Comunicació presentada a la 19th Conference on Computational Language Learning, celebrada els dies 30 i 31 de juliol 2015 a Beijing (China) i organitzada per l'ACL Special Interest Group on Natural Language Learning.

We present a transition-based arc-eager model to parse spinal trees, a dependencybased representation that includes phrasestructure 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.

Miguel Ballesteros is supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA).