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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Ricci, E., De Bie, T., & Cristianini, N. (2007). Discriminative sequence labeling by Z-score optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4701 LNAI, pp. 274–285). Springer Verlag. https://doi.org/10.1007/978-3-540-74958-5_27
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