Exploiting Morphological Regularities in Distributional Word Representations
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.
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