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            <namePart type="given">Bharathi</namePart>
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    <abstract>This paper presents our ensembling solutions for detecting signs of depression in social media text, as part of the Shared Task at LT-EDI@RANLP 2023. By leveraging social media posts in English, the task involves the development of a system to accurately classify them as presenting signs of depression of one of three levels: “severe”, “moderate”, and “not depressed”. We verify the hypothesis that combining contextual information from a language model with local domain-specific features can improve the classifier‘s performance. We do so by evaluating: (1) two global classifiers (support vector machine and logistic regression); (2) contextual information from language models; and (3) the ensembling results.</abstract>
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%0 Conference Proceedings
%T IJS@LT-EDI : Ensemble Approaches to Detect Signs of Depression from Social Media Text
%A Caporusso, Jaya
%A Tran, Thi Hong Hanh
%A Pollak, Senja
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F caporusso-etal-2023-ijs
%X This paper presents our ensembling solutions for detecting signs of depression in social media text, as part of the Shared Task at LT-EDI@RANLP 2023. By leveraging social media posts in English, the task involves the development of a system to accurately classify them as presenting signs of depression of one of three levels: “severe”, “moderate”, and “not depressed”. We verify the hypothesis that combining contextual information from a language model with local domain-specific features can improve the classifier‘s performance. We do so by evaluating: (1) two global classifiers (support vector machine and logistic regression); (2) contextual information from language models; and (3) the ensembling results.
%U https://aclanthology.org/2023.ltedi-1.26/
%P 172-178

Markdown (Informal)

[IJS@LT-EDI : Ensemble Approaches to Detect Signs of Depression from Social Media Text](https://aclanthology.org/2023.ltedi-1.26/) (Caporusso et al., LTEDI 2023)

• IJS@LT-EDI : Ensemble Approaches to Detect Signs of Depression from Social Media Text (Caporusso et al., LTEDI 2023)

ACL

• Jaya Caporusso, Thi Hong Hanh Tran, and Senja Pollak. 2023. IJS@LT-EDI : Ensemble Approaches to Detect Signs of Depression from Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 172–178, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.