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input string with respect to a
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dependency transduction
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model . A method for automatically
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grammars are equivalent to the
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dependency transduction
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model from ( Al - shawi et al.
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of a derivation produced by a
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dependency transduction
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model is the sum of all the weights
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method for automatically training a
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dependency transduction
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model from a set of input-output
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To carry out translation with a
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dependency transduction
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model , we apply a dynamic programming
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Introduction We will define a
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model in terms of a collection
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source and target languages . The
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dependency transduction
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model produces synchronized dependency
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translation . One is a statistical
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dependency transduction
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model using head transducers
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fully - lexicalized statistical
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dependency transduction
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models have been trained from
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constrain these . However , for a
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dependency transduction
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model to be a statistical model
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hierarchical alignments used to build the
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dependency transduction
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model . In the experiments comparing
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fully lexicalized statistical
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dependency transduction
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models from training examples
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dependency trees derived by a
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dependency transduction
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model are ordered , i.e. , there
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we estimate the parameters of a
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dependency transduction
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model from such alignments .
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recognizer . Section 3 defines
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dependency transduction
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models and describes the search
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Alignments As noted earlier ,
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dependency transduction
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models are generative probabilistic
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pair of local trees produced by a
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dependency transduction
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derivation is the result of a
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method of training statistical
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dependency transduction
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models in experiments on English-to-Spanish
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Figure 4 . Head transducers and
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dependency transduction
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models are thus related as fol
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Transducers In this section we describe
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dependency transduction
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models , which can be used for
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