TL;DR
This paper explores NLP techniques to extract and analyze musical knowledge from historical and contemporary texts, demonstrating applications across various musical genres and eras.
Contribution
It introduces a comprehensive NLP pipeline tailored for music knowledge discovery, integrating multiple methods and use cases for the first time.
Findings
Effective corpus compilation for diverse musical texts
Successful extraction of music-related information from historical documents
Insights into musical trends through data-driven analysis
Abstract
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.
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