@inproceedings{wright-bettner-etal-2020-defining,
title = "Defining and Learning Refined Temporal Relations in the Clinical Narrative",
author = "Wright-Bettner, Kristin and
Lin, Chen and
Miller, Timothy and
Bethard, Steven and
Dligach, Dmitriy and
Palmer, Martha and
Martin, James H. and
Savova, Guergana",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.louhi-1.12",
doi = "10.18653/v1/2020.louhi-1.12",
pages = "104--114",
abstract = "We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.",
}
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<abstract>We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.</abstract>
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%0 Conference Proceedings
%T Defining and Learning Refined Temporal Relations in the Clinical Narrative
%A Wright-Bettner, Kristin
%A Lin, Chen
%A Miller, Timothy
%A Bethard, Steven
%A Dligach, Dmitriy
%A Palmer, Martha
%A Martin, James H.
%A Savova, Guergana
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wright-bettner-etal-2020-defining
%X We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.
%R 10.18653/v1/2020.louhi-1.12
%U https://aclanthology.org/2020.louhi-1.12
%U https://doi.org/10.18653/v1/2020.louhi-1.12
%P 104-114
Markdown (Informal)
[Defining and Learning Refined Temporal Relations in the Clinical Narrative](https://aclanthology.org/2020.louhi-1.12) (Wright-Bettner et al., Louhi 2020)
ACL
- Kristin Wright-Bettner, Chen Lin, Timothy Miller, Steven Bethard, Dmitriy Dligach, Martha Palmer, James H. Martin, and Guergana Savova. 2020. Defining and Learning Refined Temporal Relations in the Clinical Narrative. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 104–114, Online. Association for Computational Linguistics.