Digital Commons Network^™

Penn/Umass/Chop Biocreative Ii Systems, Kuzman Ganchev, Koby Crammer, Fernando Pereira, Gideon Mann, Kedar Bellare, Andrew Mccallum, Steve Carroll, Yang Jin, Peter White

Andrew McCallum

Our team participated in the entity tagging and normalization tasks of Biocreative II. For the entity tagging task, we used a k-best MIRA learning algorithm with lexicons and automatically derived word clusters. MIRA accommodates different training loss functions, which allowed us to exploit gene alternatives in training. We also performed a greedy search over feature templates and the development data, achieving a final F-measure of 86.28%. For the normalization task, we proposed a new specialized on-line learning algorithm and applied it for filtering out false positives from a high recall list of candidates. For normalization we received an F-measure of …