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Semantic Similarity Models for Depression Severity Estimation

Pérez, Anxo ; Warikoo, Neha ; Wang, Kexin ; Parapar, Javier ; Gurevych, Iryna (2023)
Semantic Similarity Models for Depression Severity Estimation.
2023 Conference on Empirical Methods in Natural Language Processing. Singapore (06.-10.12.2023)
doi: 10.18653/v1/2023.emnlp-main.1000
Conference or Workshop Item, Bibliographie

Abstract

Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting symptoms severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI-II) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving improvement over state of the art in terms of measuring depression level.

Item Type: Conference or Workshop Item
Erschienen: 2023
Creators: Pérez, Anxo ; Warikoo, Neha ; Wang, Kexin ; Parapar, Javier ; Gurevych, Iryna
Type of entry: Bibliographie
Title: Semantic Similarity Models for Depression Severity Estimation
Language: English
Date: December 2023
Place of Publication: Singapore
Publisher: ACL
Book Title: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Event Title: 2023 Conference on Empirical Methods in Natural Language Processing
Event Location: Singapore
Event Dates: 06.-10.12.2023
DOI: 10.18653/v1/2023.emnlp-main.1000
URL / URN: https://aclanthology.org/2023.emnlp-main.1000/
Abstract:

Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting symptoms severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI-II) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving improvement over state of the art in terms of measuring depression level.

Uncontrolled Keywords: UKP_p_LOEWE_Spitzenprofessur
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 18 Jan 2024 13:42
Last Modified: 15 Mar 2024 10:37
PPN: 516304933
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