@inproceedings{zampieri-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media ({O}ffens{E}val)",
author = "Zampieri, Marcos and
Malmasi, Shervin and
Nakov, Preslav and
Rosenthal, Sara and
Farra, Noura and
Kumar, Ritesh",
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/S19-2010/",
doi = "10.18653/v1/S19-2010",
pages = "75--86",
abstract = "We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report."
}
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%0 Conference Proceedings
%T SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
%A Zampieri, Marcos
%A Malmasi, Shervin
%A Nakov, Preslav
%A Rosenthal, Sara
%A Farra, Noura
%A Kumar, Ritesh
%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 zampieri-etal-2019-semeval
%X We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.
%R 10.18653/v1/S19-2010
%U https://aclanthology.org/S19-2010/
%U https://doi.org/10.18653/v1/S19-2010
%P 75-86
Markdown (Informal)
[SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://aclanthology.org/S19-2010/) (Zampieri et al., SemEval 2019)
ACL