Defining and learning refined temporal relations in the clinical narrative

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationEMNLP 2020 - 11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages104-114
Number of pages11
ISBN (Electronic)9781952148811
DOIs
StatePublished - 2020
Event11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, co-located with EMNLP 2020 - Virtual, Online
Duration: Nov 20 2020 → …

Publication series

NameEMNLP 2020 - 11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, Proceedings of the Workshop

Conference

Conference11th International Workshop on Health Text Mining and Information Analysis, LOUHI 2020, co-located with EMNLP 2020
CityVirtual, Online
Period11/20/20 → …

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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