@inproceedings{roll-etal-2026-team,
title = "Team Aurevia at {CLP}sych 2026: Local Healthcare {NLP} for Schema-Constrained Self-State Modeling",
author = "Roll, Nathan and
Yi, Irene and
Aldogom, Sufian and
Brown, Grace and
Basile, Eric and
Gutterman, Isaac and
Tennakoon, Lakshika and
Ahmed, Ammar",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.43/",
pages = "531--534",
ISBN = "979-8-89176-421-7",
abstract = "Team Aurevia introduces a local open-weight healthcare NLP system for the CLPsych 2026 Shared Task, predicting MIND-coded self-state elements, moments of change, summaries, anddynamic signatures from social media timelines. The task is difficult because coarse presence, fine-grained ABCD subelements, and timeline-level change require different longitudinal evidence over privacy-sensitive mental-health language. Our system combines TF-IDF retrieval, schema-constrained local Qwen2.5 prompting, ordinal calibration, and conservative post-processing. Among official runs, Aurevia ranked 3rd of 17 for Task 1.2 presence prediction, 5th of 13 overall for Task 3.1, 1st on Task 3.1 consistency, and 2nd of 9 for MIND-coded deterioration signatures, showing that constrained local LLM pipelines can remain competitive in sensitive healthcare NLP while reducing reliance on hosted proprietary inference."
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<abstract>Team Aurevia introduces a local open-weight healthcare NLP system for the CLPsych 2026 Shared Task, predicting MIND-coded self-state elements, moments of change, summaries, anddynamic signatures from social media timelines. The task is difficult because coarse presence, fine-grained ABCD subelements, and timeline-level change require different longitudinal evidence over privacy-sensitive mental-health language. Our system combines TF-IDF retrieval, schema-constrained local Qwen2.5 prompting, ordinal calibration, and conservative post-processing. Among official runs, Aurevia ranked 3rd of 17 for Task 1.2 presence prediction, 5th of 13 overall for Task 3.1, 1st on Task 3.1 consistency, and 2nd of 9 for MIND-coded deterioration signatures, showing that constrained local LLM pipelines can remain competitive in sensitive healthcare NLP while reducing reliance on hosted proprietary inference.</abstract>
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%0 Conference Proceedings
%T Team Aurevia at CLPsych 2026: Local Healthcare NLP for Schema-Constrained Self-State Modeling
%A Roll, Nathan
%A Yi, Irene
%A Aldogom, Sufian
%A Brown, Grace
%A Basile, Eric
%A Gutterman, Isaac
%A Tennakoon, Lakshika
%A Ahmed, Ammar
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F roll-etal-2026-team
%X Team Aurevia introduces a local open-weight healthcare NLP system for the CLPsych 2026 Shared Task, predicting MIND-coded self-state elements, moments of change, summaries, anddynamic signatures from social media timelines. The task is difficult because coarse presence, fine-grained ABCD subelements, and timeline-level change require different longitudinal evidence over privacy-sensitive mental-health language. Our system combines TF-IDF retrieval, schema-constrained local Qwen2.5 prompting, ordinal calibration, and conservative post-processing. Among official runs, Aurevia ranked 3rd of 17 for Task 1.2 presence prediction, 5th of 13 overall for Task 3.1, 1st on Task 3.1 consistency, and 2nd of 9 for MIND-coded deterioration signatures, showing that constrained local LLM pipelines can remain competitive in sensitive healthcare NLP while reducing reliance on hosted proprietary inference.
%U https://aclanthology.org/2026.clpsych-1.43/
%P 531-534
Markdown (Informal)
[Team Aurevia at CLPsych 2026: Local Healthcare NLP for Schema-Constrained Self-State Modeling](https://aclanthology.org/2026.clpsych-1.43/) (Roll et al., CLPsych 2026)
ACL
- Nathan Roll, Irene Yi, Sufian Aldogom, Grace Brown, Eric Basile, Isaac Gutterman, Lakshika Tennakoon, and Ammar Ahmed. 2026. Team Aurevia at CLPsych 2026: Local Healthcare NLP for Schema-Constrained Self-State Modeling. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 531–534, San Diego, California, USA. Association for Computational Linguistics.