  Unlike widely used Named Entity Recognition (NER) data sets in generic
domains, biomedical NER data sets often contain mentions consisting of
discontinuous spans. Conventional sequence tagging techniques encode Markov
assumptions that are efficient but preclude recovery of these mentions. We
propose a simple, effective transition-based model with generic neural encoding
for discontinuous NER. Through extensive experiments on three biomedical data
sets, we show that our model can effectively recognize discontinuous mentions
without sacrificing the accuracy on continuous mentions.
