TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wiegand, Michael
A1  - Geulig, Maja
A1  - Ruppenhofer, Josef
ED  - Merlo, Paola
ED  - Tiedemann, Jörg
ED  - Tsarfaty, Reut
T1  - Implicitly abusive comparisons – a new dataset and linguistic analysis
T2  - Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
N2  - 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.
KW  - Vergleich <Rhetorik>
KW  - Datensatz
KW  - Crowdsourcing
KW  - Beleidigung
KW  - Beschimpfung
KW  - abusive comparisons
KW  - implicitly abusive comparisons
KW  - abusive language
Y1  - 2021
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104170
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104170
UR  - https://www.aclweb.org/anthology/2021.eacl-main.27
SP  - 358
EP  - 368
PB  - Association for Computational Linguistics
CY  - Stroudsburg, Pennsylvania
ER  -