Transition-based spinal parsing

Abstract

We present a transition-based arc-eager model to parse spinal trees, a dependency-based representation that includes phrase-structure information in the form of constituent spines assigned to tokens. As a main advantage, the arc-eager model can use a rich set of features combining dependency and constituent information, while parsing in linear time. We describe a set of conditions for the arc-eager system to produce valid spinal structures. In experiments using beam search we show that the model obtains a good trade-off between speed and accuracy, and yields state of the art performance for both dependency and constituent parsing measures.