  The entropy of an ergodic finite-alphabet process can be computed from a
single typical sample path x_1^n using the entropy of the k-block empirical
probability and letting k grow with $n$ roughly like log n. We further assume
that the distribution of the process is a g-measure; g-measures form a large
class of Gibbs measures. We prove large deviation principles for conditional,
non-conditional and relative k(n)-block empirical entropies.
