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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2125.pdf