Exploiting Morphological Regularities in Distributional Word Representations

Abstract

We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set with an accuracy of 85\% which is 12\% higher than the previous best known system.

Tasks

Reproductions