NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs

Jonathan Rusert, Padmini Srinivasan


Abstract
This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90% accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63% accuracy for trinary classification (group vs individual vs other).
Anthology ID:
S19-2125
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:
704–711
Language:
URL:
https://aclanthology.org/S19-2125
DOI:
10.18653/v1/S19-2125
Bibkey:
Cite (ACL):
Jonathan Rusert and Padmini Srinivasan. 2019. NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 704–711, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs (Rusert & Srinivasan, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2125.pdf