@inproceedings{milintsevich-etal-2024-evaluating,
title = "Evaluating Lexicon Incorporation for Depression Symptom Estimation",
author = {Milintsevich, Kirill and
Dias, Ga{\"e}l and
Sirts, Kairit},
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Bitterman, Danielle",
booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clinicalnlp-1.28",
doi = "10.18653/v1/2024.clinicalnlp-1.28",
pages = "322--328",
abstract = "This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.",
}
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<abstract>This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.</abstract>
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%0 Conference Proceedings
%T Evaluating Lexicon Incorporation for Depression Symptom Estimation
%A Milintsevich, Kirill
%A Dias, Gaël
%A Sirts, Kairit
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F milintsevich-etal-2024-evaluating
%X This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.
%R 10.18653/v1/2024.clinicalnlp-1.28
%U https://aclanthology.org/2024.clinicalnlp-1.28
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.28
%P 322-328
Markdown (Informal)
[Evaluating Lexicon Incorporation for Depression Symptom Estimation](https://aclanthology.org/2024.clinicalnlp-1.28) (Milintsevich et al., ClinicalNLP-WS 2024)
ACL