@InProceedings{rehbein-ruppenhofer:2017:Long,
  author    = {Rehbein, Ines  and  Ruppenhofer, Josef},
  title     = {Detecting annotation noise in automatically labelled data},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1160--1170},
  abstract  = {We introduce a method for error detection in automatically annotated text,
	aimed at supporting the creation of high-quality language resources at
	affordable cost. Our method combines an unsupervised generative model with
	human supervision from active learning. We test our approach on in-domain and
	out-of-domain data in two languages, in AL simulations and in a real world
	setting. For all settings, the results show that our method is able to detect
	annotation errors with high precision and high recall.},
  url       = {http://aclweb.org/anthology/P17-1107}
}