@inproceedings{anantharaman-etal-2022-ssn,
title = "{SSN}{\_}{MLRG}1@{LT}-{EDI}-{ACL}2022: Multi-Class Classification using {BERT} models for Detecting Depression Signs from Social Media Text",
author = "Anantharaman, Karun and
S, Angel and
Sivanaiah, Rajalakshmi and
Madhavan, Saritha and
Rajendram, Sakaya Milton",
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-1.44",
doi = "10.18653/v1/2022.ltedi-1.44",
pages = "296--300",
abstract = "DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely {``}not depressed{''},{``}moderately depressed{''}, and {``}severely de-pressed{''}. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN{\_}MLRG1 achieves 58.5{\%}accuracy on the DepSign-LT-EDI@ACL-2022test set.",
}
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<abstract>DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely “not depressed”,“moderately depressed”, and “severely de-pressed”. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN_MLRG1 achieves 58.5%accuracy on the DepSign-LT-EDI@ACL-2022test set.</abstract>
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%0 Conference Proceedings
%T SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text
%A Anantharaman, Karun
%A S, Angel
%A Sivanaiah, Rajalakshmi
%A Madhavan, Saritha
%A Rajendram, Sakaya Milton
%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 anantharaman-etal-2022-ssn
%X DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely “not depressed”,“moderately depressed”, and “severely de-pressed”. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN_MLRG1 achieves 58.5%accuracy on the DepSign-LT-EDI@ACL-2022test set.
%R 10.18653/v1/2022.ltedi-1.44
%U https://aclanthology.org/2022.ltedi-1.44
%U https://doi.org/10.18653/v1/2022.ltedi-1.44
%P 296-300
Markdown (Informal)
[SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text](https://aclanthology.org/2022.ltedi-1.44) (Anantharaman et al., LTEDI 2022)
ACL