@inproceedings{argota-vega-etal-2019-mineriaunam,
title = "{M}ineria{UNAM} at {S}em{E}val-2019 Task 5: Detecting Hate Speech in {T}witter using Multiple Features in a Combinatorial Framework",
author = "Argota Vega, Luis Enrique and
Reyes-Maga{\~n}a, Jorge Carlos and
G{\'o}mez-Adorno, Helena and
Bel-Enguix, Gemma",
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/S19-2079",
doi = "10.18653/v1/S19-2079",
pages = "447--452",
abstract = "This paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter. The task consists of two sub-tasks, in Spanish and English: (A) detection of hate speech and (B) classification of hateful tweets as aggressive or not, and identification of the target harassed as individual or group. We used linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, POS, among others). For task A, we trained a Support Vector Machine using a combinatorial framework, whereas for task B we followed a multi-labeled approach using the Random Forest classifier. Our approach achieved the highest F1-score in sub-task A for the Spanish language.",
}
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<abstract>This paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter. The task consists of two sub-tasks, in Spanish and English: (A) detection of hate speech and (B) classification of hateful tweets as aggressive or not, and identification of the target harassed as individual or group. We used linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, POS, among others). For task A, we trained a Support Vector Machine using a combinatorial framework, whereas for task B we followed a multi-labeled approach using the Random Forest classifier. Our approach achieved the highest F1-score in sub-task A for the Spanish language.</abstract>
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%0 Conference Proceedings
%T MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework
%A Argota Vega, Luis Enrique
%A Reyes-Magaña, Jorge Carlos
%A Gómez-Adorno, Helena
%A Bel-Enguix, Gemma
%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 argota-vega-etal-2019-mineriaunam
%X This paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter. The task consists of two sub-tasks, in Spanish and English: (A) detection of hate speech and (B) classification of hateful tweets as aggressive or not, and identification of the target harassed as individual or group. We used linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, POS, among others). For task A, we trained a Support Vector Machine using a combinatorial framework, whereas for task B we followed a multi-labeled approach using the Random Forest classifier. Our approach achieved the highest F1-score in sub-task A for the Spanish language.
%R 10.18653/v1/S19-2079
%U https://aclanthology.org/S19-2079
%U https://doi.org/10.18653/v1/S19-2079
%P 447-452
Markdown (Informal)
[MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework](https://aclanthology.org/S19-2079) (Argota Vega et al., SemEval 2019)
ACL