@inproceedings{mai-etal-2026-hope,
title = "{HOPE}: Hybrid Optimized Parallel Encoding with Supervised and Unsupervised Semantic Fusion for Depression Symptom Detection",
author = "Mai, Tu-Phuong and
H., Minh-Ha Le and
Tran, Duc-Luong and
Chu, Phuong-Anh and
Can, Duy-Cat and
Le, Hoang-Quynh",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.132/",
pages = "2898--2911",
ISBN = "979-8-89176-390-6",
abstract = "Timely detection of depression symptoms is essential for early intervention, and the continuous stream of user-generated content on social media provides an ideal source for this purpose. To address this challenge, we propose **HOPE**, a **H**ybrid **O**ptimized **P**arallel **E**ncoding framework that combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering. This parallel design enables robust symptom detection under limited labeled data and introduces a distinctive semantic-similarity perspective with automatic class-anchor adjustment. We also propose an optimized hybrid semantic fusion mechanism to combine supervised and unsupervised scores through a learnable module. We evaluate our system on multiple benchmark datasets and surpass previous approaches, demonstrating its effectiveness in detecting fine-grained symptoms and early warning of mental health risk. Source code is available at https://github.com/candleMind/hope."
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<abstract>Timely detection of depression symptoms is essential for early intervention, and the continuous stream of user-generated content on social media provides an ideal source for this purpose. To address this challenge, we propose **HOPE**, a **H**ybrid **O**ptimized **P**arallel **E**ncoding framework that combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering. This parallel design enables robust symptom detection under limited labeled data and introduces a distinctive semantic-similarity perspective with automatic class-anchor adjustment. We also propose an optimized hybrid semantic fusion mechanism to combine supervised and unsupervised scores through a learnable module. We evaluate our system on multiple benchmark datasets and surpass previous approaches, demonstrating its effectiveness in detecting fine-grained symptoms and early warning of mental health risk. Source code is available at https://github.com/candleMind/hope.</abstract>
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%0 Conference Proceedings
%T HOPE: Hybrid Optimized Parallel Encoding with Supervised and Unsupervised Semantic Fusion for Depression Symptom Detection
%A Mai, Tu-Phuong
%A H., Minh-Ha Le
%A Tran, Duc-Luong
%A Chu, Phuong-Anh
%A Can, Duy-Cat
%A Le, Hoang-Quynh
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F mai-etal-2026-hope
%X Timely detection of depression symptoms is essential for early intervention, and the continuous stream of user-generated content on social media provides an ideal source for this purpose. To address this challenge, we propose **HOPE**, a **H**ybrid **O**ptimized **P**arallel **E**ncoding framework that combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering. This parallel design enables robust symptom detection under limited labeled data and introduces a distinctive semantic-similarity perspective with automatic class-anchor adjustment. We also propose an optimized hybrid semantic fusion mechanism to combine supervised and unsupervised scores through a learnable module. We evaluate our system on multiple benchmark datasets and surpass previous approaches, demonstrating its effectiveness in detecting fine-grained symptoms and early warning of mental health risk. Source code is available at https://github.com/candleMind/hope.
%U https://aclanthology.org/2026.acl-long.132/
%P 2898-2911
Markdown (Informal)
[HOPE: Hybrid Optimized Parallel Encoding with Supervised and Unsupervised Semantic Fusion for Depression Symptom Detection](https://aclanthology.org/2026.acl-long.132/) (Mai et al., ACL 2026)
ACL