Extracting Relations from Unstructured Text Sources for Music Recommendation

TitleExtracting Relations from Unstructured Text Sources for Music Recommendation
Publication TypeConference Paper
Year of Publication2015
Conference Name20th International Conference on Applications of Natural Language to Information Systems
AuthorsSordo, M., Oramas S., & Espinosa-Anke L.
Conference Start Date17/06/2015
Conference LocationPassau, Germany
KeywordsKnowledge Acquisition, music recommendation, Relation Extraction
AbstractThis 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.
preprint/postprint documentfiles/publications/nldb2015_msordo_soramas_lespinosa.pdf
Final publicationhttps://link.springer.com/chapter/10.1007/978-3-319-19581-0_33