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
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- 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
- Cite (ACL):
- Michael Wiegand, Maximilian Wolf, and Josef Ruppenhofer. 2019. Detecting Derogatory Compounds – An Unsupervised Approach. In 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), pages 2076–2081, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Derogatory Compounds – An Unsupervised Approach (Wiegand et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1211.pdf