@inproceedings{paetzold-etal-2019-utfpr,
title = "{UTFPR} at {S}em{E}val-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks",
author = "Paetzold, Gustavo Henrique and
Zampieri, Marcos and
Malmasi, Shervin",
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/S19-2093",
doi = "10.18653/v1/S19-2093",
pages = "519--523",
abstract = "In this paper we revisit the problem of automatically identifying hate speech in posts from social media. We approach the task using a system based on minimalistic compositional Recurrent Neural Networks (RNN). We tested our approach on the SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (HatEval) shared task dataset. The dataset made available by the HatEval organizers contained English and Spanish posts retrieved from Twitter annotated with respect to the presence of hateful content and its target. In this paper we present the results obtained by our system in comparison to the other entries in the shared task. Our system achieved competitive performance ranking 7th in sub-task A out of 62 systems in the English track.",
}
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%0 Conference Proceedings
%T UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks
%A Paetzold, Gustavo Henrique
%A Zampieri, Marcos
%A Malmasi, Shervin
%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 paetzold-etal-2019-utfpr
%X In this paper we revisit the problem of automatically identifying hate speech in posts from social media. We approach the task using a system based on minimalistic compositional Recurrent Neural Networks (RNN). We tested our approach on the SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (HatEval) shared task dataset. The dataset made available by the HatEval organizers contained English and Spanish posts retrieved from Twitter annotated with respect to the presence of hateful content and its target. In this paper we present the results obtained by our system in comparison to the other entries in the shared task. Our system achieved competitive performance ranking 7th in sub-task A out of 62 systems in the English track.
%R 10.18653/v1/S19-2093
%U https://aclanthology.org/S19-2093
%U https://doi.org/10.18653/v1/S19-2093
%P 519-523
Markdown (Informal)
[UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks](https://aclanthology.org/S19-2093) (Paetzold et al., SemEval 2019)
ACL