@incollection{RehbeinRuppenhofer2018,
  author    = {Ines Rehbein and Josef Ruppenhofer},
  title     = {Detecting annotation noise in automatically labelled data},
  series = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), vol. 1 (Long Papers). July 30 - August 4, 2017 Vancouver, Canada},
  publisher = {The Association for Computational Linguistics},
  address   = {Stroudsburg PA, USA},
  isbn      = {978-1-945626-75-3},
  doi       = {10.18653/v1/P17-1107},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-80343},
  pages     = {1160 -- 1170},
  year      = {2018},
  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.},
  language  = {en}
}