Q. What technique is used to decompose the term by documents C matrix in latent semantic analysis (LSA)?

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Q. What is the purpose of the latent factors weight matrix Λ in LSA?

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Q. In LSA, what is the primary purpose of dimensionality reduction?

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Q. What characteristic do both matrices U and VT have in LSA?

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Q. What is the outcome of LSA analysis typically based on?

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Q. In the context of LSA, what is the benefit of using a subset of singular values for dimensionality reduction?

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Q. What does the "folding in" method allow LSA/LSI to do?

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Q. What is a key limitation of techniques like latent semantic analysis (LSA) as compared to generative approaches like LDA?

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Q. What is the primary advantage of latent Dirichlet analysis (LDA) over techniques like LSA?

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Q. In the context of LDA, what are anchor terms?

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Q. What role does the Dirichlet distribution play in LDA?

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Q. How is the overall number of topics for a given LDA analysis determined?

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Q. What does the generative model in LDA describe?

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Q. What is the primary purpose of using Gibbs sampling or variational inference methods in LDA?

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Q. How are the results of LDA typically evaluated?

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Q. What is a useful indication for selecting appropriate numbers of topics when evaluating LDA from a domain perspective?

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Q. How does LDA handle multilabel classification in the context of topic modeling?

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Q. What does LDA's ability to uncover scientific topics "hidden" in documents provide from a university point of view?

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Q. What is a challenge in introducing a flexible number of topics in LDA, as compared to clustering methods allowing for an adaptable number of clusters?

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Q. What motivated the extension of the LDA approach to allow for an infinite number of topics?

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