Unsupervised Domain Adaptation for Clinical Negation Detection

Timothy Miller, Steven Bethard, Hadi Amiri, Guergana Savova

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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,
Editors:
Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
165–170
Language:
URL:
https://aclanthology.org/W17-2320
DOI:
10.18653/v1/W17-2320
Bibkey:
Cite (ACL):
Timothy Miller, Steven Bethard, Hadi Amiri, and Guergana Savova. 2017. Unsupervised Domain Adaptation for Clinical Negation Detection. In BioNLP 2017, pages 165–170, Vancouver, Canada,. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Domain Adaptation for Clinical Negation Detection (Miller et al., BioNLP 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W17-2320.pdf
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