• Computer Science
  • Published in SemEval@NAACL-HLT 2019

UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs

@inproceedings{Zhu2019UMIULINGAS,
  title={UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs},
  author={Jian Zhu and Zuoyu Tian and Sandra K{\"u}bler},
  booktitle={SemEval@NAACL-HLT},
  year={2019}
}
This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6: OffensEval. 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… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES

Challenges in discriminating profanity from hate speech

  • J. Exp. Theor. Artif. Intell.
  • 2017
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Deep Contextualized Word Representations

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL