Comparisons of sequence labeling algorithms and extensions

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Comparisons of sequence labeling algorithms and extensions
In this paper, we survey the current state-ofart models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron (AP), Structured SVMs (SV Mstruct ), Max Margin Markov Networks (M3 N), 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 SVMstruct 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.
Nam Nguyen, Yunsong Guo
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Nam Nguyen, Yunsong Guo
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