Shallow semantic parsing of randomized controlled trial reports. Paek, H., Kogan, Y., Thomas, P., Codish, S., & Krauthammer, M. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2006. abstract bibtex 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.
@article{Paek2006,
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.},
author = {Paek, Hyung and Kogan, Yacov and Thomas, Prem and Codish, Seymour and Krauthammer, Michael},
issn = {1942-597X (Electronic)},
journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},
keywords = {Abstracting and Indexing as Topic,Algorithms,Artificial Intelligence,Randomized Controlled Trials as Topic,Semantics},
language = {eng},
pages = {604--608},
pmid = {17238412},
title = {{Shallow semantic parsing of randomized controlled trial reports.}},
year = {2006}
}
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