The Latent Dirichlet Allocation (LDA) estimates the distribution of topics in documents and the distribution of words in topics

Question

BlackTom AI · Accepted Answer

The statement concerns what Latent Dirichlet Allocation (LDA) estimates in the generative model. LDA defines two key distributions: first, the distribution of topics within each document, typically denoted theta (the per-document topic distribution); second, the distribution of words within each topic, typically denoted phi (the per-topic word distribution).

Option 1: True. This aligns with LDA’s core components: it models how documents are mixtures of topics and how topics are mixtures of words. Therefore, LDA estimates both the distribution of topics in documents and the distribution of words in topics.

Option 2: False. If one claimed that LDA estimates something else, such as a distribution over documents or an alternative set of parameters, this would be inaccurate. LDA does not, for example, directly estimate a single distribution over documents; rather, it estimates per-document topic proportions and per-topic word distributions, with words in documents generated from those topics.

In summary, the statement correctly captures the two primary distributions that LDA learns: per-document topic distributions and per-topic word distributions, which together explain how topics and words relate within the corpus.

SP2025.B69.DAT.562.24 COMPLETE Quiz #2 AFTER VIDEO 1: LDA Overview

The Latent Dirichlet Allocation (LDA) estimates the distribution of topics in documents and the distribution of words in topics

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Similar Questions

The output of the Latent Dirichlet Allocation (LDA) topic model is:

The Latent Dirichlet Allocation (LDA) topic model is a generative model, that is it mimics (but in a simplified way) how humans write documents:

Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

The Latent Dirichlet Allocation (LDA) topic model needs an initial guess for the distribution of topics in documents and distribution of words in each topic:

The Latent Dirichlet Allocation (LDA) topic model accepts data in the following format (choose one best answer):

The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

The Latent Dirichlet Allocation (LDA) approach to topic modeling assumes that:

What is topic modeling? Choose one best answer:

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