@inproceedings{miller-etal-2026-dataset,
title = "A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations",
author = "Miller, Timothy A. and
Dinh, Gaby and
Harris, David and
Yoon, WonJin and
Thomas, Spencer and
Ren, Boyu and
Hall, Meihua and
Savova, Guergana",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.592/",
doi = "10.63317/4pj6fbqovg3f",
pages = "7479--7484",
abstract = "Temporal information extraction is the task of identifying temporal entities in a text and relating them to each other. In medicine, electronic health records (EHRs) contain text that documents the sequence of events during an encounter with a patient, and sometimes the events prior to the encounter (e.g., social history). Temporality is especially important for the specialty of psychiatry. In this work, we describe the updates to the guidelines that allowed us to create a corpus of temporally-annotated psychiatric discharge summaries and progress notes. These updated guidelines were used to create a corpus of over 18000 events, 2200 time expressions, and 13,000 temporal relations. Temporal information extraction performance with a baseline system trained on non-psychiatric data obtains an F1 score of 0.152 on relation extraction, indicating the importance of this new dataset for making progress on temporal information extraction in the psychiatric domain."
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<abstract>Temporal information extraction is the task of identifying temporal entities in a text and relating them to each other. In medicine, electronic health records (EHRs) contain text that documents the sequence of events during an encounter with a patient, and sometimes the events prior to the encounter (e.g., social history). Temporality is especially important for the specialty of psychiatry. In this work, we describe the updates to the guidelines that allowed us to create a corpus of temporally-annotated psychiatric discharge summaries and progress notes. These updated guidelines were used to create a corpus of over 18000 events, 2200 time expressions, and 13,000 temporal relations. Temporal information extraction performance with a baseline system trained on non-psychiatric data obtains an F1 score of 0.152 on relation extraction, indicating the importance of this new dataset for making progress on temporal information extraction in the psychiatric domain.</abstract>
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%0 Conference Proceedings
%T A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations
%A Miller, Timothy A.
%A Dinh, Gaby
%A Harris, David
%A Yoon, WonJin
%A Thomas, Spencer
%A Ren, Boyu
%A Hall, Meihua
%A Savova, Guergana
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F miller-etal-2026-dataset
%X Temporal information extraction is the task of identifying temporal entities in a text and relating them to each other. In medicine, electronic health records (EHRs) contain text that documents the sequence of events during an encounter with a patient, and sometimes the events prior to the encounter (e.g., social history). Temporality is especially important for the specialty of psychiatry. In this work, we describe the updates to the guidelines that allowed us to create a corpus of temporally-annotated psychiatric discharge summaries and progress notes. These updated guidelines were used to create a corpus of over 18000 events, 2200 time expressions, and 13,000 temporal relations. Temporal information extraction performance with a baseline system trained on non-psychiatric data obtains an F1 score of 0.152 on relation extraction, indicating the importance of this new dataset for making progress on temporal information extraction in the psychiatric domain.
%R 10.63317/4pj6fbqovg3f
%U https://aclanthology.org/2026.lrec-1.592/
%U https://doi.org/10.63317/4pj6fbqovg3f
%P 7479-7484
Markdown (Informal)
[A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations](https://aclanthology.org/2026.lrec-1.592/) (Miller et al., LREC 2026)
ACL
- Timothy A. Miller, Gaby Dinh, David Harris, WonJin Yoon, Spencer Thomas, Boyu Ren, Meihua Hall, and Guergana Savova. 2026. A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7479–7484, Palma de Mallorca, Spain. ELRA Language Resource Association.