Each training instance (c, t) is represented by a feature vector f (c, t) ∈ R k , where features are defined in terms of arbitrary properties of the configuration c, including the state of the stack σ c , the input buffer β c , and the partially built dependency graph G c .
many features involve properties of the two target tokens, the token on top of the stack σ c (σ 0 c ) and the first token in the input buffer β c (β 0 c ), which are the two tokens that may become connected by a dependency arc through the transition out of c. The full set of features used by the base model MaltParser is described in Nivre et al.