Chapter 4: Natural Language Processing in Artificial Intelligence Class 7

Natural Language Processing in Artificial Intelligence Class 7

🌟 Introduction

Have you ever talked to Alexa, typed in Google Translate, or used autocorrect while texting?
Each time, you’ve interacted with Natural Language Processing (NLP) — a fascinating field of Artificial Intelligence that allows machines to understand, interpret, and respond to human language.

💡 Focus Keyword: Natural Language Processing in Artificial Intelligence Class 7 helps students learn how AI communicates using words, voice, and text — making technology more human and interactive.

From chatbots answering customer queries to voice assistants reading news aloud, NLP is the bridge between human communication and machine intelligence.

📘 4.1 Introduction to Natural Language Processing (NLP)

🔹 Definition:

Natural Language Processing (NLP) is a branch of Artificial Intelligence that enables computers to understand, interpret, and generate human language — both written and spoken.

It’s what allows machines to:

  • Read and understand text 📖
  • Listen and recognize speech 🎧
  • Reply meaningfully 🗣
  • Translate languages 🌍

💬 Example:
When you say, “Hey Siri, play music!” — Siri listens, understands your words, and performs the action. That’s NLP at work.

🧠 Why NLP Is Important

Human language is complex — it has grammar, tone, slang, and emotion. NLP teaches machines how to decode meaning, not just read words.

It helps AI:

  • Understand what people say.
  • Determine what they mean.
  • Decide how to respond correctly.

💡 Fact: There are over 7,000 languages spoken in the world, and NLP makes communication across them possible for machines.

📘 Figure 4.1 – Human Language vs. Machine Understanding

+---------------------------+              +------------------------------+
|     Human Language        |              |     Machine Understanding    |
+---------------------------+              +------------------------------+
| Words, sentences, grammar |   ---> AI ---> | Tokens, meaning, patterns   |
| Emotions, expressions     |              | Actions, responses           |
+---------------------------+              +------------------------------+

Alt Text: Natural Language Processing in Artificial Intelligence Class 7 – Human language vs. machine understanding diagram.

🧩 4.2 How Natural Language Processing Works

Natural Language Processing (NLP) may seem magical, but it’s powered by logical steps and linguistic rules.
Let’s understand its key stages — how AI turns human language into meaningful responses.

🧱 Step 1: Tokenization

  • Tokenization is the process of breaking text into smaller parts called tokens — usually words or phrases.
  • For example:
    “AI is fun” → [“AI”, “is”, “fun”]

💡 Purpose: Helps the AI analyze each word separately.

🧩 Step 2: Part-of-Speech (POS) Tagging

  • The AI identifies each token’s role in grammar — noun, verb, adjective, etc.
  • Example:
    “AI learns fast.”
    → AI (noun), learns (verb), fast (adverb)

💡 Purpose: Helps the computer understand sentence structure and meaning.

🧠 Step 3: Named Entity Recognition (NER)

  • AI identifies specific names or entities like people, places, or dates.
  • Example:
    “Elon Musk founded SpaceX in 2002.”
    → [Elon Musk: Person], [SpaceX: Organization], [2002: Year]

💡 Purpose: Helps AI extract key information.

🗣️ Step 4: Sentiment Analysis

  • AI analyzes emotions or tone in text — positive, negative, or neutral.
  • Example:
    “I love this movie!” → Positive
    “This is terrible.” → Negative

💡 Purpose: Helps businesses understand customer feedback and emotions.

🗨️ Step 5: Machine Understanding & Response

After analyzing words, grammar, and tone, the AI interprets meaning and responds appropriately.
This response could be:

  • A text message
  • A spoken reply
  • A translation
  • Or even an action (like turning on the lights)

💡 Example:
If you say, “Turn on the lights,” Alexa identifies it as a command and activates the connected bulb.

📘 Figure 4.2 – How NLP Works

[ Input Text ] → [ Tokenization ] → [ POS Tagging ] → [ NER ] → [ Sentiment Analysis ] → [ Response Generation ]

Alt Text: Steps in Natural Language Processing in Artificial Intelligence Class 7.

🧩 NLP Pipeline Simplified

StepTaskExample
1️⃣Tokenization“I love AI” → “I”, “love”, “AI”
2️⃣POS Tagginglove → Verb
3️⃣NER“Google” → Company
4️⃣Sentiment Analysis“Great product!” → Positive
5️⃣Response Generation“Thank you for your feedback!”

