an auxiliary source. Wang et al.  employ this type of node splitting in machine translation and report a small increase in BLEU score.

We use the methods described in [@ZollmannVenugopal:2006; @Venugopal:2007] to induce synchronous grammar rules, a process which requires phrase alignments and syntactic parse trees. Venugopal et al.  use generic non-terminal category symbols, as in [@Chiang:2005], as well as grammatical categories from the Stanford parser [@KleinManning03]. Their method for rule induction generalizes to any set of non-terminals. We further refine this process by adding semantic notations onto the syntactic non-terminals produced by a Penn Treebank trained parser, thus making the categories more informative.

In the parsing domain, the work of Petrov and Klein  is related to the current work. In this work, rule splitting and rule merging are applied to refine parse trees during machine learning. Hierarchical splitting leads to the creation of learned categories that have linguistic relevance, such as a breakdown of a determiner category into two subcategories of determiners by number, i.e., [*[this]{}*]{} and [*[that]{}*]{} group together as do [*[some]{}*]{} and [*[these]{}*]{}. We augment parse trees by category insertion in cases where a semantic category is inserted as a node in a parse tree, after the 