Nbest dependency parsing with linguistically rich models

Abstract

We try to improve the classifier-based deterministic dependency parsing in two ways: by introducing a better search method based on a non-deterministic nbest algorithm and by devising a series of linguistically richer models. It is experimentally shown on a ConLL 2007 shared task that this results in a system with higher performance while still keeping it simple enough for an efficient implementation.