Extracting Time Expressions from Clinical Text

Timothy A. Miller, Steven Bethard, Dmitriy Dligach, Chen Lin, Guergana K. Savova

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

6 Scopus citations

Abstract

Temporal information extraction is important to understanding text in clinical documents. Temporal expression extraction provides explicit grounding of events in a narrative. In this work we provide a direct comparison of various ways of extracting temporal expressions, using similar features as much as possible to explore the advantages of the methods themselves. We evaluate these systems on both the THYME (Temporal History of Your Medical Events) and i2b2 Challenge corpora. Our main findings are that simple sequence taggers outperform conditional random fields on the new data, and higher-level syntactic features do not seem to improve performance.

Original languageEnglish (US)
Title of host publicationACL-IJCNLP 2015 - BioNLP 2015
Subtitle of host publicationWorkshop on Biomedical Natural Language Processing, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages81-91
Number of pages11
ISBN (Electronic)1932432663, 9781932432664
StatePublished - 2015
Externally publishedYes
EventACL-IJCNLP 2015 Workshop on Biomedical Natural Language Processing, BioNLP 2015 - Beijing, China
Duration: Jul 30 2015 → …

Publication series

NameACL-IJCNLP 2015 - BioNLP 2015: Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop

Conference

ConferenceACL-IJCNLP 2015 Workshop on Biomedical Natural Language Processing, BioNLP 2015
Country/TerritoryChina
CityBeijing
Period7/30/15 → …

ASJC Scopus subject areas

  • Health Informatics
  • Language and Linguistics
  • Computer Science Applications
  • Information Systems
  • Software
  • Biomedical Engineering

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