a set of guidelines and then merged with the syntactic trees automatically. In our work we tagged our corpus with entities, modality, and negation automatically and then grafted them onto the syntactic trees automatically, for the purpose of training a statistical machine translation system. An added benefit of the extracted translation rules is that they are capable of producing semantically-tagged Urdu parses, despite the fact that the training data were processed by only an English parser and tagger.

Related work in syntax-based MT includes that of Huang and Knight , where a series of syntax rules are applied to a source language string to produce a target language phrase structure tree. The Penn English Treebank [@Marcus:93] is used as the source for the syntactic labels and syntax trees are relabeled to improve translation quality. In this work, node-internal and node-external information is used to relabel nodes, similar to earlier work where structural context was used to relabel nodes in the parsing domain [@KleinManning03]. Klein and Manning’s methods include lexicalizing determiners and percent markers, making more fine-grained VP categories, and marking the properties of sister nodes on nodes. All of these labels are derivable from the trees themselves and not from 