Efficient inference on sequence segmentation models

Sequence segmentation is a flexible and highly accurate mechanism for modeling several applications. Inference on 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 remove this limitation of segmentation models vis-a-vis sequential models by designing a succinct representation of potentials common across overlapping segments. We exploit such potentials to design efficient inference algorithms that are both analytically shown to have a lower complexity and empirically found to be comparable to sequential models for typical extraction tasks.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Sunita Sarawagi
• Keywords: ICML 2006, Machine Learning, Segmentation Models, Sequence Labeling Models, Vis-a-vis Sequential Models