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.
|Title of host publication||NIPS Workshop on Learning with Structured Outputs|
|Number of pages||10|
|Publication status||Published - 2004|