TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wiegand, Michael
A1  - Ruppenhofer, Josef
A1  - Kleinbauer, Thomas
ED  - Burstein, Jill
ED  - Doran, Christy
ED  - Solorio, Thamar
T1  - Detection of abusive language: the problem of biased datasets
T2  - The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Proceedings of the Conference Vol. 1. Minneapolis, Minnesota, June 2 - June 7, 2019
N2  - We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.
KW  - Schimpfwort
KW  - Beleidigung
KW  - Verbalagression
KW  - Automatische Sprachanalyse
Y1  - 2019
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-90165
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-90165
UR  - https://www.aclweb.org/anthology/N19-1060
SN  - 978-1-950737-13-0
SB  - 978-1-950737-13-0
SP  - 602
EP  - 608
PB  - The Association for Computational Linguistics
CY  - Stroudsburg, PA, USA
ER  -