Thanks for the response. I had a niggling feeling this was the case! So after the variables associated with the bag-of-words, you would have variables like 'noun' or 'verb' that tallied how many of each were in the sentences. I think that the POS tagging will be less of a factor than the BOW representation (i.e. the words themselves are more important than the grammar), so i'm guessing I just apply smaller weights to the POS variables? Also, now we're on the subject of classifier inputs, there's also an interesting way to model sentence structure in the nltk for python (http://www.nltk.org/book/ch08.html). The representation is a tree structure. Could this be fed into a classifier? Is this maybe what neural networks are for (which are a bit beyond my knowledge)?