🗣️ 4.3 Working of NLP with Examples

Let’s see how Natural Language Processing in Artificial Intelligence Class 7 works through real-life applications.

💬 1. Chatbots

AI-powered chatbots understand user messages and respond instantly.
They’re used in:

  • Customer support
  • Education portals
  • Shopping websites

💡 Example:
Typing “I need help with my order” on an e-commerce site triggers the chatbot to reply, “Sure! What’s your order number?”

📘 How it Works:

  1. User types message → NLP breaks it into words
  2. AI identifies intent (“need help”)
  3. AI fetches relevant response → sends reply

🌍 2. Language Translation Apps

NLP helps translate one language to another in real time.
Apps like Google Translate analyze words, grammar, and context before showing results.

💡 Example:
“Good Morning” → “Buenos Días” (Spanish)

📘 AI Steps:

  1. Detect source language
  2. Tokenize and analyze grammar
  3. Translate while preserving meaning
  4. Generate output text or speech

🧠 3. Voice Assistants (Speech-to-Text)

Voice assistants like Siri, Alexa, or Google Assistant use NLP to understand voice commands.

💡 Example:
User: “What’s the weather today?”
AI: Converts speech → analyzes → responds with, “It’s sunny with 30°C.”

📘 How it Works:

  1. Convert speech into text (Speech Recognition)
  2. Understand the meaning (NLP)
  3. Retrieve data (AI Search)
  4. Reply using speech synthesis

✍️ 4. Grammar & Writing Tools

Applications like Grammarly or MS Word Editor use NLP to check grammar, spelling, and clarity.

💡 Example:
AI suggests replacing “is goes” with “goes” based on grammar rules.

📰 5. Text Summarization Tools

NLP-based tools summarize long paragraphs into short notes for easier reading — useful for students and researchers.

💡 Example: AI condenses 1,000-word articles into 100-word summaries.

🖼️ 6. Sentiment Monitoring on Social Media

NLP analyzes thousands of comments and tweets to understand public opinion.

💡 Example: Brands track whether customers are happy or dissatisfied using AI-powered dashboards.

📘 Figure 4.3 – Applications of NLP

+------------------+-------------------+------------------+
| Chatbots         | Language Translate| Voice Assistants |
| (Text Response)  | (Multilingual)    | (Speech Commands)|
+------------------+-------------------+------------------+
| Grammar Tools    | Summarizers       | Sentiment Analysis |
| (Writing Aid)    | (AI Reading)      | (Social Media)     |
+------------------+-------------------+------------------+

Alt Text: Applications of Natural Language Processing in Artificial Intelligence Class 7.

🌍 4.4 Importance of Natural Language Processing in AI

NLP makes technology more accessible, human-like, and inclusive.
Let’s explore how it shapes our daily life and learning.

🗣️ 1. Improving Communication

NLP bridges the gap between humans and machines.
We can talk to AI in our natural language — no coding needed.

💡 Example: Students ask ChatGPT or Alexa study questions in plain English.

🌐 2. Multilingual Access

NLP helps break language barriers, making digital platforms accessible to everyone.

💡 Example: Translating government services or school lessons into regional languages.

🦻 3. Accessibility for All

NLP supports people with disabilities through voice-to-text, screen readers, and AI subtitles.

💡 Example: Speech recognition for visually impaired users.

📈 4. Enhancing Productivity

Businesses use NLP-powered tools to automate emails, summarize data, and manage tasks efficiently.

💡 Example: AI drafting professional emails or meeting notes automatically.

🧠 5. Learning and Education

Students can use NLP tools for:

  • Grammar correction
  • Essay writing assistance
  • Concept explanations
  • Reading comprehension improvement

💡 Example: AI reading assistants helping children pronounce difficult words.

🌍 6. Global Connectivity

NLP enables cross-language communication between countries, cultures, and communities — making the world more connected.

💡 Example: Translating disaster alerts or medical information globally during emergencies.

🌱 NLP and Sustainability (SDG Connection)

NLP supports United Nations Sustainable Development Goals (SDGs) by making communication and education more inclusive.

