Using a semi-Markov CRF, we model the conditional distribution over all possible opinion segmentations given the input x: where θ denotes the model parameters, y and f denotes a feature function that encodes the potentials of the boundaries for opinion segments and the potentials of transitions between two consecutive labeled segments.
semi-Markov CRFs (Sarawagi and Cohen, 2004) (henceforth semi-CRF) have been shown more appropriate for the task than CRFs since they allow contiguous spans in the input sequence (e.g.