  We design a new co-occurrence based word association measure by incorporating
the concept of significant cooccurrence in the popular word association measure
Pointwise Mutual Information (PMI). By extensive experiments with a large
number of publicly available datasets we show that the newly introduced measure
performs better than other co-occurrence based measures and despite being
resource-light, compares well with the best known resource-heavy distributional
similarity and knowledge based word association measures. We investigate the
source of this performance improvement and find that of the two types of
significant co-occurrence - corpus-level and document-level, the concept of
corpus level significance combined with the use of document counts in place of
word counts is responsible for all the performance gains observed. The concept
of document level significance is not helpful for PMI adaptation.
