Abstract / Description of output
We describe an approach to speed-up inference with latent-variable PCFGs, which
have been shown to be highly effective for natural language parsing. Our approach
is based on a tensor formulation recently introduced for spectral estimation of
latent-variable PCFGs coupled with a tensor decomposition algorithm well-known
in the multilinear algebra literature. We also describe an error bound for this
approximation, which gives guarantees showing that if the underlying tensors are
well approximated, then the probability distribution over trees will also be well
approximated. Empirical evaluation on real-world natural language parsing data
demonstrates a significant speed-up at minimal cost for parsing performance.
have been shown to be highly effective for natural language parsing. Our approach
is based on a tensor formulation recently introduced for spectral estimation of
latent-variable PCFGs coupled with a tensor decomposition algorithm well-known
in the multilinear algebra literature. We also describe an error bound for this
approximation, which gives guarantees showing that if the underlying tensors are
well approximated, then the probability distribution over trees will also be well
approximated. Empirical evaluation on real-world natural language parsing data
demonstrates a significant speed-up at minimal cost for parsing performance.
| Original language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 25 |
| Editors | P. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger |
| Publisher | NIPS Foundation |
| Pages | 2528-2536 |
| Number of pages | 9 |
| Publication status | Published - 2012 |