ELMDist: A Vector Space Model with Words and MusicBrainz Entities

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.