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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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Check out the MCQs below to embark on an enriching journey through Big Textual Data Analytics and Knowledge Management. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

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Big Textual Data Analytics and Knowledge Management MCQs | Page 9 of 11

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Q81.
How can the hierarchical Dirichlet process (HDP) be extended for more advanced applications?

a.

By using a fixed number of topics

b.

By incorporating document-specific topic counters

c.

By adding hierarchical structures to the process

d.

By introducing non-Bayesian methods

Discuss
Answer: (c).By adding hierarchical structures to the process Explanation:The hierarchical Dirichlet process (HDP) can be extended for more advanced applications by adding hierarchical structures to the process.
Q82.
What is one advantage of correlated topic models (CTMs) over traditional LDA models?

a.

CTMs handle document generation more efficiently.

b.

CTMs can accommodate an unlimited number of topics.

c.

CTMs explicitly represent topic correlations.

d.

CTMs use the same inferencing procedures as LDA.

Discuss
Answer: (c).CTMs explicitly represent topic correlations. Explanation:One advantage of correlated topic models (CTMs) over traditional LDA models is that CTMs explicitly represent topic correlations.
Q83.
What kind of prior distribution is commonly used for correlated topic models (CTMs) to account for topic correlations?

a.

Multinomial distribution

b.

Multivariate log-normal prior distribution

c.

Dirichlet distribution

d.

Gaussian distribution

Discuss
Answer: (b).Multivariate log-normal prior distribution Explanation:Correlated topic models (CTMs) commonly use a multivariate log-normal prior distribution to account for topic correlations.
Q84.
What challenge is associated with extending the correlated topic model (CTM) to accommodate a potentially unlimited number of topics?

a.

The need for a hierarchical structure

b.

The requirement for prior distribution updates

c.

Difficulty in achieving parameter estimates

d.

Fixed dimensionality of the multivariate Gaussian

Discuss
Answer: (d).Fixed dimensionality of the multivariate Gaussian Explanation:The challenge associated with extending the correlated topic model (CTM) to accommodate a potentially unlimited number of topics is the fixed dimensionality of the multivariate Gaussian.
Q85.
What is the main objective of hierarchical topic modeling?

a.

To improve the performance of LDA models

b.

To generate topic tags for documents

c.

To construct a tree of topics with varying levels of specificity

d.

To ensure document hierarchies are inferred correctly

Discuss
Answer: (c).To construct a tree of topics with varying levels of specificity Explanation:The main objective of hierarchical topic modeling is to construct a tree of topics with more generic topics closer to the root and more detailed topics closer to the leaves, with varying levels of specificity.
Q86.
What distinguishes hierarchical topic modeling from hierarchical modeling in the context of document analysis?

a.

The number of documents analyzed

b.

The use of Bayesian statistical inference

c.

The tree depth of the generated structure

d.

The inclusion of non-leaf nodes in the structure

Discuss
Answer: (c).The tree depth of the generated structure Explanation:What distinguishes hierarchical topic modeling from hierarchical modeling in the context of document analysis is the tree depth of the generated structure. In hierarchical topic modeling, the tree depth varies, while hierarchical modeling may involve a different concept of hierarchy .
Q87.
What type of matrices require efficient SVD processing in the context of LSA?

a.

Document-term matrices

b.

Topic-topic matrices

c.

Document-document matrices

d.

Document-topic matrices

Discuss
Answer: (a).Document-term matrices Explanation:In the context of LSA, efficient algorithms for processing SVD are required mainly for document-term matrices.
Q88.
What is a key advantage of LDA over LSA?

a.

LDA allows for spatial representation of documents.

b.

LDA provides orthogonal spaces for topics and documents.

c.

LDA offers fast adaptability for Big Data.

d.

LDA distinguishes between topic mixture and word selection.

Discuss
Answer: (d).LDA distinguishes between topic mixture and word selection. Explanation:A key advantage of LDA over LSA is that LDA distinguishes between a topic mixture layer and a word selection layer in a text.
Q89.
Which perspective finds advantages of LSA for research in the humanities and literary sciences?

a.

Perspective focused on classification of additional texts

b.

Perspective seeking shifts in terminology

c.

Perspective analyzing inherent conceptual structures in historical corpora

d.

Perspective based on Bayesian parameter learning

Discuss
Answer: (c).Perspective analyzing inherent conceptual structures in historical corpora Explanation:The perspective that finds advantages of LSA for research in the humanities and literary sciences is one focused on analyzing inherent conceptual structures in historical corpora.
Q90.
What type of shift is impossible to identify in LDA?

a.

Topic shift

b.

Document shift

c.

Terminology shift

d.

Perspective shift

Discuss
Answer: (c).Terminology shift Explanation:LDA cannot identify terminology shifts, which are readily possible with LSA.
Page 9 of 11

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