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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 6 of 11

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Q51.
What is the purpose of ontology-based text annotation technology in information extraction?

a.

To generate document term profiles

b.

To perform unsupervised knowledge extraction

c.

To define ontology concepts and predicates

d.

To combine information extraction with conceptual analysis

Discuss
Answer: (d).To combine information extraction with conceptual analysis Explanation:The purpose of ontology-based text annotation technology in information extraction is to combine information extraction with conceptual analysis.
Q52.
What does the vector space model consider a document as?

a.

A set of words

b.

A "bag of tokens"

c.

A bag of occurrences

d.

A term-document matrix

Discuss
Answer: (b).A "bag of tokens" Explanation:The vector space model considers a document as a "bag of tokens,".
Q53.
What is the purpose of combining the TF and IDF components in the term-document matrix?

a.

To calculate document boundaries

b.

To calculate the overall length of the corpus

c.

To account for the local frequency of terms in documents

d.

To normalize the document frequency of terms

Discuss
Answer: (c).To account for the local frequency of terms in documents Explanation:The purpose of combining the TF and IDF components in the term-document matrix is to account for the local frequency of terms in documents.
Q54.
What is the expectation of fij based on a uniform distribution of ti occurrences among all documents in the corpus referred to as?

a.

Inverted document frequency (IDF)

b.

Term frequency (TF)

c.

Document boundaries

d.

Expected term frequency (ETF)

Discuss
Answer: (d).Expected term frequency (ETF) Explanation:The expectation of fij based on a uniform distribution of ti occurrences among all documents in the corpus is referred to as Expected term frequency (ETF).
Q55.
What is the difference between the use of IDF and ETF weights in literary science and related approaches in the humanities?

a.

IDF considers a terminology for each document, while ETF focuses on a universe of discourse.

b.

IDF uses a correction for observed TF, while ETF relies on raw term frequencies.

c.

IDF is suitable for document retrieval, while ETF is not relevant for retrieval.

d.

IDF focuses on the entire document collection, while ETF is document-specific.

Discuss
Answer: (a).IDF considers a terminology for each document, while ETF focuses on a universe of discourse. Explanation:The difference between the use of IDF and ETF weights in literary science and related approaches in the humanities is that IDF considers a terminology for each document, while ETF focuses on a universe of discourse.
Q56.
What is the primary benefit of latent semantic analysis (LSA) over frequency-based document profiles?

a.

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

b.

LSA eliminates all forms of homonymy and synonymy.

c.

LSA can identify word meanings automatically without human intervention.

d.

LSA is primarily focused on linguistic preprocessing.

Discuss
Answer: (c).LSA can identify word meanings automatically without human intervention. Explanation:The primary benefit of latent semantic analysis (LSA) over frequency-based document profiles is that LSA can identify word meanings automatically without human intervention.
Q57.
What is the primary purpose of individual document classification in document topic analysis?

a.

To identify thematic clusters of documents

b.

To provide a reduced dimensional approximation of the interterm raw covariance matrix

c.

To describe documents in a compact way using terms representing word meanings

d.

To identify one or several topics characteristic for an individual document

Discuss
Answer: (d).To identify one or several topics characteristic for an individual document Explanation:The primary purpose of individual document classification in document topic analysis is to identify one or several topics characteristic for an individual document.
Q58.
What technique is used to decompose the term by documents C matrix in latent semantic analysis (LSA)?

a.

Principal components analysis (PCA)

b.

Singular value decomposition (SVD)

c.

Principal component factors analysis (PCFA)

d.

Independent component analysis (ICA)

Discuss
Answer: (b).Singular value decomposition (SVD) Explanation:The technique used to decompose the term by documents C matrix in latent semantic analysis (LSA) is singular value decomposition (SVD).
Q59.
What is the purpose of the latent factors weight matrix ฮ› in LSA?

a.

To provide a conceptual characterization of each document

b.

To relate the raw words found in the document to hypothetical latent concepts

c.

To capture the term covariance across documents

d.

To assign weights to the identified latent concepts for the entire corpus

Discuss
Answer: (d).To assign weights to the identified latent concepts for the entire corpus Explanation:The purpose of the latent factors weight matrix ฮ› in LSA is to assign weights to the identified latent concepts for the entire corpus.
Q60.
In LSA, what is the primary purpose of dimensionality reduction?

a.

To increase the complexity of the analysis

b.

To highlight interterm correlations for terms

c.

To increase the number of singular values considered

d.

To have a compressed representation of essential concepts

Discuss
Answer: (d).To have a compressed representation of essential concepts Explanation:In LSA, the primary purpose of dimensionality reduction is to have a compressed representation of essential concepts.
Page 6 of 11

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