Efficient graphical models for sequence segmentation
نویسنده
چکیده
Segmentation of sequences is an important modeling primitive with several applications. Training and inference of segmentation models involves dynamic programming computations that in the worst case can be cubic in the length of a sequence. In contrast, typical sequence labeling models require linear time. We propose an alternative graphical model for efficient sharing of potentials across overlapping segments. We then design message passing algorithms that are significantly faster than the original cubic algorithms. When segmentation models are posed as large margin structured classification tasks, our algorithm directly impact the computation of marginals for exponentiated gradient training algorithms [1] and modes for cutting plane algorithms [7].
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