@InProceedings{wiegand-EtAl:2018:N18-1,
  author    = {Wiegand, Michael  and  Ruppenhofer, Josef  and  Schmidt, Anna  and  Greenberg, Clayton},
  title     = {Inducing a Lexicon of Abusive Words -- a Feature-Based Approach},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1046--1056},
  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 cross-domain detection of abusive microposts.},
  url       = {http://www.aclweb.org/anthology/N18-1095}
}