title: POSBIOTM-NER: a trainable biomedical named-entity recognition system.
abstract: 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.
authors.affiliation: Department of CSE, POSTECH Pohang, 790-784, Korea. songyu@postech.ac.kr
authors.forename: Yu
authors.initials: Y
authors.lastname: Song
authors.forename: Eunju
authors.initials: E
authors.lastname: Kim
authors.forename: Gary Geunbae
authors.initials: GG
authors.lastname: Lee
authors.forename: Byoung-Kee
authors.initials: BK
authors.lastname: Yi
mesh_terms: 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