Much of the recent work on dependency parsing has been focused on solving inherent combinatorial problems associated with rich scoring functions .

In contrast, we demonstrate that highly expressive scoring functions can be used with substantially simpler inference procedures.

Dependency parsing is commonly cast as a maximization problem over a parameterized scoring function .

In this view, the use of more expressive scoring functions leads to more challenging combinatorial problems of finding the maximizing parse.

We depart from this view and instead focus on using highly expressive scoring functions with substantially simpler inference procedures.

The predictor CHEW“ returned by our boosting algorithm is based on a scoring function h: X x y —> R, which, as for standard ensemble algorithms such as AdaBoost,Ai/s a~convex combination of base scoring functions ht: h 2 23:1 atht, with at 2 0.

The base scoring functions used in our algorithm have the form

Thus, the~score assigned to y by the base scoring function ht is the number of positions at which y matches the prediction of path expert ht given input X. CHEW“ is defined as follows in terms of h or hts:

A collection of distributions 1P can also be used to define a deterministic prediction rule based on the scoring function approach.

The majority vote scoring function is defined by

Each factor F has an associated scoring function W, with the probability of a total assignment determined by the product of all these scores:

We score each edge by extracting a set of features f (55¢, :33) and weighting them by the (learned) weight vector w. So, the factor scoring function is:

The scoring function is similar to the one above: