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In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (2005), pp. 281-290, doi:10.3115/1220575.1220611 Key: citeulike:5243611
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Weighted deduction with aggregation is a powerful theoretical formalism that encompasses many NLP algorithms. This paper proposes a declarative specification language, Dyna; gives general agenda-based algorithms for computing weights and gradients; briefly discusses Dyna-to-Dyna program transformations; and shows that a first implementation of a Dyna-to-C++ compiler produces code that is efficient enough for real NLP research, though still several times slower than hand-crafted code.
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