Induction of the common-sense hierarchies in lexical data

Abstract

Unsupervised organization of a set of lexical concepts that captures common-sense knowledge inducting meaningful partitioning of data is described. Projection of data on principal components allow for identification of clusters with wide margins, and the procedure is recursively repeated within each cluster. Application of this idea to a simple dataset describing animals created hierarchical partitioning with each clusters related to a set of features that have common-sense interpretation. © 2011 Springer-Verlag.