Unsupervised Morphology Induction Using Word Embeddings


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.