Unsupervised Domain Adaptation for Clinical Negation Detection

摘要

Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generaliz-ability 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.