  Today, a massive amount of musical knowledge is stored in written form, with
testimonies dated as far back as several centuries ago. In this work, we
present different Natural Language Processing (NLP) approaches to harness the
potential of these text collections for automatic music knowledge discovery,
covering different phases in a prototypical NLP pipeline, namely corpus
compilation, text-mining, information extraction, knowledge graph generation
and sentiment analysis. Each of these approaches is presented alongside
different use cases (i.e., flamenco, Renaissance and popular music) where large
collections of documents are processed, and conclusions stemming from
data-driven analyses are presented and discussed.
