Detecting Derogatory Compounds – An Unsupervised Approach

Michael Wiegand, Maximilian Wolf, Josef Ruppenhofer


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.
Anthology ID:
N19-1211
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2076–2081
Language:
URL:
https://aclanthology.org/N19-1211
DOI:
10.18653/v1/N19-1211
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N19-1211.pdf
Video:
 https://vimeo.com/354243652