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Discriminative Sequence Labeling by Z-Score Optimization

Elisa Ricci, Tijl De Bie, Nello Cristianini, Discriminative Sequence Labeling by Z-Score Optimization. ECML 2007. ISBN 978-3-540-74957-8, pp. 274–285. September 2007. PDF, 515 Kbytes.

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 eciently 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.

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