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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Q. What does the Dirichlet process (DP) primarily model when used in the context of hierarchical Dirichlet process (HDP)?

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Q. What is the key to solving the problem of documents not sharing topics in the hierarchical Dirichlet process (HDP)?

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Q. What advantage does the hierarchical Dirichlet process (HDP) offer in topic modeling compared to LDA?

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Q. How can the hierarchical Dirichlet process (HDP) be extended for more advanced applications?

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Q. What is one advantage of correlated topic models (CTMs) over traditional LDA models?

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Q. What kind of prior distribution is commonly used for correlated topic models (CTMs) to account for topic correlations?

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Q. What challenge is associated with extending the correlated topic model (CTM) to accommodate a potentially unlimited number of topics?

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Q. What is the main objective of hierarchical topic modeling?

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Q. What distinguishes hierarchical topic modeling from hierarchical modeling in the context of document analysis?

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Q. What type of matrices require efficient SVD processing in the context of LSA?

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