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ELMDist: a vector space model with words and MusicBrainz entities

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dc.contributor.author Espinosa-Anke, Luis
dc.contributor.author Oramas, Sergio
dc.contributor.author Saggion, Horacio
dc.contributor.author Serra, Xavier
dc.date.accessioned 2017-07-27T14:49:15Z
dc.date.available 2017-07-27T14:49:15Z
dc.date.issued 2017
dc.identifier.citation Espinosa-Anke L, Oramas S, Saggion H, Serra X. ELMDist: a vector space model with words and MusicBrainz entities. In: Blomqvist E, Hose K, Paulheim H, Ławrynowicz A, Ciravegna F, Hartig O, editors. The Semantic Web: ESWC 2017 Satellite Events; 2017 28 May-1 June; Portoroz, Slovenia. Cham: Springer; 2017. p. 355-66. (LNCS; no. 10577). DOI: 10.1007/978-3-319-70407-4_44
dc.identifier.isbn 978-3-319-70406-7
dc.identifier.uri http://hdl.handle.net/10230/32656
dc.description Comunicació presentada a: Workshop on Semantic Deep Learning (SemDeep), celebrat amb ESWC 2017, del 28 de maig a l'1 de juny a Portoroz, Eslovènia.
dc.description.abstract Music consumption habits as well as the Music market have changed dramatically due to the increasing popularity of digital audio and streaming services. Today, users are closer than ever to a vast number of songs, albums, artists and bands. However, the challenge remains in how to make sense of all the data available in the Music domain, and how current state of the art in Natural Language Processing and semantic technologies can contribute in Music Information Retrieval areas such as music recommendation, artist similarity or automatic playlist generation. In this paper, we present and evaluate a distributional sense-based embeddings model in the music domain, which can be easily used for these tasks, as well as a device for improving artist or album clus- tering. The model is trained on a disambiguated corpus linked to the MusicBrainz musical Knowledge Base, and following current knowledge-based approaches to sense-level embeddings, entity-related vectors are provided a la WordNet, concatenating the id of the entity and its mention. The model is evaluated both intrinsically and extrinsically in a supervised entity typing task, and released for the use and scrutiny of the community.
dc.description.sponsorship We would like to thank the anonymous reviewers for their very helpful comments and suggestions for improving the quality of the manuscript. We also acknowledge support from the Spanish Minmistry of Economy and Competitiveness un- der the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and under the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Blomqvist E, Hose K, Paulheim H, Ławrynowicz A, Ciravegna F, Hartig O, editors. The Semantic Web: ESWC 2017 Satellite Events; 2017 28 May-1 June; Portoroz, Slovenia. Cham: Springer; 2017. p. 355-66. (LNCS; no. 10577). DOI: 10.1007/978-3-319-70407-4_44
dc.title ELMDist: a vector space model with words and MusicBrainz entities
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi https://doi.org/10.1007/978-3-319-70407-4_44
dc.subject.keyword Word embeddings
dc.subject.keyword Music
dc.subject.keyword Deep learning
dc.subject.keyword Semantics
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C
dc.relation.projectID info:eu-repo/grantAgreeement/ES/1PN/TIN2015-65308-C5-5-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/submittedVersion


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