@inproceedings{shanmugavadivel-etal-2023-kec,
title = "{KEC}{\_}{AI}{\_}{NLP}@{D}ravidian{L}ang{T}ech: Abusive Comment Detection in {T}amil Language",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
R, Shri Durga and
S, Srigha and
J S, Sree Harene and
P, Yasvanth Bala",
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-1.43",
pages = "293--299",
abstract = "Our work aims to identify the negative comments that is associated with Counter-speech,Xenophobia, Homophobia,Transphobia, Misandry, Misogyny, None-of-the-above categories, In order to identify these categories from the given dataset, we propose three different models such as traditional machine learning techniques, deep learning model and transfer Learning model called BERT is also used to analyze the texts. In the Tamil dataset, we are training the models with Train dataset and test the models with Validation data. Our Team Participated in the shared task organised by DravidianLangTech and secured 4th rank in the task of abusive comment detection in Tamil with a macro- f1 score of 0.35. Also, our run was submitted for abusive comment detection in code-mixed languages (Tamil-English) and secured 6th rank with a macro-f1 score of 0.42.",
}
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%0 Conference Proceedings
%T KEC_AI_NLP@DravidianLangTech: Abusive Comment Detection in Tamil Language
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A R, Shri Durga
%A S, Srigha
%A J S, Sree Harene
%A P, Yasvanth Bala
%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 shanmugavadivel-etal-2023-kec
%X Our work aims to identify the negative comments that is associated with Counter-speech,Xenophobia, Homophobia,Transphobia, Misandry, Misogyny, None-of-the-above categories, In order to identify these categories from the given dataset, we propose three different models such as traditional machine learning techniques, deep learning model and transfer Learning model called BERT is also used to analyze the texts. In the Tamil dataset, we are training the models with Train dataset and test the models with Validation data. Our Team Participated in the shared task organised by DravidianLangTech and secured 4th rank in the task of abusive comment detection in Tamil with a macro- f1 score of 0.35. Also, our run was submitted for abusive comment detection in code-mixed languages (Tamil-English) and secured 6th rank with a macro-f1 score of 0.42.
%U https://aclanthology.org/2023.dravidianlangtech-1.43
%P 293-299
Markdown (Informal)
[KEC_AI_NLP@DravidianLangTech: Abusive Comment Detection in Tamil Language](https://aclanthology.org/2023.dravidianlangtech-1.43) (Shanmugavadivel et al., DravidianLangTech-WS 2023)
ACL
- Kogilavani Shanmugavadivel, Malliga Subramanian, Shri Durga R, Srigha S, Sree Harene J S, and Yasvanth Bala P. 2023. KEC_AI_NLP@DravidianLangTech: Abusive Comment Detection in Tamil Language. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 293–299, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.