@inproceedings{WiegandWolfRuppenhofer2019,
  author    = {Michael Wiegand and Maximilian Wolf and Josef Ruppenhofer},
  title     = {Detecting derogatory compounds – an unsupervised approach},
  series = {The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Proceedings of the Conference Vol. 1. Minneapolis, Minnesota, June 2 - June 7, 2019},
  editor    = {Jill Burstein and Christy Doran and Thamar Solorio},
  publisher = {The Association for Computational Linguistics},
  address   = {Stroudsburg, PA, USA},
  isbn      = {978-1-950737-13-0},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-90185},
  pages     = {2076 -- 2081},
  year      = {2019},
  abstract  = {We examine the new task of detecting derogatory compounds (e.g. curry muncher). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. idiot) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.},
  language  = {en}
}