@inproceedings{agirrezabal-amann-2022-kucst,
title = "{KUCST}@{LT}-{EDI}-{ACL}2022: Detecting Signs of Depression from Social Media Text",
author = "Agirrezabal, Manex and
Amann, Janek",
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-1.35",
doi = "10.18653/v1/2022.ltedi-1.35",
pages = "245--250",
abstract = "In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.",
}
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<abstract>In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.</abstract>
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%0 Conference Proceedings
%T KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text
%A Agirrezabal, Manex
%A Amann, Janek
%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 agirrezabal-amann-2022-kucst
%X In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.
%R 10.18653/v1/2022.ltedi-1.35
%U https://aclanthology.org/2022.ltedi-1.35
%U https://doi.org/10.18653/v1/2022.ltedi-1.35
%P 245-250
Markdown (Informal)
[KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text](https://aclanthology.org/2022.ltedi-1.35) (Agirrezabal & Amann, LTEDI 2022)
ACL