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.
- Sprache
-
Englisch
- Thema
-
Vergleich <Rhetorik>
Datensatz
Crowdsourcing
Beleidigung
Beschimpfung
Sprache
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Wiegand, Michael
Geulig, Maja
Ruppenhofer, Josef
- Ereignis
-
Veröffentlichung
- (wer)
-
Stroudsburg, Pennsylvania : Association for Computational Linguistics
- (wann)
-
2021-04-22
- URN
-
urn:nbn:de:bsz:mh39-104170
- Letzte Aktualisierung
-
06.03.2025, 09:00 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Konferenzbeitrag
Beteiligte
- Wiegand, Michael
- Geulig, Maja
- Ruppenhofer, Josef
- Stroudsburg, Pennsylvania : Association for Computational Linguistics
Entstanden
- 2021-04-22
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