2007 ComparisonsofSequenceLabelingAl

• (Nguyen et al., 2007) ⇒ Nam Nguyen, and Yunsong Guo. (2007). “Comparisons of Sequence Labeling Algorithms and Extensions.” In: Proceedings of the 24th International Conference on Machine learning. doi:10.1145/1273496.1273582

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• http://scholar.google.com/scholar?q=%22Comparisons+of+sequence+labeling+algorithms+and+extensions%22+2007
• http://dl.acm.org/citation.cfm?id=1273496.1273582&preflayout=flat#citedby

Quotes

Abstract

In this paper, we survey the current state-of-art models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron (AP), Structured SVMs (SVM^struct), Max Margin Markov Networks (M³N), and an integration of search and learning algorithm (SEARN). With all due tuning efforts of various parameters of each model, on the data sets we have applied the models to, we found that SVM^struct enjoys better performance compared with the others. In addition, we also propose a new method which we call the Structured Learning Ensemble (SLE) to combine these structured learning models. Empirical results show that our SLE algorithm provides more accurate solutions compared with the best results of the individual models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 ComparisonsofSequenceLabelingAl	Nam Nguyen Yunsong Guo			Comparisons of Sequence Labeling Algorithms and Extensions				10.1145/1273496.1273582