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Efficient inference on sequence segmentation models

Published: 25 June 2006 Publication History

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.

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Cited By

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  • (2019)Sequence Segmentation Using Semi-Markov Conditional Random FieldsJournal of the Indian Institute of Science10.1007/s41745-019-0100-1Online publication date: 20-Mar-2019
  • (2012)Managing data quality by identifying the noisiest data samplesProceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics10.1109/SOLI.2012.6273510(90-95)Online publication date: Jul-2012
  • (2010)Static analysis of binary executables using structural SVMsProceedings of the 24th International Conference on Neural Information Processing Systems - Volume 110.5555/2997189.2997308(1063-1071)Online publication date: 6-Dec-2010
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cover image ACM Other conferences
ICML '06: Proceedings of the 23rd international conference on Machine learning
June 2006
1154 pages
ISBN:1595933832
DOI:10.1145/1143844
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;
Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

View all
  • (2019)Sequence Segmentation Using Semi-Markov Conditional Random FieldsJournal of the Indian Institute of Science10.1007/s41745-019-0100-1Online publication date: 20-Mar-2019
  • (2012)Managing data quality by identifying the noisiest data samplesProceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics10.1109/SOLI.2012.6273510(90-95)Online publication date: Jul-2012
  • (2010)Static analysis of binary executables using structural SVMsProceedings of the 24th International Conference on Neural Information Processing Systems - Volume 110.5555/2997189.2997308(1063-1071)Online publication date: 6-Dec-2010
  • (2010)Minimally-supervised extraction of entities from text advertisementsHuman Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics10.5555/1857999.1858008(73-81)Online publication date: 2-Jun-2010
  • (2010)Estimating accuracy for text classification tasks on large unlabeled dataProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871551(889-898)Online publication date: 26-Oct-2010
  • (2008)Information ExtractionFoundations and Trends in Databases10.1561/19000000031:3(261-377)Online publication date: 1-Mar-2008
  • (2007)Recurrent predictive models for sequence segmentationProceedings of the 7th international conference on Intelligent data analysis10.5555/1771622.1771647(195-206)Online publication date: 6-Sep-2007
  • (2007)Probabilistic graphical models and their role in databasesProceedings of the 33rd international conference on Very large data bases10.5555/1325851.1326038(1435-1436)Online publication date: 23-Sep-2007
  • (2007)Webpage understandingProceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1281192.1281288(903-912)Online publication date: 12-Aug-2007
  • (2007)Recurrent Predictive Models for Sequence SegmentationAdvances in Intelligent Data Analysis VII10.1007/978-3-540-74825-0_18(195-206)Online publication date: 2007
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