@inproceedings{rajalakshmi-etal-2022-dlrg-tamilnlp,
title = "{DLRG}@{T}amil{NLP}-{ACL}2022: Offensive Span Identification in {T}amil using{B}i{LSTM}-{CRF} approach",
author = "Rajalakshmi, Ratnavel and
More, Mohit and
Shrikriti, Bhamatipati and
Saharan, Gitansh and
Samyuktha, Hanchate and
Nandy, Sayantan",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.38",
doi = "10.18653/v1/2022.dravidianlangtech-1.38",
pages = "248--253",
abstract = "Identifying offensive speech is an exciting andessential area of research, with ample tractionin recent times. This paper presents our sys-tem submission to the subtask 1, focusing onusing supervised approaches for extracting Of-fensive spans from code-mixed Tamil-Englishcomments. To identify offensive spans, wedeveloped the Bidirectional Long Short-TermMemory (BiLSTM) model with Glove Em-bedding. To this end, the developed systemachieved an overall F1 of 0.1728. Addition-ally, for comments with less than 30 characters,the developed system shows an F1 of 0.3890,competitive with other submissions.",
}
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<abstract>Identifying offensive speech is an exciting andessential area of research, with ample tractionin recent times. This paper presents our sys-tem submission to the subtask 1, focusing onusing supervised approaches for extracting Of-fensive spans from code-mixed Tamil-Englishcomments. To identify offensive spans, wedeveloped the Bidirectional Long Short-TermMemory (BiLSTM) model with Glove Em-bedding. To this end, the developed systemachieved an overall F1 of 0.1728. Addition-ally, for comments with less than 30 characters,the developed system shows an F1 of 0.3890,competitive with other submissions.</abstract>
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%0 Conference Proceedings
%T DLRG@TamilNLP-ACL2022: Offensive Span Identification in Tamil usingBiLSTM-CRF approach
%A Rajalakshmi, Ratnavel
%A More, Mohit
%A Shrikriti, Bhamatipati
%A Saharan, Gitansh
%A Samyuktha, Hanchate
%A Nandy, Sayantan
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F rajalakshmi-etal-2022-dlrg-tamilnlp
%X Identifying offensive speech is an exciting andessential area of research, with ample tractionin recent times. This paper presents our sys-tem submission to the subtask 1, focusing onusing supervised approaches for extracting Of-fensive spans from code-mixed Tamil-Englishcomments. To identify offensive spans, wedeveloped the Bidirectional Long Short-TermMemory (BiLSTM) model with Glove Em-bedding. To this end, the developed systemachieved an overall F1 of 0.1728. Addition-ally, for comments with less than 30 characters,the developed system shows an F1 of 0.3890,competitive with other submissions.
%R 10.18653/v1/2022.dravidianlangtech-1.38
%U https://aclanthology.org/2022.dravidianlangtech-1.38
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.38
%P 248-253
Markdown (Informal)
[DLRG@TamilNLP-ACL2022: Offensive Span Identification in Tamil usingBiLSTM-CRF approach](https://aclanthology.org/2022.dravidianlangtech-1.38) (Rajalakshmi et al., DravidianLangTech 2022)
ACL