TY - GEN
T1 - IIITDWD@TamilNLP-ACL2022
T2 - 2nd Workshop on Speech and Language Technologies for Dravidian Languages, Proceedings of the Workshop, DravidianLangTech 2022
AU - Biradar, Shankar
AU - Saumya, Sunil
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - Identifying abusive content or hate speech in social media text has raised the research community's interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 % accuracy on code-mixed Tamil-English text.
AB - Identifying abusive content or hate speech in social media text has raised the research community's interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, it also leads to identifying abusive content in low-resource regional languages, which is an important research problem in computational linguistics. As part of ACL-2022, organizers of DravidianLangTech@ACL 2022 have released a shared task on abusive category identification in Tamil and Tamil-English code-mixed text to encourage further research on offensive content identification in low-resource Indic languages. This paper presents the working notes for the model submitted by IIITDWD at DravidianLangTech@ACL 2022. Our team competed in Sub-Task B and finished in 9th place among the participating teams. In our proposed approach, we used a pre-trained transformer model such as Indic-bert for feature extraction, and on top of that, SVM classifier is used for stance detection. Further, our model achieved 62 % accuracy on code-mixed Tamil-English text.
UR - https://www.scopus.com/pages/publications/85134591191
UR - https://www.scopus.com/pages/publications/85134591191#tab=citedBy
U2 - 10.18653/v1/2022.dravidianlangtech-1.16
DO - 10.18653/v1/2022.dravidianlangtech-1.16
M3 - Conference contribution
AN - SCOPUS:85134591191
T3 - DravidianLangTech 2022 - 2nd Workshop on Speech and Language Technologies for Dravidian Languages, Proceedings of the Workshop
SP - 100
EP - 104
BT - DravidianLangTech 2022 - 2nd Workshop on Speech and Language Technologies for Dravidian Languages, Proceedings of the Workshop
A2 - Chakravarthi, Bharathi Raja
A2 - Priyadharshini, Ruba
A2 - Madasamy, Anand Kumar
A2 - Krishnamurthy, Parameswari
A2 - Sherly, Elizabeth
A2 - Mahesan, Sinnathamby
PB - Association for Computational Linguistics (ACL)
Y2 - 26 May 2022
ER -