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Previous researches have shown that learning multiple representations forpolysemous words can improve the performance of word embeddings on many tasks.However, this leads to another problem. Several vectors of a word may actuallypoint to the same meaning, namely pseudo multi-sense. In this paper, weintroduce the concept of pseudo multi-sense, and then propose an algorithm todetect such cases. With the consideration of the detected pseudo multi-sensecases, we try to refine the existing word embeddings to eliminate the influenceof pseudo multi-sense. Moreover, we apply our algorithm on previous releasedmulti-sense word embeddings and tested it on artificial word similarity tasksand the analogy task. The result of the experiments shows that diminishingpseudo multi-sense can improve the quality of word representations. Thus, ourmethod is actually an efficient way to reduce linguistic complexity.

Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation