Piecewise Training with Parameter Independence Diagrams: Comparing Globally- and Locally-trained Linear-chain CRFs

Andrew McCallum, Charles Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a diagrammatic formalism and practial methods for introducing additional independence assumptions into parameter estimation, enabling efficient training of undirected graphical models in locally-normalized pieces. On two real-world data sets we demonstrate our locally-trained linear-chain CRFs outperforming traditional CRFs, training in less than one-fifth the time, and providing a statistically significant gain in accuracy.
Original languageEnglish
Title of host publicationNIPS Workshop on Learning with Structured Outputs
PublisherNIPS Foundation
Number of pages10
Publication statusPublished - 2004

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