Discriminative Sequence Labeling by Z-Score Optimization

Authors:
Elisa Ricci;Tijl Bie;Nello Cristianini
Affiliations:
Dept. of Electronic and Information Engineering, University of Perugia, 06125, Perugia, Italy;Dept. of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK;Dept. of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK and Dept. of Computer Science, University of Bristol, Bristol, BS8 1TR, UK
Venue:
ECML '07 Proceedings of the 18th European conference on Machine Learning
Year:
2007

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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.