@inproceedings{saroj-etal-2020-irlab,
title = "{IR}lab@{IITV} at {S}em{E}val-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using {SVM}",
author = "Saroj, Anita and
Chanda, Supriya and
Pal, Sukomal",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.265",
doi = "10.18653/v1/2020.semeval-1.265",
pages = "2012--2016",
abstract = "This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro $F_1$ score of 0.779 and 0.718 for Arabic and Greek, respectively.",
}
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<abstract>This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro F₁ score of 0.779 and 0.718 for Arabic and Greek, respectively.</abstract>
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%0 Conference Proceedings
%T IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM
%A Saroj, Anita
%A Chanda, Supriya
%A Pal, Sukomal
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F saroj-etal-2020-irlab
%X This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro F₁ score of 0.779 and 0.718 for Arabic and Greek, respectively.
%R 10.18653/v1/2020.semeval-1.265
%U https://aclanthology.org/2020.semeval-1.265
%U https://doi.org/10.18653/v1/2020.semeval-1.265
%P 2012-2016
Markdown (Informal)
[IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM](https://aclanthology.org/2020.semeval-1.265) (Saroj et al., SemEval 2020)
ACL