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        <title>SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts</title>
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        <namePart type="given">Adarsh</namePart>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="family">Bharathi</namePart>
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            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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            <namePart type="given">Paul</namePart>
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    <abstract>Depression is one of the most common mentalissues faced by people. Detecting signs ofdepression early on can help in the treatmentand prevention of extreme outcomes like suicide. Since the advent of the internet, peoplehave felt more comfortable discussing topicslike depression online due to the anonymityit provides. This shared task has used datascraped from various social media sites andaims to develop models that detect signs andthe severity of depression effectively. In thispaper, we employ transfer learning by applyingenhanced BERT model trained for Wikipediadataset to the social media text and performtext classification. The model gives a F1-scoreof 63.8% which was reasonably better than theother competing models.</abstract>
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%0 Conference Proceedings
%T SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts
%A S, Adarsh
%A Antony, Betina
%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 s-antony-2022-ssn
%X Depression is one of the most common mentalissues faced by people. Detecting signs ofdepression early on can help in the treatmentand prevention of extreme outcomes like suicide. Since the advent of the internet, peoplehave felt more comfortable discussing topicslike depression online due to the anonymityit provides. This shared task has used datascraped from various social media sites andaims to develop models that detect signs andthe severity of depression effectively. In thispaper, we employ transfer learning by applyingenhanced BERT model trained for Wikipediadataset to the social media text and performtext classification. The model gives a F1-scoreof 63.8% which was reasonably better than theother competing models.
%R 10.18653/v1/2022.ltedi-1.50
%U https://aclanthology.org/2022.ltedi-1.50/
%U https://doi.org/10.18653/v1/2022.ltedi-1.50
%P 326-330

Markdown (Informal)

[SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts](https://aclanthology.org/2022.ltedi-1.50/) (S & Antony, LTEDI 2022)

• SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts (S & Antony, LTEDI 2022)

ACL

• Adarsh S and Betina Antony. 2022. SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 326–330, Dublin, Ireland. Association for Computational Linguistics.