This is possible because both models are global and linear.
the transition-based model can be written as: If we take T ∈act(y) Φ(T , s T ) as the global feature vector Φ T (y), we have: which has the same form as the graph-based model:  We therefore combine the two models to give: Concatenating the feature vectors Φ G (y) and Φ T (y) to give a global feature vector Φ C (y), and the weight vectors w G and w T to give a weight vector w C , the combined model can be written as: which is a linear model with exactly the same form as both sub-models, and can be trained with the perceptron algorithm in Figure 1.