Find the most probable label sequence (with probabilities of individual lables determined with respect to marginal distributions) satisfying the constraints imposed over label values.
Get (at most) k best tags for each word and return them in descending order. TODO: Tagging with respect to marginal distributions might not be the best idea. Think of some more elegant method.
Tag probabilities with respect to marginal distributions.
Compute the accuracy of the model with respect to the labeled dataset.
A list of features (represented by feature indices) defined within the context of the sentence accompanied by expected probabilities determined on the basis of the model.
One feature can occur multiple times in the output list.
Normalization factor computed for the
Xs sentence using the