@inproceedings{benito-etal-2019-gsi,
title = "{GSI}-{UPM} at {S}em{E}val-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on {T}witter",
author = "Benito, Diego and
Araque, Oscar and
Iglesias, Carlos A.",
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/S19-2070",
doi = "10.18653/v1/S19-2070",
pages = "396--403",
abstract = "This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS. This combination of several features is fine-tuned through ablation tests, demonstrating the usefulness of different features. While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish sub-task A.",
}
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%0 Conference Proceedings
%T GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter
%A Benito, Diego
%A Araque, Oscar
%A Iglesias, Carlos A.
%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 benito-etal-2019-gsi
%X This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS. This combination of several features is fine-tuned through ablation tests, demonstrating the usefulness of different features. While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish sub-task A.
%R 10.18653/v1/S19-2070
%U https://aclanthology.org/S19-2070
%U https://doi.org/10.18653/v1/S19-2070
%P 396-403
Markdown (Informal)
[GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter](https://aclanthology.org/S19-2070) (Benito et al., SemEval 2019)
ACL