Cohen, Shay

Collins, Michael

English

We describe an approach to speed-up inference with latent-variable PCFGs, whichhave been shown to be highly effective for natural language parsing. Our approachis based on a tensor formulation recently introduced for spectral estimation oflatent-variable PCFGs coupled with a tensor decomposition algorithm well-knownin the multilinear algebra literature. We also describe an error bound for thisapproximation, which gives guarantees showing that if the underlying tensors arewell approximated, then the probability distribution over trees will also be wellapproximated. Empirical evaluation on real-world natural language parsing datademonstrates a significant speed-up at minimal cost for parsing performance

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Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs

Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs

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