@inproceedings{bala-krishnamurthy-2023-abhipaw-dravidianlangtech,
title = "{A}bhi{P}aw@ {D}ravidian{L}ang{T}ech: Abusive Comment Detection in {T}amil and {T}elugu using Logistic Regression",
author = "Bala, Abhinaba and
Krishnamurthy, Parameswari",
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-1.33",
pages = "231--234",
abstract = "Abusive comments in online platforms have become a significant concern, necessitating the development of effective detection systems. However, limited work has been done in low resource languages, including Dravidian languages. This paper addresses this gap by focusing on abusive comment detection in a dataset containing Tamil, Tamil-English and Telugu-English code-mixed comments. Our methodology involves logistic regression and explores suitable embeddings to enhance the performance of the detection model. Through rigorous experimentation, we identify the most effective combination of logistic regression and embeddings. The results demonstrate the performance of our proposed model, which contributes to the development of robust abusive comment detection systems in low resource language settings. Keywords: Abusive comment detection, Dravidian languages, logistic regression, embeddings, low resource languages, code-mixed dataset.",
}
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<abstract>Abusive comments in online platforms have become a significant concern, necessitating the development of effective detection systems. However, limited work has been done in low resource languages, including Dravidian languages. This paper addresses this gap by focusing on abusive comment detection in a dataset containing Tamil, Tamil-English and Telugu-English code-mixed comments. Our methodology involves logistic regression and explores suitable embeddings to enhance the performance of the detection model. Through rigorous experimentation, we identify the most effective combination of logistic regression and embeddings. The results demonstrate the performance of our proposed model, which contributes to the development of robust abusive comment detection systems in low resource language settings. Keywords: Abusive comment detection, Dravidian languages, logistic regression, embeddings, low resource languages, code-mixed dataset.</abstract>
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%0 Conference Proceedings
%T AbhiPaw@ DravidianLangTech: Abusive Comment Detection in Tamil and Telugu using Logistic Regression
%A Bala, Abhinaba
%A Krishnamurthy, Parameswari
%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 bala-krishnamurthy-2023-abhipaw-dravidianlangtech
%X Abusive comments in online platforms have become a significant concern, necessitating the development of effective detection systems. However, limited work has been done in low resource languages, including Dravidian languages. This paper addresses this gap by focusing on abusive comment detection in a dataset containing Tamil, Tamil-English and Telugu-English code-mixed comments. Our methodology involves logistic regression and explores suitable embeddings to enhance the performance of the detection model. Through rigorous experimentation, we identify the most effective combination of logistic regression and embeddings. The results demonstrate the performance of our proposed model, which contributes to the development of robust abusive comment detection systems in low resource language settings. Keywords: Abusive comment detection, Dravidian languages, logistic regression, embeddings, low resource languages, code-mixed dataset.
%U https://aclanthology.org/2023.dravidianlangtech-1.33
%P 231-234
Markdown (Informal)
[AbhiPaw@ DravidianLangTech: Abusive Comment Detection in Tamil and Telugu using Logistic Regression](https://aclanthology.org/2023.dravidianlangtech-1.33) (Bala & Krishnamurthy, DravidianLangTech-WS 2023)
ACL