POSBIOTM-NER: a trainable biomedical named-entity recognition system.
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.
Song
Yu
Y
Department of CSE, POSTECH Pohang, 790-784, Korea. songyu@postech.ac.kr
Kim
Eunju
E
Lee
Gary Geunbae
GG
Yi
Byoung-Kee
BK
D000465:Algorithms; D001185:Artificial Intelligence; D035843:Biomedical Research / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D012984:Software; D009626:Terminology as Topic; D018875:Vocabulary, Controlled