@InProceedings{liu-EtAl:2017:EMNLP20171,
  author    = {Liu, Liyuan  and  Ren, Xiang  and  Zhu, Qi  and  Zhi, Shi  and  Gui, Huan  and  Ji, Heng  and  Han, Jiawei},
  title     = {Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach},
  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     = {46--56},
  abstract  = {Relation extraction is a fundamental task in information extraction.
	Most existing methods have heavy reliance on annotations labeled by human
	experts, which are costly and time-consuming.
	To overcome this drawback, we propose a novel framework, REHession, to conduct
	relation extractor learning using annotations from heterogeneous information
	source, e.g., knowledge base and domain heuristics.
	These annotations, referred as heterogeneous supervision, often conflict with
	each other, which brings a new challenge to the original relation extraction
	task: how to infer the true label from noisy labels for a given instance.
	Identifying context information as the backbone of both relation extraction and
	true label discovery, we adopt embedding techniques to learn the distributed
	representations of context, which bridges all components with mutual
	enhancement in an iterative fashion.
	Extensive experimental results demonstrate the superiority of REHession over
	the state-of-the-art.},
  url       = {https://www.aclweb.org/anthology/D17-1005}
}