Abstract
This paper presents the Know-Center system submitted for task 5 of the SemEval-2019 workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether it contains hateful speech and second, to determine the target and level of aggression used. For this purpose our system utilizes word embeddings and a neural network architecture, consisting of both dilated and traditional convolution layers. We achieved average F1-scores of 0.57 and 0.74 for English and Spanish respectively.- Anthology ID:
- S19-2076
- 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:
- 431–435
- Language:
- URL:
- https://aclanthology.org/S19-2076
- DOI:
- 10.18653/v1/S19-2076
- Cite (ACL):
- Kevin Winter and Roman Kern. 2019. Know-Center at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter using CNNs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 431–435, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Know-Center at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter using CNNs (Winter & Kern, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S19-2076.pdf