@inproceedings{shanmugavadivel-etal-2024-kec-ai,
title = "{KEC} {AI} {DSNLP}@{LT}-{EDI}-2024:Caste and Migration Hate Speech Detection using Machine Learning Techniques",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
M, Aiswarya and
T, Aruna and
S, Jeevaananth",
editor = {Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Durairaj, Thenmozhi and
Kov{\'a}cs, Gy{\"o}rgy and
Garc{\'\i}a Cumbreras, Miguel {\'A}ngel},
booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ltedi-1.24",
pages = "206--210",
abstract = "Commonly used language defines {``}hate speech{''} as objectionable statements that may jeopardize societal harmony by singling out a group or a person based on fundamental traits (including gender, caste, or religion). Using machine learning techniques, our research focuses on identifying hate speech in social media comments. Using a variety of machine learning methods, we created machine learning models to detect hate speech. An approximate Macro F1 of 0.60 was attained by the created models.",
}
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%0 Conference Proceedings
%T KEC AI DSNLP@LT-EDI-2024:Caste and Migration Hate Speech Detection using Machine Learning Techniques
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A M, Aiswarya
%A T, Aruna
%A S, Jeevaananth
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shanmugavadivel-etal-2024-kec-ai
%X Commonly used language defines “hate speech” as objectionable statements that may jeopardize societal harmony by singling out a group or a person based on fundamental traits (including gender, caste, or religion). Using machine learning techniques, our research focuses on identifying hate speech in social media comments. Using a variety of machine learning methods, we created machine learning models to detect hate speech. An approximate Macro F1 of 0.60 was attained by the created models.
%U https://aclanthology.org/2024.ltedi-1.24
%P 206-210
Markdown (Informal)
[KEC AI DSNLP@LT-EDI-2024:Caste and Migration Hate Speech Detection using Machine Learning Techniques](https://aclanthology.org/2024.ltedi-1.24) (Shanmugavadivel et al., LTEDI-WS 2024)
ACL