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.