TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wiegand, Michael
A1  - Ruppenhofer, Josef
A1  - Eder, Elisabeth
ED  - Toutanova, Kristina
ED  - Rumshisky, Anna
ED  - Zettlemoyer, Luke
ED  - Hakkani-Tur, Dilek
ED  - Beltagy, Iz
ED  - Bethard, Steven
ED  - Cotterell, Ryan
ED  - Chakraborty, Tanmoy
ED  - Zhou, Yichao
T1  - Implicitly abusive language – What does it actually look like and why are we not getting there?
T2  - Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
N2  - Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.
KW  - Automatische Sprachanalyse
KW  - Forschungsdaten
KW  - Datensatz
KW  - Beleidigung
KW  - Beschimpfung
KW  - implicitly abusive language
KW  - implicit abuse
KW  - abusive language
Y1  - 2021
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104498
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104498
UR  - https://www.aclweb.org/anthology/2021.naacl-main.48
SN  - 978-1-954085-46-6
SB  - 978-1-954085-46-6
SP  - 576
EP  - 587
PB  - Association for Computational Linguistics
CY  - Stroudsburg, Pennsylvania
ER  -