Sarawagi, Sunita (2006) Efficient inference on sequence segmentation models In: 23rd international conference on Machine learning.
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Official URL: http://doi.org/10.1145/1143844.1143944
Related URL: http://dx.doi.org/10.1145/1143844.1143944
Abstract
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.
Item Type: | Conference or Workshop Item (Paper) |
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Source: | Copyright of this article belongs to ACM, Inc |
ID Code: | 128393 |
Deposited On: | 20 Oct 2022 04:38 |
Last Modified: | 14 Nov 2022 11:16 |
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