@inproceedings{yigezu-etal-2023-habesha-dravidianlangtech,
title = "Habesha@{D}ravidian{L}ang{T}ech: Abusive Comment Detection using Deep Learning Approach",
author = "Yigezu, Mesay Gemeda and
Kanta, Selam and
Kolesnikova, Olga and
Sidorov, Grigori and
Gelbukh, Alexander",
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-1.36",
pages = "244--249",
abstract = "This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.",
}
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%0 Conference Proceedings
%T Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach
%A Yigezu, Mesay Gemeda
%A Kanta, Selam
%A Kolesnikova, Olga
%A Sidorov, Grigori
%A Gelbukh, Alexander
%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 yigezu-etal-2023-habesha-dravidianlangtech
%X This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.
%U https://aclanthology.org/2023.dravidianlangtech-1.36
%P 244-249
Markdown (Informal)
[Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach](https://aclanthology.org/2023.dravidianlangtech-1.36) (Yigezu et al., DravidianLangTech-WS 2023)
ACL