Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Flexible text segmentation with structured multilabel classification
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Creating probabilistic databases from information extraction models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Webpage understanding: an integrated approach
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic graphical models and their role in databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Foundations and Trends in Databases
Recurrent predictive models for sequence segmentation
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Minimally-supervised extraction of entities from text advertisements
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Estimating accuracy for text classification tasks on large unlabeled data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.