@inproceedings{oberstrass-etal-2019-hhu,
title = "{HHU} at {S}em{E}val-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with {ELM}o",
author = "Oberstrass, Alexander and
Romberg, Julia and
Stoll, Anke and
Conrad, Stefan",
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/S19-2112",
doi = "10.18653/v1/S19-2112",
pages = "628--634",
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 $F_1$ of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo
%A Oberstrass, Alexander
%A Romberg, Julia
%A Stoll, Anke
%A Conrad, Stefan
%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 oberstrass-etal-2019-hhu
%X 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.
%R 10.18653/v1/S19-2112
%U https://aclanthology.org/S19-2112
%U https://doi.org/10.18653/v1/S19-2112
%P 628-634
Markdown (Informal)
[HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo](https://aclanthology.org/S19-2112) (Oberstrass et al., SemEval 2019)
ACL