Abstract
How useful are topic models based on song lyrics for applications in music information retrieval? Unsupervised topic models on text corpora are often difficult to interpret. Based on a large collection of lyrics, we investigate how well automatically generated topics are related to manual topic annotations. We propose to use the kurtosis metric to align unsupervised topics with a reference model of supervised topics. This metric is well-suited for topic assessments, as it turns out to be more strongly correlated with manual topic quality scores than existing measures for semantic coherence. We also show how it can be used for a detailed graphical topic quality assessment.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Logan, B., Kositsky, A., Moreno, P.: Semantic analysis of song lyrics. In: International Conference on Multimedia and Expo, ICME 2004, vol. 2, pp. 827–830. IEEE (2004)
Kleedorfer, F., Knees, P., Pohle, T.: Oh oh oh whoah! towards automatic topic detection in song lyrics. In: Proceedings of the 9th ISMIR, pp. 287–292 (2008)
Newman, D., Karimi, S., Cavedon, L.: External evaluation of topic models. In: Australasian Document Computing Symposium (ADCS), pp. 1–8 (2009)
Chuang, J., Gupta, S., Manning, C.D., Heer, J.: Topic model diagnostics: Assessing domain relevance via topical alignment. In: ICML (2013)
McFee, B., Bertin-Mahieux, T., Ellis, D.P., Lanckriet, G.R.: The million song dataset challenge. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 909–916. ACM (2012)
McCallum, A.K.: Mallet: A machine learning for language toolkit (2002), http://mallet.cs.umass.edu
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on EMNLP, pp. 248–256. ACL (2009)
Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24–26. ACM (1986)
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Sterckx, L., Demeester, T., Deleu, J., Mertens, L., Develder, C. (2014). Assessing Quality of Unsupervised Topics in Song Lyrics. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_55
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DOI: https://doi.org/10.1007/978-3-319-06028-6_55
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