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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/N18-1095.pdf
Video:
 https://aclanthology.org/N18-1095.mp4
Code
 miwieg/naacl2018