Learning and Generalization of Abstract Semantic Relations: Preliminary Investigation of Bayesian Approaches

A deep problem in cognitive science is to explain the acquisition of abstract semantic relations, such as antonymy and synonymy. Are such relations necessarily part of an innate representational endowment provided to humans? Or, is it possible for a learning system to acquire abstract relations from non-relational inputs of realistic complexity (avoiding hand-coding)? We present a series of computational experiments using Bayesian methods in an effort to learn and generalize abstract semantic relations, using as inputs pairs of specific concepts represented by feature vectors created by Latent Semantic Analysis.