We compare the effectiveness of different syntactic features and representations for prosodic boundary prediction, setting out to clarify which representations are most suitable for this task. We took a machine learning approach, and ran a series of eight experiments. The results show that the representations have different strengths and that a combination yields the best result. We also find that linguistically deep representations do not yield clearly superior classifiers compared to classifiers obtained by extraction of shallow features.