We represent the prior and posterior beliefs as vectors of the probabilities of each syntactic category spanning w j i , and measure M ij , the amount of modification required, as the summed K-L divergence of the prior from the posterior vector.
if N is the set of nonterminals in the grammar, the size of the belief update is modeled as 1 In the remainder of the paper, we show how to compute M ij by using Bayesian inference on quantities calculated in ordinary probabilistic incremental Earley parsing with a stochastic context-free grammar (SCFG), and show that our model makes the correct predictions using an SCFG for English on the original local-coherences experiment of .