Embeddings as representation for symbolic music

Particularly in Natural Language Processing (NLP), the necessity of a good representation of the words that achieves some implicit context understanding is important [Turian et al., 2010]. A typical representation to feed in a machine learning model is the binary one-hot vector, in this case, an array with as many positions as words in the vocabulary is created, and the words are represented by a version of the array containing a one digit in the position corresponding to the word. For example, the sentence "I like eating bread and eating cheese", would have as vocabulary the set "I", "like", "eating", "bread", "and", "cheese", thus the representation of this words would be 6-dimensional binary one-hot vectors like "I" 100000, "like" 010000, cheese 000001. As you can imagine, this representation has no context understanding at all, since all words are completely independent, "bread" and "cheese" are as different between them as "I" and "like", which for a human is not the case.