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

Jian Zhu; Zuoyu Tian; Sandra Kübler

doi:10.18653/v1/S19-2138

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

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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
Bibkey:
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)
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PDF:: https://aclanthology.org/S19-2138.pdf

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@inproceedings{zhu-etal-2019-um,
    title = "{UM}-{IU}@{LING} at {S}em{E}val-2019 Task 6: Identifying Offensive Tweets Using {BERT} and {SVM}s",
    author = {Zhu, Jian  and
      Tian, Zuoyu  and
      K{\"u}bler, Sandra},
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2138/",
    doi = "10.18653/v1/S19-2138",
    pages = "788--795",
    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."
}

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%0 Conference Proceedings
%T UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
%A Zhu, Jian
%A Tian, Zuoyu
%A Kübler, Sandra
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F zhu-etal-2019-um
%X 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.
%R 10.18653/v1/S19-2138
%U https://aclanthology.org/S19-2138/
%U https://doi.org/10.18653/v1/S19-2138
%P 788-795

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Markdown (Informal)

[UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs](https://aclanthology.org/S19-2138/) (Zhu et al., SemEval 2019)

UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs (Zhu et al., SemEval 2019)

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.