@inproceedings{perello-etal-2019-ua,
title = "{UA} at {S}em{E}val-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection",
author = "Perell{\'o}, Carlos and
Tom{\'a}s, David and
Garcia-Garcia, Alberto and
Garcia-Rodriguez, Jose and
Camacho-Collados, Jose",
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/S19-2091",
doi = "10.18653/v1/S19-2091",
pages = "508--513",
abstract = "This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate. The purpose of this work is to build a strong baseline for hate speech detection, using a traditional machine learning approach with standard textual features, which could serve in a near future as a reference to compare with deep learning systems. We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification). Despite its simplicity, our system obtained a remarkable F1-score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.",
}
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<abstract>This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate. The purpose of this work is to build a strong baseline for hate speech detection, using a traditional machine learning approach with standard textual features, which could serve in a near future as a reference to compare with deep learning systems. We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification). Despite its simplicity, our system obtained a remarkable F1-score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.</abstract>
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%0 Conference Proceedings
%T UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection
%A Perelló, Carlos
%A Tomás, David
%A Garcia-Garcia, Alberto
%A Garcia-Rodriguez, Jose
%A Camacho-Collados, Jose
%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 perello-etal-2019-ua
%X This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate. The purpose of this work is to build a strong baseline for hate speech detection, using a traditional machine learning approach with standard textual features, which could serve in a near future as a reference to compare with deep learning systems. We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification). Despite its simplicity, our system obtained a remarkable F1-score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.
%R 10.18653/v1/S19-2091
%U https://aclanthology.org/S19-2091
%U https://doi.org/10.18653/v1/S19-2091
%P 508-513
Markdown (Informal)
[UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection](https://aclanthology.org/S19-2091) (Perelló et al., SemEval 2019)
ACL