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Topic Models

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Probabilistic Topic Models
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Abstract

Topic models have attracted substantial attention from academia and industry. Researchers have designed various types of topic models that are applied to a wide range of tasks. In this chapter, we select some representative topic models for introducing the mathematical principles behind topic models. By studying this chapter, readers will have a deep understanding of the foundation of topic models, and the ability to select appropriate existing models or design brand-new models for their own scenarios.

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Notes

  1. 1.

    We choose the TWE-1 model [10].

  2. 2.

    MCMC sampling algorithm will be explained in detail in Chap. 5. In this chapter, readers only need to know that the MCMC sampling algorithm annotates each word with a topic.

References

  1. Balikas G, Amini MR, Clausel M (2016) On a topic model for sentences. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 921–924

    Google Scholar 

  2. Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning. ACM, pp 113–120

    Google Scholar 

  3. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    MATH  Google Scholar 

  4. Deerwester S (1988) Improving information retrieval with latent semantic indexing. BibSonomy

    Google Scholar 

  5. Hofmann T (1999) Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers, Burlington, pp 289–296

    Google Scholar 

  6. Jiang D, Ng W (2013) Mining web search topics with diverse spatiotemporal patterns. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 881–884

    Google Scholar 

  7. Jiang D, Vosecky J, Leung KWT, Ng W (2012) G-wstd: A framework for geographic web search topic discovery. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp 1143–1152

    Google Scholar 

  8. Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. ACM, pp 815–824

    Google Scholar 

  9. Li W, McCallum A (2006) Pachinko allocation: Dag-structured mixture models of topic correlations. In: Proceedings of the 23rd International Conference on Machine Learning. ACM, pp 577–584

    Google Scholar 

  10. Liu Y, Liu Z, Chua TS, Sun M (2015) Topical word embeddings. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp 2418–2424

    Google Scholar 

  11. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. Computer Science. arXiv:1301.3781

    Google Scholar 

  12. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge

    MATH  Google Scholar 

  13. Sizov S (2010) Geofolk: latent spatial semantics in web 2.0 social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining. ACM, pp 281–290

    Google Scholar 

  14. Wang X, Mccallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 424–433

    Google Scholar 

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Jiang, D., Zhang, C., Song, Y. (2023). Topic Models. In: Probabilistic Topic Models. Springer, Singapore. https://doi.org/10.1007/978-981-99-2431-8_2

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  • DOI: https://doi.org/10.1007/978-981-99-2431-8_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2430-1

  • Online ISBN: 978-981-99-2431-8

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