  Word embedding pioneered by Mikolov et al. is a staple technique for word
representations in natural language processing (NLP) research which has also
found popularity in music information retrieval tasks. Depending on the type of
text data for word embedding, however, vocabulary size and the degree of
musical pertinence can significantly vary. In this work, we (1) train the
distributed representation of words using combinations of both general text
data and music-specific data and (2) evaluate the system in terms of how they
associate listening contexts with musical compositions.
