Unsupervised Domain Adaptation for Clinical Negation Detection

Citation:

Miller T, Bethard S, Amiri H, Savova G. Unsupervised Domain Adaptation for Clinical Negation Detection, in BioNLP 2017. Vancouver, Canada, Association for Computational Linguistics ; 2017 :165–170.

Date Presented:

August

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.

Website