What is topic modeling? Choose one best answer:

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BlackTom AI · Accepted Answer

Question: What is topic modeling? Choose one best answer:

Option 1: 'Categorizing documents into predefined topics'—This describes a supervised or predefined taxonomy approach, where topics are already defined and documents are assigned to them. Topic modeling, by contrast, is typically unsupervised and aims to uncover latent topics that emerge from the data rather than forcing documents into pre-existing categories.

Option 2: 'Discovering and describing topics in documents'—This aligns with the core idea of topic modeling: identifying hidden topics present in a collection of documents and describing these topics by examining the most representative terms. This approach seeks to reveal the underlying thematic structure without requiring predefined labels.

In evaluating the options, the key distinction is whether the task assumes predefined topics or aims to discover topics from the data itself. The first option reflects a classification task with existing topics, whereas topic modeling is inherently about discovery and description of topics that emerge from the text data. The second option accurately captures the essence of topic modeling by emphasizing discovery and description of topics within documents.

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

What is topic modeling? Choose one best answer:

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类似问题

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:

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

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