Natural Language Processing for Music Knowledge Discovery
This work addresses the challenge of automating music knowledge discovery for researchers and historians, but it is incremental as it applies standard NLP methods to new textual data.
The paper tackled the problem of extracting music knowledge from historical text collections using NLP, presenting a pipeline that processes documents across genres like flamenco and Renaissance music to derive data-driven conclusions.
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.