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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Detalhes bibliográficos
Autores: Oramas, Sergio, Espinosa-Anke, Luis, Gómez, Francisco, Serra, Xavier
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2018
País:España
Recursos:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/35119
Acesso em linha:http://hdl.handle.net/10230/35119
http://dx.doi.org/10.1080/09298215.2018.1488878
Access Level:acceso abierto
Palavra-chave:Musicology
Natural language processing
Information extraction
Entity linking
Sentiment analysis
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spelling Natural language processing for music knowledge discoveryOramas, SergioEspinosa-Anke, LuisGómez, FranciscoSerra, XavierMusicologyNatural language processingInformation extractionEntity linkingSentiment analysisToday, 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.This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the COFLA2 research project (Proyectos de Excelencia de la Junta de Andalucía, FEDER P12-TIC-1362).Taylor & Francis (Routledge)20182018info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/35119http://dx.doi.org/10.1080/09298215.2018.1488878reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésJournal of New Music Research. 2018;47(4):365-82© Taylor & Francis. This is an electronic version of an article published in "Oramas S, Espinosa-Anke L, Gómez F, Serra X. Natural language processing for music knowledge discovery. J New Music Res. 2018;47(4):365-82. Journal of New Music Research is available online at: https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1488878.info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/351192026-06-12T07:21:37Z
dc.title.none.fl_str_mv Natural language processing for music knowledge discovery
title Natural language processing for music knowledge discovery
spellingShingle Natural language processing for music knowledge discovery
Oramas, Sergio
Musicology
Natural language processing
Information extraction
Entity linking
Sentiment analysis
title_short Natural language processing for music knowledge discovery
title_full Natural language processing for music knowledge discovery
title_fullStr Natural language processing for music knowledge discovery
title_full_unstemmed Natural language processing for music knowledge discovery
title_sort Natural language processing for music knowledge discovery
dc.creator.none.fl_str_mv Oramas, Sergio
Espinosa-Anke, Luis
Gómez, Francisco
Serra, Xavier
author Oramas, Sergio
author_facet Oramas, Sergio
Espinosa-Anke, Luis
Gómez, Francisco
Serra, Xavier
author_role author
author2 Espinosa-Anke, Luis
Gómez, Francisco
Serra, Xavier
author2_role author
author
author
dc.subject.none.fl_str_mv Musicology
Natural language processing
Information extraction
Entity linking
Sentiment analysis
topic Musicology
Natural language processing
Information extraction
Entity linking
Sentiment analysis
description 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.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/35119
http://dx.doi.org/10.1080/09298215.2018.1488878
url http://hdl.handle.net/10230/35119
http://dx.doi.org/10.1080/09298215.2018.1488878
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of New Music Research. 2018;47(4):365-82
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis (Routledge)
publisher.none.fl_str_mv Taylor & Francis (Routledge)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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