Abstract-Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, PMI max , that augments PMI with information about a word's number of senses. The coefficients of PMI max are determined empirically by maximizing a utility function based on the performance of automatic thesaurus generation. We show that it outperforms traditional PMI in the application of automatic thesaurus generation and in two word similarity benchmark tasks: human similarity ratings and TOEFL synonym questions. PMI max achieves a correlation coefficient comparable to the best knowledge-based approaches on the Miller-Charles similarity rating data set.
REF developed a new metric pointwise mutual information (PMI), PMI max that augments traditional PMI to improve semantic similarity between two concepts by estimating information about the word's number of senses.
Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Computer Science