SDGNLP ApplicationImpact
🧑‍🏫 Quality Education (SDG 4)Language learning chatbotsPromotes inclusive digital learning
♿ Reduced Inequalities (SDG 10)Speech-to-text toolsEnables access for people with disabilities
🌍 Peace, Justice (SDG 16)Multilingual information systemsPromotes transparency & awareness
💼 Decent Work (SDG 8)Automation toolsBoosts productivity & skills

💡 Example: UNESCO’s AI for Inclusive Learning Initiative uses NLP for multilingual classrooms.

🧩 Classroom Activity – “Talk to Your AI!”

🎯 Objective:

Learn how NLP responds to commands and understands language.

🔹 Steps:

  1. Open Google Teachable Machine or ChatGPT.
  2. Type or speak a question — e.g., “What is Artificial Intelligence?”
  3. Observe how AI processes and responds.
  4. Discuss with classmates how AI understood the query.

🧠 Skills Learned:

  • Linguistic understanding
  • Critical thinking
  • Digital communication

🧭 Recap – Natural Language Processing in Artificial Intelligence Class 7

  • NLP enables AI to understand and respond to human language.
  • The NLP pipeline includes tokenization, POS tagging, NER, and sentiment analysis.
  • Applications include chatbots, translators, assistants, and grammar tools.
  • NLP enhances communication, accessibility, and learning.
  • Supports SDGs by promoting inclusion and education for all.

🧩 Exercise Section

🌟 1. Multiple Choice Questions (MCQs)

Choose the correct option.

  1. Natural Language Processing (NLP) helps computers to —
    A. Understand numbers and graphs
    B. See and recognize images
    C. Understand and process human language
    D. Create robotic movements
    Answer: C
  2. When you use Google Translate, which AI domain is at work?
    A. Data Science
    B. Computer Vision
    C. Natural Language Processing
    D. Robotics
    Answer: C
  3. The process of breaking text into smaller parts (words or tokens) is called —
    A. Tokenization
    B. Segmentation
    C. Translation
    D. Filtering
    Answer: A
  4. In NLP, POS Tagging helps AI to —
    A. Count words
    B. Identify the grammatical role of each word
    C. Detect images
    D. Play sounds
    Answer: B
  5. Named Entity Recognition (NER) in NLP is used to —
    A. Identify faces
    B. Detect special words like names or places
    C. Translate text into another language
    D. Create summaries
    Answer: B
  6. Which of these is an example of Sentiment Analysis?
    A. Detecting positive or negative comments online
    B. Translating English to Hindi
    C. Generating voice from text
    D. Tagging nouns and verbs
    Answer: A
  7. The first step in an NLP pipeline is —
    A. POS Tagging
    B. Tokenization
    C. Sentiment Analysis
    D. NER
    Answer: B
  8. “Hey Siri, set an alarm for 7 AM” is an example of —
    A. Computer Vision
    B. Voice Command using NLP
    C. Data Mining
    D. Machine Translation
    Answer: B
  9. Which Sustainable Development Goal (SDG) is supported by NLP for education access?
    A. SDG 4 – Quality Education
    B. SDG 2 – Zero Hunger
    C. SDG 7 – Clean Energy
    D. SDG 13 – Climate Action
    Answer: A
  10. The AI process that detects whether a sentence is happy or sad is called —
    A. Emotion Recognition
    B. Sentiment Analysis
    C. Feature Extraction
    D. NLP Parsing
    Answer: B

✅ Answer Key (MCQs)

1-C | 2-C | 3-A | 4-B | 5-B | 6-A | 7-B | 8-B | 9-A | 10-B

✍️ 2. Fill in the Blanks

  1. Natural Language Processing enables AI to understand __________ language.
    Answer: human
  2. The process of dividing text into small parts is called __________.
    Answer: Tokenization
  3. POS Tagging identifies the __________ role of each word.
    Answer: grammatical
  4. NER stands for __________.
    Answer: Named Entity Recognition
  5. Sentiment Analysis detects the __________ behind text.
    Answer: emotion or tone
  6. NLP powers chatbots, voice assistants, and __________ tools.
    Answer: translation
  7. Google Translate is an example of an NLP-based __________ system.
    Answer: language translation
  8. AI converting speech into text is known as __________.
    Answer: Speech Recognition
  9. NLP supports accessibility through __________ tools for the visually impaired.
    Answer: voice-to-text or screen reader
  10. NLP promotes inclusion under the SDG goal __________.
    Answer: Quality Education (SDG 4)

⚖️ 3. Assertion–Reason Questions

Choose the correct option:
A — Both Assertion and Reason are true, and Reason is the correct explanation.
B — Both are true, but Reason is not the correct explanation.
C — Assertion is true, but Reason is false.
D — Assertion is false, but Reason is true.

  1. Assertion: NLP helps AI understand and respond to human language.
    Reason: NLP works only for numerical data.
    Answer: C
  2. Assertion: Tokenization divides text into smaller parts.
    Reason: This helps AI analyze each word’s meaning individually.
    Answer: A
  3. Assertion: Sentiment Analysis helps identify emotions in a message.
    Reason: It categorizes text as positive, negative, or neutral.
    Answer: A
  4. Assertion: NLP-based translators make global communication easier.
    Reason: They convert text or speech between different languages.
    Answer: A
  5. Assertion: Voice Assistants like Alexa and Siri do not use NLP.
    Reason: They only use images for communication.
    Answer: D

💬 4. Very Short Answer Type Questions (VSAQs)

(Answer in 1–2 lines)

  1. What does NLP stand for?
    Answer: Natural Language Processing.
  2. Define Natural Language Processing.
    Answer: It is an AI technology that helps computers understand and communicate using human language.
  3. What is Tokenization?
    Answer: Breaking text into smaller parts like words or phrases.
  4. Give one example of POS Tagging.
    Answer: “AI learns fast” → learns = verb.
  5. What is Named Entity Recognition used for?
    Answer: Identifying specific names, places, or dates in text.
  6. What is Sentiment Analysis?
    Answer: Detecting emotions or tone in text as positive, negative, or neutral.
  7. Name one app that uses NLP for translation.
    Answer: Google Translate.
  8. Which AI device uses NLP to answer spoken questions?
    Answer: Alexa or Siri.
  9. How does NLP help people with disabilities?
    Answer: Through speech-to-text or text-to-speech tools.
  10. Mention one SDG supported by NLP.
    Answer: SDG 4 – Quality Education.

🧩 5. Short Answer Type Questions (SAQs)

(Answer in 2–3 sentences)

  1. Explain how NLP helps AI interact with humans.
    Answer: NLP allows AI to understand spoken or written words, interpret their meaning, and generate meaningful responses — enabling natural communication.
  2. What are the main steps in the NLP process?
    Answer: Tokenization, POS Tagging, Named Entity Recognition (NER), Sentiment Analysis, and Response Generation.
  3. Define Sentiment Analysis with one example.
    Answer: It identifies the mood in text — e.g., “I love this product” is positive; “I hate waiting” is negative.
  4. How does NLP help in education?
    Answer: It powers grammar correction tools, reading aids, and AI tutors for personalized learning.
  5. Differentiate between Tokenization and POS Tagging.
    Answer: Tokenization splits text into words; POS Tagging identifies each word’s grammatical role.
  6. What role does NLP play in accessibility?
    Answer: It supports speech-to-text and voice assistant technologies for people with visual or hearing disabilities.
  7. Give two examples of NLP-based tools.
    Answer: Grammarly and Google Translate.
  8. What is Named Entity Recognition?
    Answer: It identifies names, locations, organizations, or dates in a sentence.
  9. How does NLP make technology inclusive?
    Answer: It enables communication in multiple languages and formats for diverse users.
  10. Mention one real-world use of NLP in business.
    Answer: Chatbots for customer support that reply instantly to text queries.

🧠 6. Long Answer Type Questions (LAQs)

(Answer in 5–8 sentences)

  1. Define Natural Language Processing and explain its role in AI.
    Answer: NLP is an AI branch that helps computers understand and respond to human language. It processes words, grammar, and context to generate responses. NLP enables technologies like chatbots, translators, and assistants, making human-computer interaction natural and efficient.
  2. Describe the NLP workflow with examples.
    Answer: NLP follows several steps — Tokenization (splitting text), POS Tagging (assigning grammar roles), NER (finding names/places), Sentiment Analysis (detecting emotion), and Response Generation. For example, a chatbot analyzing “I’m happy with your service” recognizes the positive sentiment and replies accordingly.
  3. Discuss five major applications of NLP in real life.
    Answer: Chatbots (automated replies), Language Translation (Google Translate), Voice Assistants (Siri/Alexa), Grammar Checkers (Grammarly), and Sentiment Analysis (social media feedback). These make communication faster, smarter, and accessible.
  4. How does NLP contribute to sustainable development?
    Answer: NLP supports SDGs like Quality Education and Reduced Inequalities by providing multilingual education, accessibility tools, and automated content translation — making knowledge inclusive for all learners.
  5. Explain how NLP changes the way humans interact with technology.
    Answer: NLP allows natural, voice-based communication without coding or typing. From asking questions to giving commands, it bridges the human-machine gap, making AI tools more personal, conversational, and accessible.

