Shallow Semantic Parsing

  • Che W
  • Liu T
  • Li S
ISSN: 1942597X
N/ACitations
Citations of this article
7Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free