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Probabilistic CFG with Latent Annotations (PCFG-LA)

Matsuzaki, Miyao, & Tsujii
examine the possibility of automating the task of choosing an optimal
problem decomposition. They restrict their attention to the class of
problem decompositions that is formed by augmenting PCFG nodes with
discrete feature values (or *latent annotations*). These
decompositions effectively transform the canonical parse tree by
subdividing the existing phrase types (NP, PP, etc) into sub-types.

This transformation differs from most of the transformations discussed so far in that it does not define a one-to-one mapping between canonical values and transformed values. In particular, if discrete feature values are used to augment canonical trees, then a canonical tree with nonterminal nodes corresponds to different augmented trees (one for each possible assignment of feature values to nodes). As a result, applying standard parsing algorithms to the augmented PCFG will generate the most likely annotated tree; but this does not necessarily correspond to the most likely unannotated (canonical) tree. Matsuzaki, Miyao, & Tsujii therefore explore the use of three different variants on the CKY parsing algorithm which approximate the search for the best unannotated tree.

Starting with a PCFG grammar and a fixed set of feature values, Matsuzaki, Miyao, & Tsujii apply the Expectation Maximization algorithm to iteratively improve upon the PCFG's transition probabilities. As a result, the PCFG automatically learns to make use of the feature values in such a way that the likelihood of the training corpus is maximized. Using their approximate-best parsing algorithms on the PCFG generated by EM, Matsuzaki, Miyao, & Tsujii's parser achieves performance comparable to unlexicalized parsers that make use of hand-crafted problem decompositions.