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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 7 of 11

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Q61.
What characteristic do both matrices U and VT have in LSA?

a.

They have square matrices

b.

They contain raw word frequencies

c.

They are not orthogonal

d.

They have orthogonal base vectors in the latent conceptual dimensions

Discuss
Answer: (d).They have orthogonal base vectors in the latent conceptual dimensions Explanation:Both matrices U and VT in LSA have orthogonal base vectors in the latent conceptual dimensions.
Q62.
What is the outcome of LSA analysis typically based on?

a.

Examination of singular values

b.

Clusters of documents only

c.

Clusters of terms only

d.

Inspection of the factorial structure output from the SVD analysis

Discuss
Answer: (d).Inspection of the factorial structure output from the SVD analysis Explanation:The outcome of LSA analysis is typically based on inspection of the factorial structure output from the SVD analysis.
Q63.
In the context of LSA, what is the benefit of using a subset of singular values for dimensionality reduction?

a.

It reduces the independence of the latent conceptual factors.

b.

It makes the analysis less suitable for Big Data requirements.

c.

It helps highlight interterm correlations for terms in a "semantic space."

d.

It increases the number of latent concepts identified.

Discuss
Answer: (c).It helps highlight interterm correlations for terms in a "semantic space." Explanation:In the context of LSA, the benefit of using a subset of singular values for dimensionality reduction is that it helps highlight interterm correlations for terms in a "semantic space,".
Q64.
What does the "folding in" method allow LSA/LSI to do?

a.

It helps identify linguistic preprocessing steps for LSA/LSI.

b.

It enables the smooth updating of generative model parameters.

c.

It facilitates dimensionality reduction.

d.

It optimizes the selection of singular values in LSA/LSI.

Discuss
Answer: (b).It enables the smooth updating of generative model parameters. Explanation:The "folding in" method allows LSA/LSI to enable the smooth updating of generative model parameters.
Q65.
What is a key limitation of techniques like latent semantic analysis (LSA) as compared to generative approaches like LDA?

a.

LSA assumes a topic-based document generation model.

b.

LSA predicts relative frequencies of specific terms within a document.

c.

LSA requires Bayesian inference.

d.

LSA is not applicable to weakly structured textual data.

Discuss
Answer: (a).LSA assumes a topic-based document generation model. Explanation:A key limitation of techniques like latent semantic analysis (LSA) as compared to generative approaches like LDA is that LSA does not start from a generative model of documents.
Q66.
What is the primary advantage of latent Dirichlet analysis (LDA) over techniques like LSA?

a.

LDA does not require Bayesian inference.

b.

LDA allows an infinite number of topics.

c.

LDA provides a compact way to describe documents using raw words.

d.

LDA integrates a topic-based document generation model with Bayesian inference.

Discuss
Answer: (d).LDA integrates a topic-based document generation model with Bayesian inference. Explanation:The primary advantage of latent Dirichlet analysis (LDA) over techniques like LSA is that LDA integrates a topic-based document generation model with Bayesian inference.
Q67.
In the context of LDA, what are anchor terms?

a.

Terms that appear in multiple topics

b.

Terms specific to a given topic

c.

Terms that appear only in one topic

d.

Terms related to the corpus-level topics

Discuss
Answer: (b).Terms specific to a given topic Explanation:In the context of LDA, anchor terms are terms specific to a given topic.
Q68.
What role does the Dirichlet distribution play in LDA?

a.

It is a prior distribution for generating the document-topic multinomial.

b.

It is a posterior distribution for estimating the topic-term multinomials.

c.

It specifies the number of topics for LDA.

d.

It defines the anchor terms in LDA.

Discuss
Answer: (a).It is a prior distribution for generating the document-topic multinomial. Explanation:The Dirichlet distribution in LDA is a prior distribution for generating the document-topic multinomial.
Q69.
How is the overall number of topics for a given LDA analysis determined?

a.

It is based on a corpus level prior distribution.

b.

It must be specified in advance.

c.

It is a dynamic value that changes during analysis.

d.

It depends on the hyperparameter vector.

Discuss
Answer: (b).It must be specified in advance. Explanation:The overall number of topics for a given LDA analysis must be specified in advance.
Q70.
What does the generative model in LDA describe?

a.

The sequence of tokens in a document

b.

The probability distribution of terms in a document

c.

The process of predicting relative frequencies of terms in a document

d.

The generation of a document as a combination of topic and term sampling

Discuss
Answer: (d).The generation of a document as a combination of topic and term sampling Explanation:The generative model in LDA describes the generation of a document as a combination of topic and term sampling.
Page 7 of 11

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