We present a language agnostic, unsupervised method for inducing morphological
transformations between words. The method relies heavily on certain regularities
that manifest in high-dimensional vector spaces. We show that this method is
capable of discovering a wide-range of morphological rules, which can be
successfully used towards improved natural language processing. We evaluate this
method across six different languages and nine datasets, and show significant
improvements across all languages.