TY - GEN
T1 - Defining and learning refined temporal relations in the clinical narrative
AU - Lin, Chen
AU - Wright-Bettner, Kristin
AU - Miller, Timothy
AU - Bethard, Steven
AU - Dligach, Dmitriy
AU - Palmer, Martha
AU - Martin, James H.
AU - Savova, Guergana
N1 - Funding Information:
The work was supported by funding from the United States National Institutes of Health --R01LM010090 from the National Library of Medicine and U24CA248010 and UG3CA243120 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2020 Association for Computational Linguistics
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85107881963&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107881963&partnerID=8YFLogxK
U2 - 10.18653/v1/2020.louhi-1.12
DO - 10.18653/v1/2020.louhi-1.12
M3 - Conference contribution
AN - SCOPUS:85107881963
T3 - EMNLP 2020 - 11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, Proceedings of the Workshop
SP - 104
EP - 114
BT - EMNLP 2020 - 11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, co-located with EMNLP 2020
Y2 - 20 November 2020
ER -