Konferenzbeitrag
Implicitly abusive comparisons – a new dataset and linguistic analysis
We examine the task of detecting implicitly abusive comparisons (e.g. “Your hair looks like you have been electrocuted”). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. “dumbass” or “scum”) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.
- Language
-
Englisch
- Subject
-
Vergleich <Rhetorik>
Datensatz
Crowdsourcing
Beleidigung
Beschimpfung
Sprache
- Event
-
Geistige Schöpfung
- (who)
-
Wiegand, Michael
Geulig, Maja
Ruppenhofer, Josef
- Event
-
Veröffentlichung
- (who)
-
Stroudsburg, Pennsylvania : Association for Computational Linguistics
- (when)
-
2021-04-22
- URN
-
urn:nbn:de:bsz:mh39-104170
- Last update
-
06.03.2025, 9:00 AM CET
Data provider
This object is provided by:
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Wiegand, Michael
- Geulig, Maja
- Ruppenhofer, Josef
- Stroudsburg, Pennsylvania : Association for Computational Linguistics
Time of origin
- 2021-04-22
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