Unsupervised Domain Adaptation for Clinical Negation Detection
Timothy M. Miller, Steven J. Bethard, Hadi Amiri, Guergana Savova · 2017
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.