Abstract
This paper describes the UM-IU@LING’s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.- Anthology ID:
- S19-2138
- 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:
- 788–795
- Language:
- URL:
- https://aclanthology.org/S19-2138
- DOI:
- 10.18653/v1/S19-2138
- Cite (ACL):
- Jian Zhu, Zuoyu Tian, and Sandra Kübler. 2019. UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 788–795, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs (Zhu et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S19-2138.pdf
- Code
- zytian9/SemEval-2019-Task-6