title: Shallow semantic parsing of randomized controlled trial reports.
abstract: 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.
authors.affiliation: Center for Medical Informatics, Yale University School of Medicine, New Haven, USA.
authors.forename: Hyung
authors.initials: H
authors.lastname: Paek
authors.forename: Yacov
authors.initials: Y
authors.lastname: Kogan
authors.forename: Prem
authors.initials: P
authors.lastname: Thomas
authors.forename: Seymour
authors.initials: S
authors.lastname: Codish
authors.forename: Michael
authors.initials: M
authors.lastname: Krauthammer
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D016032:Randomized Controlled Trials as Topic; D012660:Semantics