I must be missing something here - in step 3, PMI for x, y is calculated as: log( P(x|y) / ( P(x)P(y) ) ) Because the skipgram probabilities are sparse, P(x|y

I must be missing something here - in step 3, PMI for x, y is calculated as: log( P(x|y) / ( P(x)P(y) ) ) Because the skipgram probabilities are sparse, P(x|y) is often going to be zero, so taking the log yields negative infinity. The result is a dense PMI matrix filled (mostly) with -Inf. Should we be adding 1 before taking the log?