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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 4 of 11

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Q31.
What is a key component in Deep NLP systems for knowledge extraction or analysis?

a.

Syntactic context

b.

Feature-rich lexicon

c.

Logical structure

d.

Rule engine

Discuss
Answer: (b).Feature-rich lexicon Explanation:A key component in Deep NLP systems for knowledge extraction or analysis is a feature-rich lexicon.
Q32.
How does the slot grammar used in the IBM Watson system enhance parsing for knowledge extraction?

a.

By focusing on morphological analysis

b.

By identifying semantic markers

c.

By using a shallow lexicon

d.

By reducing syntactic contexts

Discuss
Answer: (b).By identifying semantic markers Explanation:The slot grammar used in the IBM Watson system enhances parsing for knowledge extraction by identifying semantic markers.
Q33.
What is the primary transformation that deep parsing leads to in the context of knowledge extraction?

a.

Transformation into a syntactic structure

b.

Transformation into a logical structure with predicates and arguments

c.

Transformation into a Prolog engine

d.

Transformation into a feature-rich lexicon

Discuss
Answer: (b).Transformation into a logical structure with predicates and arguments Explanation:Deep parsing leads to a transformation into a logical structure with predicates and arguments in the context of knowledge extraction.
Q34.
What kind of engine is used in the DeepQA system for further processing the knowledge implied in the analyzed sentences?

a.

Python engine

b.

JavaScript engine

c.

Prolog engine

d.

Ruby engine

Discuss
Answer: (c).Prolog engine Explanation:In the DeepQA system, a Prolog engine is used for further processing the knowledge implied in the analyzed sentences.
Q35.
What does the term "machine readers" refer to in the context of Deep NLP with a focus on knowledge extraction?

a.

Components that process large corpora of texts

b.

Automated devices for linguistic analysis

c.

Human interpreters of textual data

d.

Advanced syntax analyzers

Discuss
Answer: (a).Components that process large corpora of texts Explanation:In the context of Deep NLP with a focus on knowledge extraction, the term "machine readers" refers to components that process large corpora of texts.
Q36.
What is one of the key challenges for knowledge extraction through machine reading?

a.

Domain independence

b.

Syntactic complexity

c.

Syntax tree construction

d.

Prolog programming

Discuss
Answer: (a).Domain independence Explanation:One of the key challenges for knowledge extraction through machine reading is domain independence.
Q37.
Why is entity or predicate disambiguation needed in machine reading?

a.

To improve syntactic analysis

b.

To enhance rule-based knowledge extraction

c.

To map knowledge from texts to common or cultural knowledge

d.

To remove stop words from text corpora

Discuss
Answer: (c).To map knowledge from texts to common or cultural knowledge Explanation:Entity or predicate disambiguation is needed in machine reading to map knowledge extracted from texts to common or cultural knowledge.
Q38.
What is relation extraction used for in machine reading?

a.

Disambiguating entities

b.

Creating syntactic structures

c.

Identifying extracted relations in documents

d.

Parsing large document corpora

Discuss
Answer: (c).Identifying extracted relations in documents Explanation:Relation extraction in machine reading is used for identifying extracted relations in documents.
Q39.
What is one of the criteria for distinguishing between document scope and corpus scope in knowledge discovery analyses on textual data?

a.

The use of deep NLP

b.

The reference to descriptors or parameters

c.

The analysis output

d.

The modeling focus

Discuss
Answer: (c).The analysis output Explanation:One of the criteria for distinguishing between document scope and corpus scope in knowledge discovery analyses on textual data is the analysis output.
Q40.
In the context of the modeling level of analysis, what is the primary distinction between information extraction and knowledge extraction?

a.

The use of deep NLP

b.

The scope of analysis

c.

The focus on document classification

d.

The use of shallow NLP

Discuss
Answer: (a).The use of deep NLP Explanation:In the context of the modeling level of analysis, the primary distinction between information extraction and knowledge extraction is the use of deep NLP.
Page 4 of 11

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