ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter
Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, Torsten Zesch
Abstract
In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.- Anthology ID:
- S19-2078
- 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:
- 441–446
- Language:
- URL:
- https://aclanthology.org/S19-2078
- DOI:
- 10.18653/v1/S19-2078
- Cite (ACL):
- Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, and Torsten Zesch. 2019. ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 441–446, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter (Zhang et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2078.pdf