Efficient Inference on Sequence Segmentation Models
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.
Cite
Text
Sarawagi. "Efficient Inference on Sequence Segmentation Models." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143944Markdown
[Sarawagi. "Efficient Inference on Sequence Segmentation Models." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/sarawagi2006icml-efficient/) doi:10.1145/1143844.1143944BibTeX
@inproceedings{sarawagi2006icml-efficient,
title = {{Efficient Inference on Sequence Segmentation Models}},
author = {Sarawagi, Sunita},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {793-800},
doi = {10.1145/1143844.1143944},
url = {https://mlanthology.org/icml/2006/sarawagi2006icml-efficient/}
}