However, word embedding models use only one vector to represent a word, and are problematic in some natural language processing applications that require sense level representation (e.g., word sense disambiguation, semantic relation identification, etc.).
some researches try to resolve the polysemy and homonymy issue and introduce sense level embedding, either act as pre-process (Iacobacci et al., 2015) or post-process  fashion.