@inproceedings{esackimuthu-etal-2022-ssn,
title = "{SSN}{\_}{MLRG}3 @{LT}-{EDI}-{ACL}2022-Depression Detection System from Social Media Text using Transformer Models",
author = "Esackimuthu, Sarika and
Hariprasad, Shruthi and
Sivanaiah, Rajalakshmi and
S, Angel and
Rajendram, Sakaya Milton and
T T, Mirnalinee",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.26/",
doi = "10.18653/v1/2022.ltedi-1.26",
pages = "196--199",
abstract = "Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely {\textquotedblleft}not depressed{\textquotedblright}, {\textquotedblleft}moderately depressed{\textquotedblright}, and {\textquotedblleft}severely depressed{\textquotedblright}. We have build a system using Deep Learning Model {\textquotedblleft}Transformers{\textquotedblright}. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN{\_}MLRG3 obtained a Macro F1 score of 0.473."
}
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<abstract>Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.</abstract>
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%0 Conference Proceedings
%T SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models
%A Esackimuthu, Sarika
%A Hariprasad, Shruthi
%A Sivanaiah, Rajalakshmi
%A S, Angel
%A Rajendram, Sakaya Milton
%A T T, Mirnalinee
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F esackimuthu-etal-2022-ssn
%X Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.
%R 10.18653/v1/2022.ltedi-1.26
%U https://aclanthology.org/2022.ltedi-1.26/
%U https://doi.org/10.18653/v1/2022.ltedi-1.26
%P 196-199
Markdown (Informal)
[SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models](https://aclanthology.org/2022.ltedi-1.26/) (Esackimuthu et al., LTEDI 2022)
ACL