varying 6 β and optimizing the relevant measure for F β ; the points labeled "baseline" show the precision and recall in token and entity level of the baseline model, learned by VP-HMM.
learning methods such as VP-HMM and CRFs optimize criteria such as margin separation (implicitly maximized by VP-HMMs) or log-likelihood (explicitly maximized by CRFs), which are at best indirectly related to precision and recall.