I liked their approach, but I read this paper 9 months ago and was totally confused. This time around it makes more sense, especially after Aldebaro's talk on discriminative vs. generative models.

My main confusion this time was on how the features (f_<b,s>) is connected with the probabilities, but equations 2 and 4 make it fairly clear. The features chosen seem to be good, if heuristically chosen. However, my biggest complaint is that they appear to be ignoring MOST word/vocabulary information! This makes the model significantly different from the HMMs I am used to. True, the features could be used for vocabulary, but it seems that it would be very hard to adapt this model to a vocabulary-based, free-structure environment. In other words, this model seems good for highly structured text (where whitespace and certain keywords play a big role), but not for unstructured text. Incorporating word frequency information seems to be very difficult.