Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence
Table 6: 5-fold cross validation comparison of cPMId with other PMI variants.
['[EMPTY]', 'Edinburgh (83,713)', 'Florida (59,852)', 'Kent (14,086)', 'Minnesota (9,649)', 'White- Abrams (652)', 'Goldfarb- Halpern (384)', 'Wordsim (351)', 'Esslli (272)']
Since the maximum value of f(x,y)f(x)∗f(y)/W is 1f(x,y)/W, one way to bound the former is to divide it by later. =logf(x,y)2f(x)∗f(y) and min. value of PMI2 are 0,−∞, whereas one can get 1,-1 as the bounds if one normalize PMI as nPMI: nPMI(x,y)=logf(x,y)f(x)∗f(y)/Wlog1f(x, y)/W We find that while both nPMI and PMI2 perform better than PMI,cPMId performs better than both variants of nPMI and PMI2 on almost all datasets.