With the pre-trained word embedding, some researches propose post-processing models that incorporate with the existing semantic knowledge into the word embedding model (Faruqui et al., 2015;Yu and Dredze, 2014).
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.).