Natural language processing for music knowledge discovery

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 discov...

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Bibliographic Details
Published in:Journal of new music research 2018-08, Vol.47 (4), p.365-382
Main Authors: Oramas, Sergio, Espinosa-Anke, Luis, Gómez, Francisco, Serra, Xavier
Format: Article
Language:English
Subjects:
Data mining
Entity linking
Genre
Information extraction
Information retrieval
Knowledge discovery
Music
Musicology
Natural language processing
Popular music
Sentiment analysis
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Summary: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.
ISSN:0929-8215
1744-5027
DOI:10.1080/09298215.2018.1488878