@inproceedings{yadav-etal-2024-dkit,
title = "dkit@{LT}-{EDI}-2024: Detecting Homophobia and Transphobia in {E}nglish Social Media Comments",
author = "Yadav, Sargam and
Kaushik, Abhishek and
McDaid, Kevin",
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-1.36",
pages = "271--276",
abstract = "Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.",
}
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<abstract>Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.</abstract>
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%0 Conference Proceedings
%T dkit@LT-EDI-2024: Detecting Homophobia and Transphobia in English Social Media Comments
%A Yadav, Sargam
%A Kaushik, Abhishek
%A McDaid, Kevin
%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 yadav-etal-2024-dkit
%X Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.
%U https://aclanthology.org/2024.ltedi-1.36
%P 271-276
Markdown (Informal)
[dkit@LT-EDI-2024: Detecting Homophobia and Transphobia in English Social Media Comments](https://aclanthology.org/2024.ltedi-1.36) (Yadav et al., LTEDI-WS 2024)
ACL