Because for efficiency reasons we must work with just a portion of the original tensor, we thought that the AP data set, consisting of a relatively large and balanced collection of nominal concepts, would offer a sensible starting point to extract the subset.
we extract from our best tensor TypeDM the values labeled by tuples w AP , l, w 2 where w AP is in the AP set, l is one of the 100 most common links occurring in tuples with a w AP , and w 2 is one of the 1,000 most common words occurring in tuples with a w AP and a l. The resulting (sub-)tensor, APTypeDM, has dimensionality 402 × 100 × 1, 000 with 1,318,214 non-zero entries (density: 3%).