Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence
This work addresses the need for more accurate and efficient word association measures in natural language processing, though it is incremental as it builds on existing PMI.
The authors tackled the problem of improving word association measures by incorporating significant co-occurrence into Pointwise Mutual Information (PMI), resulting in a new measure that outperforms other co-occurrence-based methods and competes with resource-heavy alternatives.
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.