Shallow Semantic Parsing of Randomized Controlled Trial Reports

摘要

In this work, we are measuring the performance of Propbank-based Machine Learning (ML) for automatically annotating abstracts of Randomized Controlled Trials (RCTs) 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 abstracts) performance. Although the intra-domain performance is superior, we found a reasonable cross-domain performance.