Since the earliest work on statistical parsing, a constant theme has been the development of discriminative and generative models with complementary strengths. In this work I’ll give a brief history of discriminative and generative models in statistical parsing, focusing on strengths and weaknesses of the various models. I’ll start with early work on discriminative history-based models (in particular, the SPATTER parser), moving through early discriminative and generative models based on lexicalized (dependency) representations, through to recent work on conditional-random-field based models. Finally, I’ll describe research on semi-supervised approaches that combine discriminative and generative models.