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EACL
2009
ACL Anthology

Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings

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Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings
We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-local features that violate the classical structural independence assumptions. It is inspired by cube pruning (Chiang, 2007; Huang and Chiang, 2007) in its computation of non-local features dynamically using scored k-best lists, but also maintains additional residual quantities used in calculating approximate marginals. When restricted to local features, cube summing reduces to a novel semiring (k-best+residual) that generalizes many of the semirings of Goodman (1999). When non-local features are included, cube summing does not reduce to any semiring, but is compatible with generic techniques for solving dynamic programming equations.
Kevin Gimpel, Noah A. Smith
Added 24 Nov 2009
Updated 24 Nov 2009
Type Conference
Year 2009
Where EACL
Authors Kevin Gimpel, Noah A. Smith
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