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Natural Language Processing CT-2

Total questions: 20

Worksheet time: 40mins

Name
Class
Date
1.

Why is code switching considered a challenge in sentence boundary detection in natural language processing?

a)

It simplifies the identification of sentence boundaries

b)

It has no impact on sentence boundary detection

c)

It standardizes the sentence structure across languages

d)

It affects the characteristics of sentences by introducing different punctuation rules

2.

How is topic segmentation typically approached in natural language processing?

a)

By automatically dividing a stream of text into topically homogeneous blocks

b)

By focusing on sentence boundaries only

c)

By analyzing punctuation marks

d)

By categorizing words based on their meanings

3.

What is the primary difference between generative and discriminative sequence classification methods in natural language processing?

a)

Generative methods estimate the joint distribution of observations and labels, while discriminative methods focus on differences between labeling examples

b)

Generative methods require iterative optimization, while discriminative methods do not

c)

Generative methods use local features, while discriminative methods use global features

d)

Generative methods focus on feature differences, while discriminative methods estimate joint distributions

4.

Why is sentence boundary detection challenging in conversational text or multiparty meetings?

a)

The use of formal language structures simplifies boundary detection

b)

The boundaries may not be absolute due to ungrammatical sentences and disfluencies

c)

The lack of topic shifts in conversations complicates boundary identification

d)

The presence of clear punctuation marks makes boundary detection difficult

5.

What is the primary goal of sentence boundary detection in natural language processing?

a)

To segment words into phrases

b)

To determine where sentences begin and end

c)

To classify sentence types

d)

To identify punctuation marks

6.

___________ tools allow businesses to identify customer sentiment toward products, brands or services in online feedback.

a)
  1. Syntactical analysis

b)
  1. Sentiment Analysis

c)
  1. Hybrid analysis

d)
  1. Lexical analysis

7.

Sentiment analysis is also known as ____.

a)
  1. Opinion mining

b)
  1. Data mining

c)
  1. Text Analysis

d)
  1. Automatic Summerization

8.

What Is The Major Difference Between Crf (Conditional Random Field) And Hmm (Hidden Markov Model)?

a)
  1. Crf And Hmm Are Generative Model

b)
  1. Crf Is Discriminative Whereas Hmm Is Generative Model

c)
  1. Crf Is Generative Whereas Hmm Is Discriminative Model

d)
  1. Crf And Hmm Are Discriminative Model

9.

Which algorithm is used to train HMM model?

a)
  1. A centering algorithm

b)
  1. Early algorithm

c)
  1. Lexk algorithm

d)
  1. Forward-backward or Welch algorithm

10.

Following property is of - .These taggers are knowledge-driven.

a)
  1. Rule based Tagging and Stochastic Tagging

b)
  1. Rule based Tagging

c)
  1. Stochastic Tagging

d)
  1. Neither Rule based Tagging nor Stochastic Tagging

11.

"Ram's bike is new" is _____________ type of presupposition.

a)
  1. Existential

b)
  1. Factive

c)
  1. Lexical

d)
  1. Structural

12.

HMM graphs consist of a Hidden Space and Observed Space, where the hidden space consists of the _______ and the observed space is the ______.

a)
  1. Labels, Input

b)
  1. Values, Variables

c)
  1. Variables, Values

d)
  1. Input, Categories

13.

Symbolic Approach is also called _____.

a)

Rule based Approach.

b)

Convolutional Neural Networks.

c)

Corpus based.

d)

Hybrid.

14.

What is corpus?

a)

A corpus is collection of Parameters and arguments

b)

A corpus is a large and structured set of machine-readable texts that have been produced in

a natural communicative setting.

c)

It refers to a situation where the context of a phrase gives it multiple interpretation

d)

All of the above

15.

In HMMs, spaces are connected via __________ matrices {T,A} to represent the probability of ____________ from one state to another following their _____

a)
  1. Transitions, Transitioning, Connections

b)
  1. Transitions, Chaning, Sets

c)
  1. Attribute, Changing, groups

d)
  1. Label, moving, sets

16.

_____ is a process of assigning corresponding part of speech like noun, verb, adverb, adjective, verb to each word in a sentence.

a)
  1. Parsing

b)
  1. Name entity tagging

c)
  1. Disambiguation

d)
  1. Part of speech tagging

17.

Which Of the Following Best Describes Grammar Induction?

a)
  1. Supervised Learning Problem

b)
  1. Conditional Random Field Problem

c)
  1. Maximum-A-Posteriori (Map) Estimation Problem

d)
  1. Unsupervised Learning Problem

18.

____________________ is the step in which an input sentence is converted into a hierarchical structure that corresponds to the units of meaning in the sentence.

a)

Semantic Processing

b)

Syntactic Processing

c)

Graph Processing

d)

All of the mentioned

19.

How does the TextTiling method contribute to topic segmentation in natural language processing?

a)

It uses a lexical cohesion metric in a word vector space to indicate topic similarity

b)

It focuses on sentence boundaries to determine topic shifts

c)

It utilizes punctuation marks to segment topics

d)

It relies on syntactic analysis to identify topic boundaries

20.

What is the main drawback of enumerative morphological models in natural language processing?

a)

Limited coverage and lack of generative potential

b)

High accuracy and efficiency

c)

Ease of development and verification

d)

Robustness in handling irregularities