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ECML
2007
Springer

Discriminative Sequence Labeling by Z-Score Optimization

13 years 9 months ago
Discriminative Sequence Labeling by Z-Score Optimization
Abstract. We consider a new discriminative learning approach to sequence labeling based on the statistical concept of the Z-score. Given a training set of pairs of hidden-observed sequences, the task is to determine some parameter values such that the hidden labels can be correctly reconstructed from observations. Maximizing the Z-score appears to be a very good criterion to solve this problem both theoretically and empirically. We show that the Z-score is a convex function of the parameters and it can be efficiently computed with dynamic programming methods. In addition to that, the maximization step turns out to be solvable by a simple linear system of equations. Experiments on artificial and real data demonstrate that our approach is very competitive both in terms of speed and accuracy with respect to previous algorithms.
Elisa Ricci, Tijl De Bie, Nello Cristianini
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ECML
Authors Elisa Ricci, Tijl De Bie, Nello Cristianini
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