Abstract
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 (AD3). Our system achieved the top score in the open challenge, and the second highest score in the closed track.
Cite
CITATION STYLE
Martins, A. F. T., & Almeida, M. S. C. (2014). Priberam: A Turbo Semantic Parser with Second Order Features. In 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 471–476). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/s14-2082
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