ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter

Huangpan Zhang; Michael Wojatzki; Tobias Horsmann; Torsten Zesch

doi:10.18653/v1/S19-2078

ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter

Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, Torsten Zesch

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Abstract

In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.

Anthology ID:: S19-2078
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:: 441–446
Language:
URL:: https://aclanthology.org/S19-2078/
DOI:: 10.18653/v1/S19-2078
Bibkey:
Cite (ACL):: Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, and Torsten Zesch. 2019. ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 441–446, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):: ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter (Zhang et al., SemEval 2019)
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PDF:: https://aclanthology.org/S19-2078.pdf

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@inproceedings{zhang-etal-2019-ltl,
    title = "ltl.uni-due at {S}em{E}val-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in {T}witter",
    author = "Zhang, Huangpan  and
      Wojatzki, Michael  and
      Horsmann, Tobias  and
      Zesch, Torsten",
    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/S19-2078/",
    doi = "10.18653/v1/S19-2078",
    pages = "441--446",
    abstract = "In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39."
}

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%0 Conference Proceedings
%T ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter
%A Zhang, Huangpan
%A Wojatzki, Michael
%A Horsmann, Tobias
%A Zesch, Torsten
%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 zhang-etal-2019-ltl
%X In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.
%R 10.18653/v1/S19-2078
%U https://aclanthology.org/S19-2078/
%U https://doi.org/10.18653/v1/S19-2078
%P 441-446

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Markdown (Informal)

[ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter](https://aclanthology.org/S19-2078/) (Zhang et al., SemEval 2019)

ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter (Zhang et al., SemEval 2019)

ACL

Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, and Torsten Zesch. 2019. ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 441–446, Minneapolis, Minnesota, USA. Association for Computational Linguistics.