TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wiegand, Michael
A1  - Eder, Elisabeth
A1  - Ruppenhofer, Josef
ED  - Carpuat, Marine
ED  - de Marneffe, Marie-Catherine
ED  - Meza Ruiz, Ivan Vladimir
T1  - Identifying implicitly abusive remarks about identity groups using a linguistically informed approach
T2  - Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. July 10-15, 2022.
N2  - We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”). Implicitly abusive language are utterances not conveyed by abusive words (e.g. “bimbo” or “scum”). So far, the detection of such utterances could not be properly addressed since existing datasets displaying a high degree of implicit abuse are fairly biased. Following the recently-proposed strategy to solve implicit abuse by separately addressing its different subtypes, we present a new focused and less biased dataset that consists of the subtype of atomic negative sentences about identity groups. For that task, we model components that each address one facet of such implicit abuse, i.e. depiction as perpetrators, aspectual classification and non-conformist views. The approach generalizes across different identity groups and languages.
KW  - Datensatz
KW  - Beleidigung
KW  - Beschimpfung
KW  - Computerlinguistik
KW  - abusive remarks
KW  - identity groups
KW  - abusive language
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-112614
SN  - 978-1-955917-71-1
SB  - 978-1-955917-71-1
U6  - https://doi.org/10.18653/v1/2022.naacl-main.410
DO  - https://doi.org/10.18653/v1/2022.naacl-main.410
SP  - 5600
EP  - 5612
PB  - Stroudsburg
CY  - Association for Computational Linguistics
ER  -