TüKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification

Madeeswaran Kannan, Lukas Stein


Abstract
We describe our system (TüKaSt) submitted for Task 6: Offensive Language Classification, at SemEval 2019. We developed multiple SVM classifier models that used sentence-level dense vector representations of tweets enriched with sentiment information and term-weighting. Our best results achieved F1 scores of 0.734, 0.660 and 0.465 in the first, second and third sub-tasks respectively. We also describe a neural network model that was developed in parallel but not used during evaluation due to time constraints.
Anthology ID:
S19-2134
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
763–769
Language:
URL:
https://aclanthology.org/S19-2134
DOI:
10.18653/v1/S19-2134
Bibkey:
Cite (ACL):
Madeeswaran Kannan and Lukas Stein. 2019. TüKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 763–769, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
TüKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification (Kannan & Stein, SemEval 2019)
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PDF:
https://aclanthology.org/S19-2134.pdf