We show that generation of contextually appropriate syntactic variation can be improved using a model based on automatically extracted features. We adapt a model for predicting dative alternation from (Bresnan et al. 2005); this model incorporates lexical, syntactic, semantic and pragmatic features. We evaluate the effect of using different types of feature on this classification task and show that the most predictive features for text differ from those for dialog. Finally, we show that modeling this type of syntactic variation improves the performance of surface realization for both text and dialog.