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Implicitly abusive language – What does it actually look like and why are we not getting there?

  • 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.

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Author:Michael WiegandORCiDGND, Josef RuppenhoferORCiDGND, Elisabeth Eder
Parent Title (English):Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, Pennsylvania
Editor:Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Document Type:Conference Proceeding
Year of first Publication:2021
Date of Publication (online):2021/06/04
Tag:abusive language; implicit abuse; implicitly abusive language
GND Keyword:Automatische Sprachanalyse; Beleidigung; Beschimpfung; Datensatz; Forschungsdaten
First Page:576
Last Page:587
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Program areas:P2: Mündliche Korpora
Licence (English):License LogoCreative Commons - Attribution 4.0 International