@inproceedings{bansal-etal-2019-tubingen,
title = {{HAD}-{T}{\"u}bingen at {S}em{E}val-2019 Task 6: Deep Learning Analysis of Offensive Language on {T}witter: Identification and Categorization},
author = "Bansal, Himanshu and
Nagel, Daniel and
Soloveva, Anita",
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/S19-2111/",
doi = "10.18653/v1/S19-2111",
pages = "622--627",
abstract = {This paper describes the submissions of our team, HAD-T{\"u}bingen, for the SemEval 2019 - Task 6: ``OffensEval: Identifying and Categorizing Offensive Language in Social Media''. We participated in all the three sub-tasks: Sub-task A - ``Offensive language identification'', sub-task B - ``Automatic categorization of offense types'' and sub-task C - ``Offense target identification''. As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model. The best macro-average F1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.}
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<abstract>This paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019 - Task 6: “OffensEval: Identifying and Categorizing Offensive Language in Social Media”. We participated in all the three sub-tasks: Sub-task A - “Offensive language identification”, sub-task B - “Automatic categorization of offense types” and sub-task C - “Offense target identification”. As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model. The best macro-average F1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.</abstract>
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%0 Conference Proceedings
%T HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization
%A Bansal, Himanshu
%A Nagel, Daniel
%A Soloveva, Anita
%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 bansal-etal-2019-tubingen
%X This paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019 - Task 6: “OffensEval: Identifying and Categorizing Offensive Language in Social Media”. We participated in all the three sub-tasks: Sub-task A - “Offensive language identification”, sub-task B - “Automatic categorization of offense types” and sub-task C - “Offense target identification”. As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model. The best macro-average F1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.
%R 10.18653/v1/S19-2111
%U https://aclanthology.org/S19-2111/
%U https://doi.org/10.18653/v1/S19-2111
%P 622-627
Markdown (Informal)
[HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization](https://aclanthology.org/S19-2111/) (Bansal et al., SemEval 2019)
ACL