Trevor Cohen’s, co-author with Roger Schvaneveldt, and Dominic Widdows of Reflective Random Indexing and indirect inference…, page on distributional semantics which starts with:
Empirical Distributional Semantics is an emerging discipline that is primarily concerned with the derivation of semantics (or meaning) from the distribution of terms in natural language text. My research in DS is concerned primarily with spatial models of meaning, in which terms are projected into high-dimensional semantic space, and an estimate of their semantic relatedness is derived from the distance between them in this space.
The relations derived by these models have many useful applications in biomedicine and beyond. A particularly interesting property of distributional semantics models is their capacity to recognize connections between terms that do not occur together in the same document, as this has implications for knowledge discovery. In many instances it is possible also to reveal a plausible pathway linking these terms by using the distances estimated by distributional semantic models to generate a network representation, and using Pathfinder networks (PFNETS) to reveal the most significant links in this network, as shown in the example below:
Links to projects, software and other cool stuff! Making a separate post on one of his software libraries.