Thus, a term like "professor" that may only occur once in the dataset has the same likelihood of occurring in an "on-event" sentence as a term like "kill" that has a very high frequency in the dataset.
the Jelinek-Mercer and Absolute Discounting smoothing methods estimate the probability of unseen terms according to a background model built using the entire collection.