@inproceedings{shanmugavadivel-etal-2024-mit,
title = "{MIT}-{KEC}-{NLP}@{D}ravidian{L}ang{T}ech-{EACL} 2024: Offensive Content Detection in {K}annada and {K}annada-{E}nglish Mixed Text Using Deep Learning Techniques",
author = "Shanmugavadivel, Kogilavani and
K S, Sowbarnigaa and
M S, Mehal Sakthi and
K, Subhadevi and
Subramanian, Malliga",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.24",
pages = "146--150",
abstract = "This study presents a strong methodology for detecting offensive content in multilingual text, with a focus on Kannada and Kannada-English mixed comments. The first step in data preprocessing is to work with a dataset containing Kannada comments, which is backed by Google Translate for Kannada-English translation. Following tokenization and sequence labeling, BIO tags are assigned to indicate the existence and bounds of objectionable spans within the text. On annotated data, a Bidirectional LSTM neural network model is trained and BiLSTM model{'}s macro F1 score is 61.0 in recognizing objectionable content. Data preparation, model architecture definition, and iterative training with Kannada and Kannada- English text are all part of the training process. In a fresh dataset, the trained model accurately predicts offensive spans, emphasizing comments in the aforementioned languages. Predictions that have been recorded and include offensive span indices are organized into a database.",
}
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%0 Conference Proceedings
%T MIT-KEC-NLP@DravidianLangTech-EACL 2024: Offensive Content Detection in Kannada and Kannada-English Mixed Text Using Deep Learning Techniques
%A Shanmugavadivel, Kogilavani
%A K S, Sowbarnigaa
%A M S, Mehal Sakthi
%A K, Subhadevi
%A Subramanian, Malliga
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shanmugavadivel-etal-2024-mit
%X This study presents a strong methodology for detecting offensive content in multilingual text, with a focus on Kannada and Kannada-English mixed comments. The first step in data preprocessing is to work with a dataset containing Kannada comments, which is backed by Google Translate for Kannada-English translation. Following tokenization and sequence labeling, BIO tags are assigned to indicate the existence and bounds of objectionable spans within the text. On annotated data, a Bidirectional LSTM neural network model is trained and BiLSTM model’s macro F1 score is 61.0 in recognizing objectionable content. Data preparation, model architecture definition, and iterative training with Kannada and Kannada- English text are all part of the training process. In a fresh dataset, the trained model accurately predicts offensive spans, emphasizing comments in the aforementioned languages. Predictions that have been recorded and include offensive span indices are organized into a database.
%U https://aclanthology.org/2024.dravidianlangtech-1.24
%P 146-150
Markdown (Informal)
[MIT-KEC-NLP@DravidianLangTech-EACL 2024: Offensive Content Detection in Kannada and Kannada-English Mixed Text Using Deep Learning Techniques](https://aclanthology.org/2024.dravidianlangtech-1.24) (Shanmugavadivel et al., DravidianLangTech-WS 2024)
ACL