Discriminative Sequence Labeling by Z-Score Optimization

Elisa Ricci, Tijl De Bie, Nello Cristianini

Research output: Contribution to journalArticle (Academic Journal)peer-review


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.
Translated title of the contributionDiscriminative Sequence Labeling by Z-Score Optimization
Original languageEnglish
Pages (from-to)274-285
JournalECML 2007
Publication statusPublished - 2007

Bibliographical note

ISBN: 9783540749578
Publisher: Springer
Name and Venue of Conference: ECML 2007
Other identifier: 2000792


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