(2012) applied K-means clustering to decompose word embeddings into multiple prototypes, each denoting a distinct meaning of the target word.
in order to alleviate this issue, we leveraged a state-of-the-art Word Sense Disambiguation (WSD) algorithm to automatically generate large amounts of sense-annotated corpora.