Semantic Annotation of UMLS using Conditional Random Fields

Shahad Kudama, Rafael Berlanga

2014

Abstract

In this work, we present a first approximation to the semantic annotation of Unified Medical Language System (UMLS®) concept descriptions based on the extraction of relevant linguistic features and its use in conditional random fields (CRF) to classify them at the different semantic groups provided by UMLS. Experiments have been carried out over the whole set of concepts of UMLS (more than 1 million). The precision scores obtained in the global system evaluation are high, between 70% and 80% approximately, depending on the percentage of semantic information provided as input. Regarding results by semantic group, the precision even reaches the 100% in those groups with highest representation in the selected descriptions of UMLS.

Paper Citation

in Harvard Style

Kudama S. and Berlanga R. (2014). Semantic Annotation of UMLS using Conditional Random Fields . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 335-341. DOI: 10.5220/0005131003350341

in Bibtex Style

@conference{kdir14,
author={Shahad Kudama and Rafael Berlanga},
title={Semantic Annotation of UMLS using Conditional Random Fields},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={335-341},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005131003350341},
isbn={978-989-758-048-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Semantic Annotation of UMLS using Conditional Random Fields
SN - 978-989-758-048-2
AU - Kudama S.
AU - Berlanga R.
PY - 2014
SP - 335
EP - 341
DO - 10.5220/0005131003350341