@inproceedings{m-etal-2023-athena,
title = "Athena@{D}ravidian{L}ang{T}ech: Abusive Comment Detection in Code-Mixed Languages using Machine Learning Techniques",
author = "M, Hema and
Prem, Anza and
Sivanaiah, Rajalakshmi and
S, Angel Deborah",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.19",
pages = "147--151",
abstract = "The amount of digital material that is disseminated through various social media platforms has significantly increased in recent years. Online networks have gained popularity in recent years and have established themselves as goto resources for news, information, and entertainment. Nevertheless, despite the many advantages of using online networks, mounting evidence indicates that an increasing number of malicious actors are taking advantage of these networks to spread poison and hurt other people. This work aims to detect abusive content in youtube comments written in the languages like Tamil, Tamil-English (codemixed), Telugu-English (code-mixed). This work was undertaken as part of the {``}DravidianLangTech@ RANLP 2023{''} shared task. The Macro F1 values for the Tamil, Tamil-English, and Telugu-English datasets were 0.28, 0.37, and 0.6137 and secured 5th, 7th, 8th rank respectively.",
}
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%0 Conference Proceedings
%T Athena@DravidianLangTech: Abusive Comment Detection in Code-Mixed Languages using Machine Learning Techniques
%A M, Hema
%A Prem, Anza
%A Sivanaiah, Rajalakshmi
%A S, Angel Deborah
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F m-etal-2023-athena
%X The amount of digital material that is disseminated through various social media platforms has significantly increased in recent years. Online networks have gained popularity in recent years and have established themselves as goto resources for news, information, and entertainment. Nevertheless, despite the many advantages of using online networks, mounting evidence indicates that an increasing number of malicious actors are taking advantage of these networks to spread poison and hurt other people. This work aims to detect abusive content in youtube comments written in the languages like Tamil, Tamil-English (codemixed), Telugu-English (code-mixed). This work was undertaken as part of the “DravidianLangTech@ RANLP 2023” shared task. The Macro F1 values for the Tamil, Tamil-English, and Telugu-English datasets were 0.28, 0.37, and 0.6137 and secured 5th, 7th, 8th rank respectively.
%U https://aclanthology.org/2023.dravidianlangtech-1.19
%P 147-151
Markdown (Informal)
[Athena@DravidianLangTech: Abusive Comment Detection in Code-Mixed Languages using Machine Learning Techniques](https://aclanthology.org/2023.dravidianlangtech-1.19) (M et al., DravidianLangTech-WS 2023)
ACL