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This paper presents our contribution to the SemEval-2014 shared task on BroadCoverage 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.

Priberam: A Turbo Semantic Parser with Second Order Features