Priberam: A Turbo Semantic Parser with Second Order Features
André F. T. Martins, Mariana S. C. Almeida · 2014
This paper presents our contribution to the SemEval-2014 shared task on Broad-Coverage Semantic Dependency Parsing.We employ a feature-rich linear model, including scores for first and second-order dependencies (arcs, siblings, grandparents and co-parents).Decoding is performed in a global manner by solving a linear relaxation with alternating directions dual decomposition (AD 3 ).Our system achieved the top score in the open challenge, and the second highest score in the closed track.