@inproceedings{RehbeinRuppenhofer2016,
  author    = {Ines Rehbein and Josef Ruppenhofer},
  title     = {Evaluating the Impact of Coder Errors on Active Learning},
  series = {HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  number    = {1},
  publisher = {Association for Computational Linguistics},
  address   = {Stroudsburg},
  isbn      = {978-1-932432-87-9},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-52929},
  pages     = {43 -- 51},
  year      = {2016},
  abstract  = {Active Learning (AL) has been proposed as a technique to reduce the amount of annotated data needed in the context of supervised classification. While various simulation studies for a number of NLP tasks have shown that AL works well on goldstandard data, there is some doubt whether the approach can be successful when applied to noisy, real-world data sets. This paper presents a thorough evaluation of the impact of annotation noise on AL and shows that systematic noise resulting from biased coder decisions can seriously harm the AL process. We present a method to filter out inconsistent annotations during AL and show that this makes AL far more robust when applied to noisy data.},
  language  = {en}
}