N-best list search: For each sentence W in document d, find N -best parse trees, and denote T N as the collection of N -best list parse trees for sentences over entire corpus D under model parameter p. 2.
eM update: Perform one iteration (or several iterations) of eM algorithm to estimate model parameters that maximizes N -best-list likelihood of the training corpus D, (a) e-step: Compute the auxiliary function of the N -best-list likelihood with respect to p ′ to get new update for p. Iterate steps (1) and (2) until the convergence of the N -best-list likelihood.