@inproceedings{swaminathan-etal-2022-ssncse,
title = "{SSNCSE}{\_}{NLP}@{LT}-{EDI}-{ACL}2022: Homophobia/Transphobia Detection in Multiple Languages using {SVM} Classifiers and {BERT}-based Transformers",
author = "Swaminathan, Krithika and
B, Bharathi and
G L, Gayathri and
Sampath, Hrishik",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.34",
doi = "10.18653/v1/2022.ltedi-1.34",
pages = "239--244",
abstract = "Over the years, there has been a slow but steady change in the attitude of society towards different kinds of sexuality. However, on social media platforms, where people have the license to be anonymous, toxic comments targeted at homosexuals, transgenders and the LGBTQ+ community are not uncommon. Detection of homophobic comments on social media can be useful in making the internet a safer place for everyone. For this task, we used a combination of word embeddings and SVM Classifiers as well as some BERT-based transformers. We achieved a weighted F1-score of 0.93 on the English dataset, 0.75 on the Tamil dataset and 0.87 on the Tamil-English Code-Mixed dataset.",
}
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<abstract>Over the years, there has been a slow but steady change in the attitude of society towards different kinds of sexuality. However, on social media platforms, where people have the license to be anonymous, toxic comments targeted at homosexuals, transgenders and the LGBTQ+ community are not uncommon. Detection of homophobic comments on social media can be useful in making the internet a safer place for everyone. For this task, we used a combination of word embeddings and SVM Classifiers as well as some BERT-based transformers. We achieved a weighted F1-score of 0.93 on the English dataset, 0.75 on the Tamil dataset and 0.87 on the Tamil-English Code-Mixed dataset.</abstract>
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%0 Conference Proceedings
%T SSNCSE_NLP@LT-EDI-ACL2022: Homophobia/Transphobia Detection in Multiple Languages using SVM Classifiers and BERT-based Transformers
%A Swaminathan, Krithika
%A B, Bharathi
%A G L, Gayathri
%A Sampath, Hrishik
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F swaminathan-etal-2022-ssncse
%X Over the years, there has been a slow but steady change in the attitude of society towards different kinds of sexuality. However, on social media platforms, where people have the license to be anonymous, toxic comments targeted at homosexuals, transgenders and the LGBTQ+ community are not uncommon. Detection of homophobic comments on social media can be useful in making the internet a safer place for everyone. For this task, we used a combination of word embeddings and SVM Classifiers as well as some BERT-based transformers. We achieved a weighted F1-score of 0.93 on the English dataset, 0.75 on the Tamil dataset and 0.87 on the Tamil-English Code-Mixed dataset.
%R 10.18653/v1/2022.ltedi-1.34
%U https://aclanthology.org/2022.ltedi-1.34
%U https://doi.org/10.18653/v1/2022.ltedi-1.34
%P 239-244
Markdown (Informal)
[SSNCSE_NLP@LT-EDI-ACL2022: Homophobia/Transphobia Detection in Multiple Languages using SVM Classifiers and BERT-based Transformers](https://aclanthology.org/2022.ltedi-1.34) (Swaminathan et al., LTEDI 2022)
ACL