22 Table 5 shows the PARSEVAL scores of all systems.
(2016), these depth-specific models are assumed to be independent of each other and fit with a Gibbs sampler, backward sampling hidden variable sequences from forward distributions using this compiled transition model M (Carter and Kohn, 1996), then counting individual sub-model outcomes from sampled hidden variable sequences, then resampling each sub-model using these counts with Dirichlet priors over a, b, and p models and Beta priors over f and j models, then re-compiling these resampled models into a new M. note that with K category labels this model contains DK 2 + 3DK 3 separate parameters for preterminal categories and top and bottom categories of derivation fragments at every depth level, each of which can be independently learned by the Gibbs sampler.