Konferenzbeitrag
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.
- Language
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Englisch
- Subject
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Automatische Sprachanalyse
Forschungsdaten
Datensatz
Beleidigung
Beschimpfung
Sprache
- Event
-
Geistige Schöpfung
- (who)
-
Wiegand, Michael
Ruppenhofer, Josef
Eder, Elisabeth
- Event
-
Veröffentlichung
- (who)
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Stroudsburg, Pennsylvania : Association for Computational Linguistics
- (when)
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2021-06-04
- URN
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urn:nbn:de:bsz:mh39-104498
- Last update
-
06.03.2025, 9:00 AM CET
Data provider
This object is provided by:
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Wiegand, Michael
- Ruppenhofer, Josef
- Eder, Elisabeth
- Stroudsburg, Pennsylvania : Association for Computational Linguistics
Time of origin
- 2021-06-04
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