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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1046–1056
Language:
URL:
https://www.aclweb.org/anthology/N18-1095
DOI:
10.18653/v1/N18-1095
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1095.pdf
Video:
 http://vimeo.com/282325809