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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 3 of 11

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Q21.
What methodologies can be applied to processing steps in both image analysis and unstructured information processing?

a.

Clustering and supervised learning

b.

Classification and unsupervised learning

c.

Novelty detection and object recognition

d.

Grammatical analysis and clustering

Discuss
Answer: (b).Classification and unsupervised learning Explanation:Both image analysis and unstructured information processing can use classification (based on supervised learning) and clustering (based on unsupervised learning) methodologies.
Q22.
What is the analogous concept to image segmentation in processing unstructured information from textual documents?

a.

Decomposition of texts into grammatical units

b.

Recognition of named entities

c.

Detection of typical object configurations

d.

Analysis at the word level

Discuss
Answer: (a).Decomposition of texts into grammatical units Explanation:The analogous concept to image segmentation in processing unstructured information from textual documents is the decomposition of texts into grammatical units.
Q23.
What is the primary level of analysis at the word level relevant for in unstructured information processing?

a.

Knowledge processing

b.

Clustering

c.

Grammatical forms

d.

Automatic speech recognition

Discuss
Answer: (d).Automatic speech recognition Explanation:The primary level of analysis at the word level relevant for unstructured information processing is automatic speech recognition.
Q24.
What is analogous to object recognition in unstructured information processing from textual documents?

a.

Recognition of typical scenes

b.

Detection of grammatical forms

c.

Decomposition of texts into grammatical units

d.

Recognition of named entities

Discuss
Answer: (d).Recognition of named entities Explanation:In unstructured information processing from textual documents, the analogous concept to object recognition is the recognition of named entities.
Q25.
How is scene recognition performed in textual information processing?

a.

By identifying thematic categories in the text

b.

By counting the number of tokens in the text

c.

By creating syntax trees from the text

d.

By removing stop words from the text

Discuss
Answer: (a).By identifying thematic categories in the text Explanation:Scene recognition in textual information processing is performed by identifying topics that a text is relevant to.
Q26.
What is information extraction primarily referred to as in computer linguistics?

a.

Text clustering

b.

Information retrieval

c.

Object recognition

d.

Shallow NLP

Discuss
Answer: (b).Information retrieval Explanation:Information extraction is primarily referred to as information retrieval in computer linguistics.
Q27.
What is an annotation in the context of information extraction?

a.

A type of entity recognition

b.

A span of input text with entity or relationship information

c.

A kind of syntax tree

d.

A segment of text that is irrelevant for analysis

Discuss
Answer: (b).A span of input text with entity or relationship information Explanation:In the context of information extraction, an annotation is a span of input text with entity or relationship information.
Q28.
What does "shallow" NLP primarily focus on in the context of information retrieval and extraction?

a.

Counting tokens in documents

b.

Syntax tree construction

c.

Morphological processing

d.

Part-of-speech tagging

Discuss
Answer: (a).Counting tokens in documents Explanation:"Shallow" NLP primarily focuses on counting tokens in documents in the context of information retrieval and extraction.
Q29.
For what purposes has deep NLP mainly been used in the past?

a.

Document classification and clustering

b.

Machine translation and virtual tourist kiosks

c.

Multilingual deep learning models

d.

Information extraction and knowledge-based question answering

Discuss
Answer: (b).Machine translation and virtual tourist kiosks Explanation:Deep NLP has mainly been used in the past for machine translation and related applications, such as multilingual virtual tourist kiosks.
Q30.
In the context of computer linguistics, what is emphasized by calling the IBM Watson system "DeepQA"?

a.

The use of deep NLP for information extraction

b.

The value of syntax tree construction

c.

The importance of stop word removal

d.

The reference to deep NLP in question answering

Discuss
Answer: (d).The reference to deep NLP in question answering Explanation:Calling the IBM Watson system "DeepQA" emphasizes the reference to deep NLP in question answering.
Page 3 of 11

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