A Provably Correct Learning Algorithm for Latent-Variable PCFGs

By Shay B. Cohen, Michael Collins. 2014

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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.

Subfields: Computational Linguistics & Semantics, Statistical Learning Theory