adplus-dvertising

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.

frame-decoration

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.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Textual Data Analytics and Knowledge Management. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Textual Data Analytics and Knowledge Management MCQs | Page 5 of 11

Explore more Topics under Big Data Computing

Q41.
What modeling focus is distinguished for information retrieval?

a.

Entity focus

b.

Term focus

c.

Fact-based focus

d.

Relationship focus

Discuss
Answer: (b).Term focus Explanation:For information retrieval, the modeling focus distinguished is the term focus.
Q42.
How does document classification help in the context of electronic patent filing?

a.

It helps assign patent authors to patent applications.

b.

It helps identify common taxonomies.

c.

It assists in near-automatic assignment of reviewers to patent applications.

d.

It provides keywords for patent authors.

Discuss
Answer: (c).It assists in near-automatic assignment of reviewers to patent applications. Explanation:In the context of electronic patent filing, document classification assists in near-automatic assignment of reviewers to patent applications.
Q43.
In knowledge discovery procedures, what is the term "multilabeling" typically associated with?

a.

Fact-based analysis

b.

Shallow NLP

c.

Document classification

d.

Relationship extraction

Discuss
Answer: (c).Document classification Explanation:In knowledge discovery procedures, the term "multilabeling" is typically associated with document classification.
Q44.
In practice, what are the two machine learning method approaches often combined for in an exploratory phase?

a.

Classification and information retrieval

b.

Clustering and information extraction

c.

Shallow NLP and deep NLP

d.

Clustering and class identification

Discuss
Answer: (b).Clustering and information extraction Explanation:In practice, the two machine learning method approaches are often combined for class identification and a detailed modeling phase based on training a classifier on the classes.
Q45.
What is the primary purpose of document-based information extraction?

a.

To analyze the sentiment in documents

b.

To generate document term profiles

c.

To uncover specific items of information according to a predefined information structure

d.

To perform multidocument similarity analysis

Discuss
Answer: (c).To uncover specific items of information according to a predefined information structure Explanation:The primary purpose of document-based information extraction is to uncover specific items of information according to a predefined information structure.
Q46.
How is document classification based on information extraction used in opinion or sentiment mining?

a.

To identify possible entities or relationships

b.

To generate document term profiles

c.

To detect statements in documents

d.

To examine the degree of concepts related to opinions

Discuss
Answer: (d).To examine the degree of concepts related to opinions Explanation:Document classification based on information extraction is used in opinion or sentiment mining to examine the degree of concepts related to opinions.
Q47.
What is the main distinction between knowledge extraction and information extraction?

a.

Knowledge extraction involves ontology-based text annotation technology.

b.

Information extraction is based on unsupervised methods.

c.

Knowledge extraction uses supervised methods to populate ontologies.

d.

Information extraction focuses on conceptual analysis.

Discuss
Answer: (c).Knowledge extraction uses supervised methods to populate ontologies. Explanation:The main distinction between knowledge extraction and information extraction is that knowledge extraction uses supervised methods to populate ontologies.
Q48.
What is the purpose of unsupervised methods for information extraction on the corpus level?

a.

To analyze sentiment in documents

b.

To perform multidocument similarity analysis

c.

To generate document term profiles

d.

To extract plausible ontology concepts and predicates

Discuss
Answer: (d).To extract plausible ontology concepts and predicates Explanation:The purpose of unsupervised methods for information extraction on the corpus level is to extract plausible ontology concepts and predicates, particularly related to ontology learning.
Q49.
In multidocument analysis, how is document similarity typically measured?

a.

By using cosine similarity measure

b.

By employing deep NLP techniques

c.

By calculating term frequency-inverse document frequency (TF-IDF)

d.

By conducting sentiment analysis

Discuss
Answer: (a).By using cosine similarity measure Explanation:In multidocument analysis, document similarity is typically measured using the cosine similarity measure.
Q50.
How are entities and their relationships treated in the modeling level of information extraction?

a.

As individual sentences within a document

b.

As separate XML schema elements

c.

As bags of words

d.

As mentions and their possible relationships to other entities

Discuss
Answer: (d).As mentions and their possible relationships to other entities Explanation:In the modeling level of information extraction, entities and their relationships are treated as mentions and their possible relationships to other entities.
Page 5 of 11

Suggested Topics

Are you eager to expand your knowledge beyond Big Data Computing? We've curated a selection of related categories that you might find intriguing.

Click on the categories below to discover a wealth of MCQs and enrich your understanding of Computer Science. Happy exploring!

Microprocessor

Understand the heart of your computer with our Microprocessor MCQs. Topics include...

Software Architecture and Design

Get a strong hold on the blueprint of software systems with our Software Architecture...

Python

Dive into one of the most popular languages with our Python MCQs. Understand...

Artificial Intelligence

Get started on the path to the future with our Artificial Intelligence MCQs. Covering...

Digital Communication

Master the basics of electronic communication with our Digital Communication MCQs....

Human Computer Interaction

Enhance your knowledge of Human-Computer Interaction with our specialized MCQs....

Discrete Mathematics

Refine your mathematical skills essential for computer science with our Discrete...

Computer Hardware

Explore the tangible components of computers with our Computer Hardware MCQs. Learn...

Software Engineering

Learn about the systematic approach to developing software with our Software...