🌍 7. Source-Based / Case-Based Assessment Questions

Case Study 1: Chatbot Customer Support

Source Extract:
“An online store uses an AI chatbot to handle customer questions. When users type messages like ‘Where is my order?’, the chatbot identifies the intent, checks the database, and provides instant replies.”

Questions:

  1. Which AI technology is used here?
  2. What is the main advantage of using chatbots?
  3. Name the NLP step where the chatbot identifies the intent.
  4. Mention one limitation of chatbot-based support.

Answer Key:

  1. Natural Language Processing (NLP).
  2. Provides instant, automated responses 24/7.
  3. Intent recognition after Tokenization and POS Tagging.
  4. It may misunderstand complex or emotional queries.

Case Study 2: Voice Assistant in Daily Life

Source Extract:
“Siri and Alexa use NLP to understand spoken commands. When you say, ‘Remind me to study at 6 PM,’ they convert speech into text, analyze meaning, and set reminders automatically.”

Questions:

  1. Which step converts speech into text?
  2. How does NLP interpret the meaning of the sentence?
  3. Mention one benefit of using voice assistants.
  4. Which SDG goal is supported by such accessibility tools?

Answer Key:

  1. Speech Recognition.
  2. Through Tokenization and grammar analysis.
  3. Saves time and improves accessibility.
  4. SDG 10 – Reduced Inequalities.

Case Study 3: Sentiment Analysis on Social Media

Source Extract:
“Companies use AI tools to read thousands of online reviews. NLP detects words like ‘amazing’ or ‘terrible’ to classify feedback as positive or negative. This helps brands improve products.”

Questions:

  1. Which NLP process is used in this example?
  2. How does it help companies?
  3. Mention one ethical concern with such AI use.
  4. Give one real-life example of sentiment analysis.

Answer Key:

  1. Sentiment Analysis.
  2. Helps companies understand customer satisfaction.
  3. Privacy or misuse of public data.
  4. AI dashboards tracking user reviews on Amazon or Twitter.

Case Study 4: AI for Inclusive Education

Source Extract:
“A school uses an AI-based reading app that listens to a student’s pronunciation and corrects mistakes. The system uses NLP to analyze speech patterns and offer guidance.”

Questions:

  1. Which AI domain is applied here?
  2. How does NLP improve learning outcomes?
  3. Which SDG does this support?
  4. Mention one benefit for students with special needs.

Answer Key:

  1. Natural Language Processing (NLP).
  2. Provides instant feedback to improve language and pronunciation.
  3. SDG 4 – Quality Education.
  4. Enables personalized and accessible learning for all.

❓ FAQ – Natural Language Processing in Artificial Intelligence Class 7

Q1. What is Natural Language Processing (NLP)?
👉 It is a branch of AI that enables computers to understand and generate human language.

Q2. What are the main steps in NLP?
👉 Tokenization, POS Tagging, Named Entity Recognition, Sentiment Analysis, and Response Generation.

Q3. Where is NLP used?
👉 In chatbots, translation apps, voice assistants, and grammar tools.

Q4. How does NLP benefit students?
👉 It improves learning, language skills, and accessibility.

Q5. How does NLP support sustainability?
👉 It makes communication and education inclusive for all languages and abilities.

🌍 External DoFollow References

🏁 Conclusion

Natural Language Processing in Artificial Intelligence Class 7 opens the door to a future where humans and machines communicate seamlessly.

It helps students understand how AI listens, learns, and responds — from smart assistants to educational apps.

By mastering NLP concepts, learners not only explore technology but also gain empathy — realizing that the goal of AI is to make communication simpler, inclusive, and accessible to all. 🌍✨

💬 Remember: When machines understand language, technology becomes more human.