Two multivariate generalizations of pointwise mutual information
Tim Van de Cruys · 2011
Since its introduction into the NLP community, pointwise mutual information has proven to be a useful association measure in numerous natural language processing applications such as collocation extraction and word space models. In its original form, it is restricted to the analysis of two-way co-occurrences. NLP problems, however, need not be restricted to twoway co-occurrences; often, a particular problem can be more naturally tackled when formulated as a multi-way problem. In this paper, we explore two multivariate generalizations of pointwise mutual information, and explore their usefulness and nature in the extraction of subject verb object triples. 1