In general, an n-gram model trained on a limited set of topic-specific documents tends to overemphasize words from the observed topics instead of evenly distributing weights over all potential topics.
given the list of words following an n-gram context, we would like to deemphasize the observed occurrences of topic words and ideally redistribute these counts to all potential topic words.