A Provably Correct Learning Algorithm for Latent-Variable PCFGs

Shay Cohen, Michael Collins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
Pages1052-1061
Number of pages10
Publication statusPublished - 2014

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