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Bioinformatics Advance Access published online on April 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti414
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received August 9, 2004
Revised March 4, 2005
Accepted March 28, 2005

Applications note

POSBIOTM-NER: a trainable biomedical named-entity recognition system

Yu Song 1*, Eunju Kim 1, Gary Geunbae Lee 1, and Byoung-kee Yi 1

1 Department of CSE, POSTECH, Pohang, 790-784, Korea

* To whom correspondence should be addressed.
Yu Song, E-mail: songyu{at}postech.ac.kr


  Abstract

Summary: POSBIOTM-NER is a trainable biomedical named entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new data sets without performance degradation, using CRF (Conditional Random Field) machine learning techniques and automatic linguistic feature analysis. Currently we have trained our system on three different datasets. GENIA-NER was trained based on GENIA Corpus (Kim et al., 2003), GENE-NER was trained based on BioCreative (Blaschke et al., 2004) data and GPCR-NER was trained based on our own POSBIOTM/NE corpus, which would be used in GPCR-related pathway extraction.

Availability: http://isoft.postech.ac.kr/Research/BioNER/POSBIOTM/NER/main.html.


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