%0 Conference Paper %1 zampieri2019semeval2019 %A Zampieri, Marcos %A Malmasi, Shervin %A Nakov, Preslav %A Rosenthal, Sara %A Farra, Noura %A Kumar, Ritesh %B Proceedings of the 13th International Workshop on Semantic Evaluation %D 2019 %I Association for Computational Linguistics %K antrag deconspire language media nlp offensive semeval semeval19 social %R 10.18653/v1/S19-2010 %T SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) %U https://www.aclweb.org/anthology/S19-2010/ %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. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on 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, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.

@inproceedings{zampieri2019semeval2019, 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. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on 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, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.}, added-at = {2020-09-04T11:26:05.000+0200}, author = {Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, biburl = {https://www.bibsonomy.org/bibtex/2ae0448b5af2cee0f342c3b788b9385fc/schwemmlein}, booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation}, doi = {10.18653/v1/S19-2010}, interhash = {1c7c3ab36d228ddc87ef1a74160781f3}, intrahash = {ae0448b5af2cee0f342c3b788b9385fc}, keywords = {antrag deconspire language media nlp offensive semeval semeval19 social}, publisher = {Association for Computational Linguistics}, timestamp = {2020-09-11T11:23:33.000+0200}, title = {SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)}, url = {https://www.aclweb.org/anthology/S19-2010/}, year = 2019 }