Defining and Learning Refined Temporal Relations in the Clinical Narrative

Kristin Wright-Bettner, Chen Lin, Timothy Miller, Steven Bethard, Dmitriy Dligach, Martha Palmer, James H. Martin, Guergana Savova


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.
Anthology ID:
2020.louhi-1.12
Volume:
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–114
Language:
URL:
https://aclanthology.org/2020.louhi-1.12
DOI:
10.18653/v1/2020.louhi-1.12
Bibkey:
Cite (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.
Cite (Informal):
Defining and Learning Refined Temporal Relations in the Clinical Narrative (Wright-Bettner et al., Louhi 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.louhi-1.12.pdf
Video:
 https://slideslive.com/38940047