Natural Language Processing Quiz-1

Natural Language Processing Quiz-1

University

9 Qs

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Natural Language Processing Quiz-1

Natural Language Processing Quiz-1

Assessment

Quiz

Engineering

University

Practice Problem

Medium

Created by

Khaleel S

Used 2+ times

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9 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

NLP is all about .........?

the study of computer hardware
the interaction between computers and human language
the development of mobile applications
the analysis of financial data

2.

DRAG AND DROP QUESTION

30 sec • 1 pt

Stemming Leads to ​ (a)   ?

Revert back to Root Word
Change the meaning of a word
Create new words
Increase word complexity

3.

DRAG AND DROP QUESTION

30 sec • 1 pt

Which library in Python is commonly used for ​ (a)   tasks?​

NLTK
Pandas
NumPy
Matplotlib

4.

MATCH QUESTION

45 sec • 5 pts

Match the following

more accuracy

Lemmatization

Adjective Tag

stemming

Belongs to NLTK

Corpus

<jj>

(n-1) History

BI-Gram Model

Removing of affixes

Brown corpus

5.

CLASSIFICATION QUESTION

1 min • 4 pts

Organize these options into the right categories

Groups:

(a) LLM

,

(b) NLTK

,

(c) pos tagging

,

(d) SpeechAnalysis

language learning model

wordnet

<NN>

python library

siri

gpt

Alexa

word Classes

gemini ai

brown Corpus

6.

REORDER QUESTION

1 min • 5 pts

NLP Analysis Order of Text/Speech

pos tagging

Stemming/Lemmatization

Revert Back With Appropriate Solution

Named Entity Recognition

Text/Speech Recognization

7.

FILL IN THE BLANK QUESTION

30 sec • 1 pt

Which algorithm is commonly used for text classification in NLP_________________?

8.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Devset Referred as ?

Trainnig data

Test data

UnKonwn data

Future Purpose Used Data

9.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of named entity recognition (NER) in NLP?

To summarize the main ideas in a text.
The purpose of NER in NLP is to identify and classify named entities in text.
To translate text from one language to another.
To generate new text based on existing content.