Shallow semantic parsing of randomized controlled trial reports.
In this work, we are measuring the performance of Propbank-based Machine Learning (ML) for automatically annotating abstracts of Randomized Controlled Trials (CTRs) with semantically meaningful tags. Propbank is a resource of annotated sentences from the Wall Street Journal (WSJ) corpus, and we were interested in assessing performance issues when porting this resource to the medical domain. We compare intra-domain (WSJ/WSJ) with cross-domain (WSJ/medical abstract) performance. Although the intra-domain performance is superior, we found a reasonable cross-domain performance.
Paek
Hyung
H
Center for Medical Informatics, Yale University School of Medicine, New Haven, USA.
Kogan
Yacov
Y
Thomas
Prem
P
Codish
Seymour
S
Krauthammer
Michael
M
D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D016032:Randomized Controlled Trials as Topic; D012660:Semantics