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.
References
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, pp. 3111–3119. Curran Associates Inc., USA (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543. Association for Computational Linguistics (2014)
Pilehvar, M.T., Collier, N.: De-conflated semantic representations. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, pp. 1680–1690. Association for Computational Linguistics (2016)
Neelakantan, A., Shankar, J., Passos, A., McCallum, A.: Efficient non-parametric estimation of multiple embeddings per word in vector space. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1059–1069. Association for Computational Linguistics (2014)
Tian, F., Dai, H., Bian, J., Gao, B., Zhang, R., Chen, E., Liu, T.-Y.: A probabilistic model for learning multi-prototype word embeddings. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, pp. 151–160. Dublin City University and Association for Computational Linguistics (2014)
Liu, Y., Liu, Z., Chua, T.-S., Sun, M.: Topical word embeddings. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 2418–2424. AAAI Press (2015)
Fellbaum, C.: WordNet, Wiley Online Library (1998)
Navigli, R., Ponzetto, S.P.: Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)
Jauhar, S.K., Dyer, C., Hovy, E.: Ontologically grounded multi-sense representation learning for semantic vector space models. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, pp. 683–693. Association for Computational Linguistics (2015)
Faruqui, M., Dodge, J., Jauhar, S.K., Dyer, C., Hovy, E., Smith, N.A.: Retrofitting word vectors to semantic lexicons. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, pp. 1606–1615. Association for Computational Linguistics (2015)
Camacho-Collados, J., Pilehvar, M.T., Navigli, R.: NASARI: a novel approach to a semantically-aware representation of items. In: Proceedings of NAACL, pp. 567–577 (2015)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, pp. 2787–2795. Curran Associates Inc. (2013)
Pilehvar, M.T., Navigli, R.: From senses to texts: an all-in-one graph-based approach for measuring semantic similarity. Artif. Intell. 228, 95–128 (2015)
Etzioni, O., Reiter, K., Soderland, S., Sammer, M., Turing Center: Lexical translation with application to image search on the web. Machine Translation Summit XI
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Stroudsburg, PA, USA, pp. 1535–1545. Association for Computational Linguistics (2011)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, pp. 1306–1313. AAAI Press (2010)
Delli Bovi, C., Espinosa Anke, L., Navigli, R.: Knowledge base unification via sense embeddings and disambiguation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 726–736. Association for Computational Linguistics (2015)
Swartz, A.: Musicbrainz: a semantic web service. IEEE Intell. Syst. 17(1), 76–77 (2002)
Oramas, S., Sordo, M., Espinosa-Anke, L., Serra, X.: A Semantic-based Approach for Artist Similarity. In: Proceedings of the International Society for Music Information Retrieval Conference, Málaga, Spain, pp. 100–106 (2015)
Sordo, M., Oramas, S., Espinosa-Anke, L.: Extracting relations from unstructured text sources for music recommendation. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds.) NLDB 2015. LNCS, vol. 9103, pp. 369–382. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19581-0_33
Oramas, S., Espinosa-Anke, L., Sordo, M., Saggion, H., Serra, X.: Information extraction for knowledge base construction in the music domain. Data Knowl. Eng. 106, 70–83 (2016)
Gruhl, D., Nagarajan, M., Pieper, J., Robson, C., Sheth, A.: Context and domain knowledge enhanced entity spotting in informal text. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 260–276. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04930-9_17
Zhang, X., Liu, Z., Qiu, H., Fu, Y.: A hybrid approach for chinese named entity recognition in music domain. In: 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 677–681 (2009)
Oramas, S., Espinosa-Anke, L., Sordo, M., Saggion, H., Serra, X.: ELMD: An automatically generated entity linking gold standard dataset in the music domain. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (2016)
Iacobacci, I., Pilehvar, M.T., Navigli, R.: Sensembed: learning sense embeddings for word and relational similarity. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1 Long Papers, Association for Computational Linguistics, Beijing, China, pp. 95–105 (2015)
Manicini, M., Camacho-Collados, J., Iacobacci, I., Navigli, R.: Embedding words and senses together via joint knowledge-enhanced training, arXiv prepring arXiv:1612.02703
Mikolov, T., Yih, W.-T., Zweig, G.: Linguistic regularities in continuous space word representations. In: HLT-NAACL 2013, pp. 746–751 (2013)
Ellis, D.P., Whitman, B., Berenzweig, A., Lawrence, S.: The quest for ground truth in musical artist similarity. In: ISMIR, Paris, France, pp. 170–177 (2002)
Cohen, W.W., Fan, W.: Web-collaborative filtering: recommending music by crawling the web. Comput. Netw. 33(1), 685–698 (2000)
Schedl, M., Knees, P., Widmer, G.: A web-based approach to assessing artist similarity using co-occurrences. In: Proceedings of the Fourth International Workshop on Content-Based Multimedia Indexing (CBMI 2005) (2005)
Whitman, B., Lawrence, S.: Inferring descriptions and similarity for music from community metadata. In: ICMC 2002 (2002)
Shwartz, V., Goldberg, Y., Dagan, I.: Improving hypernymy detection with an integrated path-based and distributional method, pp. 2389–2398 (2016)
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation, arXiv preprint arXiv:1309.4168
Fu, R., Guo, J., Qin, B., Che, W., Wang, H., Liu, T.: Learning semantic hierarchies via word embeddings. In: Proceedings of ACL, vol. 1, pp. 1199–1209. Association for Computational Linguistics (2014)
Tan, L., Zhang, H., Clarke, C., Smucker, M.: Lexical comparison between wikipedia and twitter corpora by using word embeddings. In: Proceedings of ACL (2), Beijing, China, pp. 657–661 (2015)
Rodrıguez-Fernández, S., Espinosa-Anke, L., Carlini, R., Wanner, L.: Semantics-driven recognition of collocations using word embeddings. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL): Short Papers, pp. 499–505 (2016)
Bian, J., Liu, Y., Agichtein, E., Zha, H.: Finding the right facts in the crowd: factoid question answering over social media. In: Proceedings of the 17th International Conference on World Wide Web, pp. 467–476. ACM (2008)
Espinosa Anke, L., Camacho-Collados, J., Delli Bovi, C., Saggion, H.: Supervised distributional hypernym discovery via domain adaptation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, pp. 424–435. Association for Computational Linguistics (2016)
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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