This results in a smoothed language model for each document set's content distribution where the counts from content words (as determined through inference) are used to determine their probability, and the syntax words are essentially discarded.
our model describes the process of generating a document as traversing an HMM and  emitting either a content word from a single topic's (document set's) content word distribution, or a syntax word from one of C corpus-wide syntax classes where C is a parameter input to the algorithm.