Unsupervised Domain Adaptation for Clinical Negation Detection

Timothy A. Miller, Steven Bethard, Hadi Amiri, Guergana Savova

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

4 Scopus citations

Abstract

Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.

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)
Pages165-170
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

Fingerprint

Dive into the research topics of 'Unsupervised Domain Adaptation for Clinical Negation Detection'. Together they form a unique fingerprint.

Cite this