Piecewise CRF

See: Conditional Random Field Model, Piecewise Training.

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References

2004

• (McCallum & Sutton, 2004) ⇒ Andrew McCallum, and Charles Sutton. (2004). “Piecewise Training with Parameter Independence Diagrams: Comparing Globally- and Locally-trained Linear-chain CRFs.” In: NIPS 2004 Workshop on Learning with Structured Outputs.
  • 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 statisticallysignificant gain in accuracy.
  • Cited by ~8 http://scholar.google.com/scholar?cites=10025969486943284164

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