Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks

Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova

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

25 Scopus citations

Abstract

Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.

Original languageEnglish (US)
Title of host publicationBioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages322-327
Number of pages6
ISBN (Electronic)9781945626593
StatePublished - 2017
Externally publishedYes
Event16th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2017 - Vancouver, Canada
Duration: Aug 4 2017 → …

Publication series

NameBioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop

Conference

Conference16th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2017
Country/TerritoryCanada
CityVancouver
Period8/4/17 → …

ASJC Scopus subject areas

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

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