Gemes, K. A., Kovacs, A., Reichel, M., & Recski, G. (2021). Offensive text detection on English Twitter with deep learning models and rule-based systems. In P. Mehta, T. Mandl, P. Majumder, & M. Mitra (Eds.), FIRE-WN 2021 [FIRE 2021 Working Notes] (pp. 283–296). CEUR-WS.org. https://doi.org/10.34726/4342
E194-04 - Forschungsbereich Data Science E194 - Institut für Information Systems Engineering
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Erschienen in:
FIRE-WN 2021 [FIRE 2021 Working Notes]
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Band:
3159
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Datum (veröffentlicht):
2021
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Veranstaltungsname:
Forum for Information Retrieval Evaluation
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Veranstaltungszeitraum:
13-Dez-2021 - 17-Dez-2021
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Veranstaltungsort:
Gandhinagar, Indien
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Umfang:
14
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Verlag:
CEUR-WS.org
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Peer Reviewed:
Ja
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Keywords:
social media data; hate speech detection; rule-based methods; deep learning; text classification
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Abstract:
This paper describes the systems the TUW-Inf team submitted for the HASOC 2021 shared task on identifying offensive comments in social media. Besides a simple BERT-based classifier that achieved one of the highest F-scores on the binary classification task, we also build a high-precision rule-based classifier using a custom framework for human-in-the-loop learning. Both of our approaches are also evaluated qualitatively by manual analysis of 150 tweets, which also highlights possible controversies in the ground truth labels of the HASOC dataset
CC BY 4.0
