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.
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Che, W., Liu, T., & Li, S. (2006). Shallow Semantic Parsing. AMIA Annual Symposium Proceedings AMIA Symposium AMIA Symposium, 604–8. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1839261&tool=pmcentrez&rendertype=abstract
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