Although the purpose of the shrinking technique introduced was clear after the first pass through the paper, like Sameer, i had problems undersantding the actuall process of shrinking. Now, it is clear that the task was to learn the appropriate lambda values used in equation (2) for a fixed hierarchy.

I'm also looking forward for the expaination of the set H_j in eqn (3), that was a bit fuzzy.

Although one could deduce the symbols used in the representation of the HMMs (figure 1-2), a single definition for each symbol would have made reading more enjoyable.

Although taking the harmonic mean of the precision and recall values gave a clearer view but this evaluation metric may not be appropriate and commonly used in similar work. Futhermore, i agree with the comment by Greg that the results were not conclusive and did not give a general structure/hierarchy that was performing best across all data sets.

Although i'm not familiar with the literature for HMMs but the comment in the conclusion section about the avalibility of the Viterbi algorithm for HMMs i found quite amusing. I hardly believe that that is a new technique that should be pointed out in anyway.