In statistics, probability theory and information theory, pointwise mutual information (PMI),[1] or point mutual information, is a measure of association. It compares the probability of two events occurring together to what this probability would be if the events were independent.[2]
PMI (especially in its positive pointwise mutual information variant) has been described as "one of the most important concepts in NLP", where it "draws on the intuition that the best way to weigh the association between two words is to ask how much more the two words co-occur in [a] corpus than we would have expected them to appear by chance."[2]
The concept was introduced in 1961 by Robert Fano under the name of "mutual information", but today that term is instead used for a related measure of dependence between random variables:[2] The mutual information (MI) of two discrete random variables refers to the average PMI of all possible events.