As with many other data-driven dependency parsers, we use a global linear model for disambiguation.
a discriminative classifier is utilized to approximate the oracle function for a transition system S that maps a configuration c to a transition t that is defined on c. More formally, a transitionbased statistical parser tries to find the transition sequence c 0,m that maximizes the following score Following the state-of-the-art discriminative disambiguation technique for data-driven parsing, we define the score function as a linear combination of features defined over a configuration and a transition, as follows: where φ defines a vector for each configuration-transition pair and θ is the weight vector for linear combination.