We have shown that order-1 semi-Markov conditional random fields are strictly more expressive than order-1 Markov CRFs, and that the added expressivity enables the use of features that lead to improvements on a segmentation task.
markov CRFs can more naturally incorporate certain features that may be useful for modeling sub-chunk phenomena and generalization to unseen chunks.