While both systems behave similarly in extracting glosses and learning new patterns by exploiting html tags, they are substantially different in how acquired glosses are ranked.
the former exploits the bag-of-words representation of each extracted gloss and its intersection with the domain terminology, while the latter leverages Probabilistic Topic Models (PTM) by estimating the probability of words and term/gloss pairs to be pertinent to the domain.