Many recent statistical parsers rely on a preprocessing step which uses hand-written, corpus-specific rules to augment the training data with extra information. For example, head-finding rules are used to augment node labels with lexical heads. In this paper, we provide machinery to reduce the amount of human effort needed to adapt existing models to new corpora: first, we propose a flexible notation for specifying these rules that would allow them to be shared by different models; second, we report on an experiment to see whether we can use Expectation-Maximization to automatically fine-tune a set of hand-written rules to a particular corpus.