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Title:
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ELMDist: a vector space model with words and MusicBrainz entities
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Author:
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Espinosa-Anke, Luis; Oramas, Sergio; Saggion, Horacio; Serra, Xavier
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Abstract:
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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. |
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Abstract:
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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. |
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Abstract:
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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). |
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Subject(s):
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-Word embeddings
-Music
-Deep learning
-Semantics |
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Rights:
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Document type:
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Conference Object
Article - Submitted version |
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Published by:
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Springer
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