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Discriminative Sequence Labeling by Z-Score Optimization.
- Source :
- Machine Learning: ECML 2007; 2007, p274-285, 12p
- Publication Year :
- 2007
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540749578
- Database :
- Complementary Index
- Journal :
- Machine Learning: ECML 2007
- Publication Type :
- Book
- Accession number :
- 33170038
- Full Text :
- https://doi.org/10.1007/978-3-540-74958-5_27