Unsupervised Domain Adaptation for Clinical Negation Detection

Timothy Miller, Steven Bethard, Hadi Amiri, Guergana Savova


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.
Anthology ID:
W17-2320
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
165–170
Language:
URL:
https://www.aclweb.org/anthology/W17-2320
DOI:
10.18653/v1/W17-2320
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2320.pdf