Disambiguating Homographs and Homophones Simultaneously: A Regrouping Method for Japanese

Abstract

We present a method that re-groups surface forms into clusters representing synonyms, and help disambiguate homographs as well as homophone. The method is applied post-hoc to trained contextual word embeddings. It is beneficial to languages where both homographs and homophones abound, which compromise the efficiency of language model and causes the underestimation problem in evaluation. Taking Japanese as an example, we evaluate how accurate such disambiguation can be, and how much the underestimation can be mitigated.

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