang-wang:2017:EMNLP2017,
  author    = {Zhang, Qing  and  Wang, Houfeng},
  title     = {Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective},
  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     = {1808--1813},
  abstract  = {For the task of relation extraction, distant supervision is an efficient
	approach to generate labeled data by aligning knowledge base with free texts.
	The essence of it is a challenging incomplete multi-label classification
	problem with sparse and noisy features. To address the challenge, this work
	presents a novel nonparametric Bayesian formulation for the task. Experiment
	results show substantially higher top precision improvements over the
	traditional state-of-the-art approaches.},
  url       = {https://www.aclweb.org/anthology/D17-1192}
}