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,
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W17-2320.pdf