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Extracting Relations from Unstructured Text Sources for Music Recommendation

  • Conference paper
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Book cover Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

Abstract

This paper presents a method for the generation of structured data sources for music recommendation using information extracted from unstructured text sources. The proposed method identifies entities in text that are relevant to the music domain, and then extracts semantically meaningful relations between them. The extracted entities and relations are represented as a graph, from which the recommendations are computed. A major advantage of this approach is that the recommendations can be conveyed to the user using natural language, thus providing an enhanced user experience. We test our method on texts from songfacts.com, a website that provides facts and stories about songs. The extracted relations are evaluated intrinsically by assessing their linguistic quality, as well as extrinsically by assessing the extent to which they map an existing music knowledge base. Finally, an experiment with real users is performed to assess the suitability of the extracted knowledge for music recommendation. Our method is able to extract relations between pair of musical entities with high precision, and the explanation of those relations to the user improves user satisfaction considerably.

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Notes

  1. 1.

    http://nlp.stanford.edu/software/tokenizer.shtml.

  2. 2.

    https://github.com/dbpedia-spotlight/dbpedia-spotlight/wiki/Web-service.

  3. 3.

    http://ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html.

  4. 4.

    http://www.songfacts.com.

  5. 5.

    http://musicbrainz.org/.

  6. 6.

    http://musicbrainz.org/doc/Development/XML_Web_Service/Version_2.

  7. 7.

    https://musicbrainz.org/relationships.

  8. 8.

    The individual precision and recall scores are available at http://goo.gl/C4Coj3.

  9. 9.

    Some participants did not rate all the 10 recommended songs.

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Acknowledgments

The authors would like to thank Miguel Ballesteros for his valuable advice and the subjects of the online experiment for their feedback.

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Authors and Affiliations

  1. Center for Computational Science, University of Miami, Coral Gables, FL, USA

    Mohamed Sordo

  2. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Sergio Oramas & Luis Espinosa-Anke

Authors
  1. Mohamed Sordo
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  2. Sergio Oramas
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  3. Luis Espinosa-Anke
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Corresponding author

Correspondence to Mohamed Sordo .

Editor information

Editors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Chris Biemann

  2. Universität Passau, Passau, Germany

    Siegfried Handschuh

  3. Universität Passau, Passau, Germany

    André Freitas

  4. University of Salford, Salford, United Kingdom

    Farid Meziane

  5. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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Sordo, M., Oramas, S., Espinosa-Anke, L. (2015). Extracting Relations from Unstructured Text Sources for Music Recommendation. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_33

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