@inproceedings{WiegandRuppenhoferSchmidtetal.2019,
  author    = {Michael Wiegand and Josef Ruppenhofer and Anna Schmidt and Clayton Greenberg},
  title     = {Inducing a Lexicon of Abusive Words – a Feature-Based Approach},
  series = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 1-June 6, 2018, New Orleans, Louisiana, Volume 1 (Long Papers)},
  publisher = {Association for Computational Linguistics},
  address   = {Stroudsburg, PA},
  organization    = {Association for Computational Linguistics},
  isbn      = {978-1-948087-27-8},
  doi       = {10.18653/v1/N18-1095},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84719},
  pages     = {1046 -- 1056},
  year      = {2019},
  abstract  = {We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the crossdomain detection of abusive microposts.},
  language  = {en}
}