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Welcome to the Big Textual Data Analytics and Knowledge Management MCQs Page

Dive deep into the fascinating world of Big Textual Data Analytics and Knowledge Management with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Textual Data Analytics and Knowledge Management, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Textual Data Analytics and Knowledge Management, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Big Textual Data Analytics and Knowledge Management MCQs | Page 8 of 11

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Q71.
What is the primary purpose of using Gibbs sampling or variational inference methods in LDA?

a.

To estimate the number of topics for an LDA analysis

b.

To adapt the parameters and hyperparameters according to Bayes' rule

c.

To count the number of documents in a corpus

d.

To define the anchor terms in LDA

Discuss
Answer: (b).To adapt the parameters and hyperparameters according to Bayes' rule Explanation:The primary purpose of using Gibbs sampling or variational inference methods in LDA is to adapt the parameters and hyperparameters according to Bayes' rule.
Q72.
How are the results of LDA typically evaluated?

a.

By performing Bayesian inference

b.

By reporting perplexity based on a held-out set from the training corpus

c.

By analyzing the factorial structure output

d.

By using the Gibbs sampling learning process

Discuss
Answer: (b).By reporting perplexity based on a held-out set from the training corpus Explanation:The results of LDA are typically evaluated by reporting perplexity based on a held-out set from the training corpus.
Q73.
What is a useful indication for selecting appropriate numbers of topics when evaluating LDA from a domain perspective?

a.

The overall number of topics in the model

b.

The complexity of the model's architecture

c.

The thematic coherence of words in the words-by-topic distributions

d.

The flatness of the topic-by-document distributions

Discuss
Answer: (c).The thematic coherence of words in the words-by-topic distributions Explanation:Thematic coherence of words in the words-by-topic distributions is a useful indication for selecting appropriate numbers of topics when evaluating LDA from a domain perspective.
Q74.
How does LDA handle multilabel classification in the context of topic modeling?

a.

By forcing documents to belong to a single class

b.

By assigning classes to documents based on posterior probabilities

c.

By using hierarchical structures for topic classification

d.

By employing a separate classifier for each document

Discuss
Answer: (b).By assigning classes to documents based on posterior probabilities Explanation:LDA handles multilabel classification by assigning classes to documents based on posterior probabilities.
Q75.
What does LDA's ability to uncover scientific topics "hidden" in documents provide from a university point of view?

a.

Handcrafted multidomain ontologies

b.

A common topic model viewpoint

c.

Multilabel classification

d.

Specific modeling requirements

Discuss
Answer: (b).A common topic model viewpoint Explanation:LDA's ability to uncover scientific topics hidden in documents provides a common topic model viewpoint from a university point of view.
Q76.
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?

a.

The lack of meaningful distinctions in the training sample

b.

The requirement for specific modeling requirements

c.

The need for handcrafted multidomain ontologies

d.

The inability to handle hierarchical structures

Discuss
Answer: (a).The lack of meaningful distinctions in the training sample Explanation:The challenge in introducing a flexible number of topics in LDA, as compared to clustering methods allowing for an adaptable number of clusters, is the lack of meaningful distinctions in the training sample.
Q77.
What motivated the extension of the LDA approach to allow for an infinite number of topics?

a.

To improve the performance of LDA on fixed-size corpora

b.

To prevent the introduction of new topics with additional documents

c.

To address the issue of potentially new topics entering the analysis with each new document

d.

To simplify the topic modeling process

Discuss
Answer: (c).To address the issue of potentially new topics entering the analysis with each new document Explanation:The extension of the LDA approach to allow for an infinite number of topics was motivated by the need to address the issue of potentially new topics entering the analysis with each new document.
Q78.
What does the Dirichlet process (DP) primarily model when used in the context of hierarchical Dirichlet process (HDP)?

a.

The choice of topics within a single document

b.

The correlation between terms and topics in a document

c.

The number of documents in a corpus

d.

The overall structure of documents in a corpus

Discuss
Answer: (a).The choice of topics within a single document Explanation:The Dirichlet process (DP) in the context of the hierarchical Dirichlet process (HDP) primarily models the choice of topics within a single document.
Q79.
What is the key to solving the problem of documents not sharing topics in the hierarchical Dirichlet process (HDP)?

a.

Increasing the number of documents

b.

Introducing a higher-level Dirichlet process

c.

Using a document-specific topic counter

d.

Employing normalized topic-term distributions

Discuss
Answer: (b).Introducing a higher-level Dirichlet process Explanation:The key to solving the problem of documents not sharing topics in the hierarchical Dirichlet process (HDP) is introducing a higher-level Dirichlet process from which the per-document topic distributions are sampled.
Q80.
What advantage does the hierarchical Dirichlet process (HDP) offer in topic modeling compared to LDA?

a.

Eliminating the need to find adequate numbers of topics

b.

Simplifying the perplexity plot for model evaluation

c.

Speeding up the learning algorithm

d.

Enabling model parameter optimization

Discuss
Answer: (a).Eliminating the need to find adequate numbers of topics Explanation:The hierarchical Dirichlet process (HDP) offers the advantage of eliminating the need to find adequate numbers of topics.
Page 8 of 11

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