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 clustering. 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 à 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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Notes
- 1.
Available at https://bitbucket.org/luisespinosa/elmdist/.
- 2.
- 3.
Described in http://mtg.upf.edu/download/datasets/elmd.
- 4.
For readability purposes, we have shortened the mbid of the annotated entities.
- 5.
- 6.
Since this judgement is, in the end, a subjective decision, we did not ask them to look at data such as listening habits.
- 7.
- 8.
These were collected manually by inspecting nearest neighbours to the different types considered in the Google News model.
- 9.
For multiword entities, we average the corresponding vectors of each token.
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Acknowledgements
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 under 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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Espinosa-Anke, L., Oramas, S., Saggion, H., Serra, X. (2017). ELMDist: A Vector Space Model with Words and MusicBrainz Entities. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_44
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