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Unsupervised learned representations of polysemous words generate a large ofpseudo multi senses since unsupervised methods are overly sensitive tocontextual variations. In this paper, we address the pseudo multi-sensedetection for word embeddings by dimensionality reduction of sense pairs. Wepropose a novel principal analysis method, termed Ex-RPCA, designed to detectboth pseudo multi senses and real multi senses. With Ex-RPCA, we empiricallyshow that pseudo multi senses are generated systematically in unsupervisedmethod. Moreover, the multi-sense word embeddings can by improved by a simplelinear transformation based on Ex-RPCA. Our improved word embedding outperformthe original one by 5.6 points on Stanford contextual word similarity (SCWS)dataset. We hope our simple yet effective approach will help the linguisticanalysis of multi-sense word embeddings in the future.

Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis