Inducing a Lexicon of Abusive Words – a Feature-Based Approach
Michael Wiegand, Josef Ruppenhofer, Anna Schmidt, Clayton Greenberg
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.- Anthology ID:
- N18-1095
- Volume:
- 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
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1046–1056
- Language:
- URL:
- https://aclanthology.org/N18-1095
- DOI:
- 10.18653/v1/N18-1095
- Cite (ACL):
- Michael Wiegand, Josef Ruppenhofer, Anna Schmidt, and Clayton Greenberg. 2018. Inducing a Lexicon of Abusive Words – a Feature-Based Approach. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1046–1056, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Inducing a Lexicon of Abusive Words – a Feature-Based Approach (Wiegand et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/N18-1095.pdf
- Code
- miwieg/naacl2018