@InProceedings{dumitrache-aroyo-welty:2018:FEVER,
  author    = {Dumitrache, Anca  and  Aroyo, Lora  and  Welty, Chris},
  title     = {Crowdsourcing Semantic Label Propagation in Relation Classification},
  booktitle = {Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {16--21},
  abstract  = {Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels. Most progress in relation extraction and classification has been made with crowdsourced corrections to distant-supervised labels, and there is evidence that indicates still more would be better. In this paper, we explore the problem of propagating human annotation signals gathered for open-domain relation classification through the CrowdTruth methodology for crowdsourcing, that captures ambiguity in annotations by measuring inter-annotator disagreement. Our approach propagates annotations to sentences that are similar in a low dimensional embedding space, expanding the number of labels by two orders of magnitude. Our experiments show significant improvement in a sentence-level multi-class relation classifier.},
  url       = {http://www.aclweb.org/anthology/W18-5503}
}