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Bioinformatics Advance Access originally published online on April 6, 2005
Bioinformatics 2005 21(11):2794-2796; 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{at}oupjournals.org

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

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

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

*To whom correspondence should be addressed.

Summary: POSBIOTM–NER is a trainable biomedical named-entity recognition system. POSBIOTM–NER can be automatically trained and adapted to new datasets 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, GENE–NER based on BioCreative data and GPCR–NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction.

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

Contact: songyu{at}postech.ac.kr


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