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Error Correction using DOP

Machine translation with Tree-DOP

Unsupervised Parsing with U-DOP

We propose a generalization of the supervised DOP model to unsupervised learning. This new model, which we call U-DOP, initially assigns all possible unlabeled binary trees to a set of sentences and next uses all subtrees from (a large subset of) these binary trees to compute the most probable parse trees. We show how U-DOP can be implemented by a PCFG-reduction technique and report competitive...

Linguistic Constraints in Lfg-dop

LFG-DOP (Bod and Kaplan, 1998, 2003) provides an appealing answer to the question of how probabilistic methods can be incorporated into linguistic theory. However, despite its attractions, the standard model of LFG-DOP suffers from serious problems of overgeneration because (a) it is unable to define fragments of the right level of generality, and (b) it has no way of capturing the effect of an...

Rank Consistent Estimation: The DOP Case

The goal of an estimator is to approximate the unknown distribution of the language from its partial evidence. In this thesis, a rank consistent estimator is defined as an estimator that preserves the ranking frequencies of all the full parse trees in the treebank proved to be rank consistent with respect to the training treebank. The rank consistency property adopts Laplace’s Principle of Insu...