@inproceedings{kumar-etal-2019-online,
title = "Online abuse detection: the value of preprocessing and neural attention models",
author = "Kumar, Dhruv and
Cohen, Robin and
Golab, Lukasz",
editor = "Balahur, Alexandra and
Klinger, Roman and
Hoste, Veronique and
Strapparava, Carlo and
De Clercq, Orphee",
booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = jun,
year = "2019",
address = "Minneapolis, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1303",
doi = "10.18653/v1/W19-1303",
pages = "16--24",
abstract = "We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-vocabulary words in toxic content. Finally, we conduct extensive experiments on the Wikipedia Talk page datasets, showing improved predictive power over the previous state-of-the-art.",
}
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%0 Conference Proceedings
%T Online abuse detection: the value of preprocessing and neural attention models
%A Kumar, Dhruv
%A Cohen, Robin
%A Golab, Lukasz
%Y Balahur, Alexandra
%Y Klinger, Roman
%Y Hoste, Veronique
%Y Strapparava, Carlo
%Y De Clercq, Orphee
%S Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F kumar-etal-2019-online
%X We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-vocabulary words in toxic content. Finally, we conduct extensive experiments on the Wikipedia Talk page datasets, showing improved predictive power over the previous state-of-the-art.
%R 10.18653/v1/W19-1303
%U https://aclanthology.org/W19-1303
%U https://doi.org/10.18653/v1/W19-1303
%P 16-24
Markdown (Informal)
[Online abuse detection: the value of preprocessing and neural attention models](https://aclanthology.org/W19-1303) (Kumar et al., WASSA 2019)
ACL