Grade VIII • Artificial Intelligence

Natural Language Processing Worksheet

Introduction to NLP • Working of NLP • Components of NLP • NLP Tasks and Techniques • Applications of NLP

Maximum Marks: 20

Student Information

Name:
Class & Section:
Roll No.:
Date:

General Instructions

  1. Read each question carefully before answering.
  2. Attempt all questions.
  3. Use appropriate technical terms related to Natural Language Processing.
  4. Give suitable examples wherever required.
  5. Write long answers in complete and logically organised sentences.
  6. The worksheet carries a total of 20 marks.

Topics Covered

  • Introduction to NLP
  • Working of NLP
  • Components of NLP
  • NLP Tasks and Techniques
  • Applications of NLP

Section A – Objective Type Questions 5 Marks

1. Multiple Choice Questions

Questions 1–2: 1 × 2 = 2 Marks

1. What does NLP primarily enable computers to work with?

  • ☐ a) Natural human language
  • ☐ b) Computer hardware only
  • ☐ c) Electrical circuits only
  • ☐ d) Computer graphics only

2. Which of the following is an example of an NLP application?

  • ☐ a) Machine translation
  • ☐ b) Image compression
  • ☐ c) 3D printing
  • ☐ d) Battery charging

2. Fill in the Blanks

Questions 3–4: 1 × 2 = 2 Marks

3. NLP stands for Language Processing.

4. Breaking a sentence into smaller units such as words or tokens is called .

3. True / False

Questions 5a–5b: 0.5 × 2 = 1 Mark

5a. NLP can be used to analyse and process human language.

Answer: ☐ True     ☐ False

5b. NLP can only process English and cannot be used with other human languages.

Answer: ☐ True     ☐ False

Section A Total: 5 Marks

Section B – Short Answer Type Questions 6 Marks

Questions 6–8: 2 × 3 = 6 Marks

6. What is Natural Language Processing? Give two examples of systems that use NLP.

7. What is tokenization? Why is it useful in NLP?

8. Name any two NLP tasks and briefly explain each one.

Section B Total: 6 Marks

Section C – Long Answer Type Questions 9 Marks

Questions 9–11: 3 × 3 = 9 Marks

9. Explain how an NLP system processes human language. Describe the major stages involved from receiving the language input to producing a useful output.

10. Explain any three NLP tasks or techniques such as tokenization, sentiment analysis, speech recognition, text classification, machine translation or named entity recognition.

11. Explain any three real-world applications of NLP. Describe how NLP makes each application useful to people.

Section C Total: 9 Marks

NLP Concept Reminders

Natural Language Processing

Natural Language Processing (NLP) is a branch of Artificial Intelligence that enables computers to process, analyse and generate human language.

Common NLP Processing Steps

  1. Receive language input.
  2. Clean and prepare the input.
  3. Break text into meaningful units.
  4. Analyse the structure and meaning of the language.
  5. Apply an appropriate NLP model or technique.
  6. Generate a useful result or response.

Components of NLP

Component / Area Purpose
Natural Language Understanding (NLU) Helps a system interpret the meaning and intent expressed in human language.
Natural Language Generation (NLG) Helps a system generate human-readable language as an output.
Text Processing Includes operations such as tokenization and preparation of text for analysis.
Speech Processing Deals with spoken language, including converting speech to text or generating speech.

Common NLP Tasks and Techniques

Task / Technique What It Does Example
Tokenization Splits text into smaller units such as words or tokens. "I like Python" → "I", "like", "Python"
Sentiment Analysis Identifies the emotional or opinion-related tone of text. Classifying a review as positive or negative.
Text Classification Assigns text to predefined categories. Classifying an email as spam or not spam.
Machine Translation Converts text from one language into another. Translating Hindi text into English.
Speech Recognition Converts spoken language into machine-readable text. Voice typing.
Named Entity Recognition Identifies entities such as people, places and organisations in text. Identifying "Delhi" as a location.

Applications of NLP

Application Use of NLP
Chatbots Understand user questions and generate useful responses.
Voice Assistants Process spoken commands and respond to users.
Machine Translation Translate content between different human languages.
Spam Detection Analyse messages and identify potentially unwanted emails or messages.
Sentiment Analysis Analyse opinions and emotional tone in reviews or social media text.
Search Engines Help interpret search queries and retrieve relevant information.
Text Summarisation Produce shorter versions of longer pieces of text while preserving important information.

Application-Based Thinking

Imagine that you are designing a chatbot for a school. Students may ask questions in different ways, such as:

"When is the science exhibition?"
"What is the date of the science exhibition?"
"Tell me the science exhibition date."

Although the wording is different, an NLP system should try to understand the common intent behind these questions.

Assessment Blueprint

Section Question Type Question Numbers Marks per Question Total Marks
A MCQ 1–2 1 × 2 2
A Fill in the Blanks 3–4 1 × 2 2
A True / False 5a–5b 0.5 × 2 1
B Short Answer 6–8 2 × 3 6
C Long Answer 9–11 3 × 3 9
Grand Total 20

Self-Check

Before submitting your worksheet:

  • Have you attempted all 11 questions?
  • Can you explain what NLP means?
  • Can you describe the basic working of an NLP system?
  • Can you distinguish between NLP tasks and applications?
  • Can you explain at least three NLP techniques?
  • Can you identify real-world applications of NLP?
  • Have you used appropriate examples in your answers?