@inproceedings{shanmugavadivel-etal-2024-kec-ai-miracle,
title = "{KEC}{\_}{AI}{\_}{MIRACLE}{\_}{MAKERS}@{LT}-{EDI}-2024: Stress Identification in {D}ravidian Languages using Machine Learning Techniques",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
J, Monika and
S, Monishaa and
B, Rishibalan",
editor = {Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Durairaj, Thenmozhi and
Kov{\'a}cs, Gy{\"o}rgy and
Garc{\'\i}a Cumbreras, Miguel {\'A}ngel},
booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ltedi-1.37",
pages = "277--281",
abstract = "Identifying an individual where he/she is stressed or not stressed is our shared task topic. we have used several machine learning models for identifying the stress. This paper presents our system submission for the task 1 and 2 for both Tamil and Telugu dataset, focusing on us- ing supervised approaches. For Tamil dataset, we got highest accuracy for the Support Vector Machine model with f1-score of 0.98 and for Telugu dataset, we got highest accuracy for Random Forest algorithm with f1-score of 0.99. By using this model, Stress Identification System will be helpful for an individual to improve their mental health in optimistic manner.",
}
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%0 Conference Proceedings
%T KEC_AI_MIRACLE_MAKERS@LT-EDI-2024: Stress Identification in Dravidian Languages using Machine Learning Techniques
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A J, Monika
%A S, Monishaa
%A B, Rishibalan
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shanmugavadivel-etal-2024-kec-ai-miracle
%X Identifying an individual where he/she is stressed or not stressed is our shared task topic. we have used several machine learning models for identifying the stress. This paper presents our system submission for the task 1 and 2 for both Tamil and Telugu dataset, focusing on us- ing supervised approaches. For Tamil dataset, we got highest accuracy for the Support Vector Machine model with f1-score of 0.98 and for Telugu dataset, we got highest accuracy for Random Forest algorithm with f1-score of 0.99. By using this model, Stress Identification System will be helpful for an individual to improve their mental health in optimistic manner.
%U https://aclanthology.org/2024.ltedi-1.37
%P 277-281
Markdown (Informal)
[KEC_AI_MIRACLE_MAKERS@LT-EDI-2024: Stress Identification in Dravidian Languages using Machine Learning Techniques](https://aclanthology.org/2024.ltedi-1.37) (Shanmugavadivel et al., LTEDI-WS 2024)
ACL