A provably correct learning algorithm for latent-variable PCFGs

Abstract

We introduce a provably correct learning algorithm for latent-variable PCFGs. The algorithm relies on two steps: first, the use of a matrix-decomposition algorithm applied to a co-occurrence matrix estimated from the parse trees in a training sample; second, the use of EM applied to a convex objective derived from the training samples in combination with the output from the matrix decomposition. Experiments on parsing and a language modeling problem show that the algorithm is efficient and effective in practice. © 2014 Association for Computational Linguistics.