HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo
Alexander Oberstrass, Julia Romberg, Anke Stoll, Stefan Conrad
Abstract
We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6). Our results show that context embeddings are important features for the three different sub-tasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro F1 of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.- Anthology ID:
- S19-2112
- 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:
- 628–634
- Language:
- URL:
- https://aclanthology.org/S19-2112
- DOI:
- 10.18653/v1/S19-2112
- Cite (ACL):
- Alexander Oberstrass, Julia Romberg, Anke Stoll, and Stefan Conrad. 2019. HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 628–634, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo (Oberstrass et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S19-2112.pdf
- Data
- Hate Speech and Offensive Language